alva

Alva

What is Alva

Alva is an agentic finance platform. It provides unified access to 250+ financial data sources spanning crypto, equities, ETFs, macroeconomic indicators, on-chain analytics, and social sentiment -- including spot and futures OHLCV, funding rates, company fundamentals, price targets, insider and senator trades, earnings estimates, CPI, GDP, Treasury rates, exchange flows, DeFi metrics, news feeds, social media and more!

What Alva Skills Enables

The Alva skill connects any AI agent or IDE to the full Alva platform. With it you can:

• Access financial data -- query any of Alva's 250+ data SDKs programmatically, or bring your own data via HTTP API or direct upload.
• Run cloud-side analytics -- write JavaScript that executes on Alva Cloud in a secure runtime. No local compute, no dependencies, no infrastructure to manage.
• Build agentic playbooks -- create data pipelines, trading strategies, and scheduled automations that run continuously on Alva Cloud.
• Deploy trading strategies -- backtest with the Altra trading engine and run continuous live paper trading.
• Release and share -- turn your work into a hosted playbook web app at https://alva.ai/u/<username>/playbooks/<playbook_name>, and share it with the world.
• Remix existing playbooks -- take any published playbook as a template, read its feed scripts and HTML source, customize parameters/logic/UI, and deploy as your own new playbook.

In short: turn your ideas into a forever-running finance agent that gets things done for you.

Pre-flight

Run these checks on first use each session before doing anything else.

1. Version Check

bash "<this skill's directory>/scripts/version_check.sh"

• No output → up to date, proceed.
• Output present → display to user, apply the update, then proceed.

2. Alva CLI Setup

The alva CLI (@alva-ai/toolkit) is the required way to interact with the Alva platform. It manages authentication, provides self-documenting help for every command, and eliminates the need for manual curl/header management.

Check whether the CLI is already installed by running alva --help.

• If not installed, install it:

  npm install -g @alva-ai/toolkit
  

• If already installed, upgrade to the latest version to ensure access to the newest commands and fixes:

  npm install -g @alva-ai/toolkit@latest
  

Then check authentication (see step 2a below).

Discover commands:

alva --help              # list all commands
alva <command> --help    # detailed usage, flags, and examples for any command

Use alva <command> --help to discover usage — the help text includes all flags, parameter types, and practical examples.

Third-party vendor secrets belong in Alva Secret Manager (require("secret-manager")), not in the CLI config.

2a. Authentication Check

Run alva whoami. If it fails (no API key), run alva auth login to open browser-based login, then re-run alva whoami to confirm.

3. User Profile

alva whoami

{"id":1, "subscription_tier":"free", "telegram_username":"alice_tg", "username":"alice"}

Session variables:

• username — for public URLs and ALFS paths.
• subscription_tier — "pro" or "free" (default). Determines release flow (Step 7): pro can keep playbooks private.
• telegram_username — if set, recommend push-enabled feeds; if null, guide user to connect Telegram first.

4. Arrays JWT Check

Data skills require ARRAYS_JWT. The _meta.arrays_jwt field in the alva whoami output above shows its status — if it needs attention (missing, renewal_needed: true, or absent), use alva arrays token to manage it (status to inspect, ensure to provision or refresh).

5. Load Memory

If you have not read the user's memory in this conversation, read it now.

alva fs read --path '~/memory/MEMORY.md'

If the file exists, read each file listed in the index (at minimum user.md). If '~/memory/' does not exist or is empty, skip — it will be seeded on next sign-in.

Use the loaded memory to tailor your responses to the user's profile, preferences, and investment style. See the Memory section below for reading and writing rules.

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Communication

Vocabulary — use the terms in language.md exactly. Key terms: automation (not "cronjob"), feed, playbook, script. Full definitions and "avoid" labels are in that file.

No ALFS paths, API payloads, automation IDs, raw function names, internal jargon, or implementation details in user-facing responses. Say what it DOES, not how it works. These details are operating instructions for you, not content for the user.

Lead with the result, not the process. The first thing the user reads should be what they got ("Your dashboard is live at …"), not what you did ("I deployed 3 feeds and wrote the HTML"). During multi-step builds, give a short status update at each milestone so the user knows work is progressing.

Data provenance in direct answers. When a direct answer cites specific financial figures, each number must either come from a fresh SDK/BYOD fetch (attributed inline to its source) or be explicitly qualified as an estimate that the user should verify with current sources.

Request Routing

Request Type	Core Objectives
Dashboard / Playbook	Identify the needed data sources, validate the data flow, and produce a usable dashboard or playbook when the user wants a shareable artifact
Backtest / Strategy	Use Altra, run the backtest correctly, and always produce a visual playbook (equity curve, trade log, metrics) alongside the text summary. Optionally deploy as live paper trading.
Data Query	Fetch the requested data accurately and return it directly unless the user asks for a richer artifact
Remix	Reuse the source artifact, apply the requested changes, and return an updated result that matches the requested customization

Choose Template (mandatory when /use-template:<name> is present)

If the user's message contains a /use-template:<name> directive (e.g. /use-template:thesis, /use-template:screener), this step is mandatory and must run before Guided Planning and before any build work.

1. Resolve <name> to skills/alva/templates/<name>/template.md (relative to this skill).
2. Read that template file via the filesystem — do not proceed from memory of a prior session. If the file does not exist, list the directories under templates/ and ask the user which to use.
3. Treat the template as the authoritative blueprint for layout, sections, widgets, data contracts, and cadence. Deviate only where the user explicitly overrides it.
4. In the plan you present during Guided Planning, state which template is being used and call out any intentional deviations.

Content arrangement. A template's default sections are a floor, not a ceiling. Lead with whatever carries the user's core question, proactively add sections the request demands, and cut or fold near-empty sections into neighbors rather than padding them.

No /use-template: directive → skip this step and proceed to Guided Planning normally.

Guided Planning

For all routes except Data Query, present a plan and get user approval before building. Even seemingly clear requests ("build a BTC dashboard") have real choices — which data, timeframe, widgets — that are cheaper to resolve upfront than to rebuild.

1. Understand intent — Ask clarifying questions one at a time, prefer multiple-choice. Focus on what's missing: asset, scope, output type, or purpose. Skip this step if the request already specifies all of these.
2. Propose approaches — Offer 2-3 concrete options with trade-offs. Lead with your recommendation.
3. Confirm the plan — List the specific feeds and widgets (5-8 lines, no implementation details). Build only after approval.

If the user says "just do it" at any point, skip planning for the rest of the session.

Completion Gate

For Dashboard/Playbook and Backtest/Strategy requests, the default goal is to leave the user with a result they can actually use. In many cases that means a released playbook and a published_url, but do not force that path if the user only asked for code, analysis, debugging help, or an intermediate artifact.

Before finishing, verify that the delivered result matches the user's actual goal. When a shareable playbook was part of the task, verify:

• A playbook was released and a published_url was returned

Must Do After Completion Gate:

• Summarize the whole process and what is delivered to the user.

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Content Legitimacy Rules

These rules are non-negotiable. Violations produce misleading content that displays fabricated data as if it were real. They apply to every response that surfaces financial values to the user — playbook builds, dashboards, query-mode answers, remixes, edits, and follow-ups — regardless of whether the session ends with a released playbook.

Core principle: the agent's role is to build the pipeline, not to be the data source. Any quantitative value the user sees must trace back to an Alva SDK module, a published Alva feed, or a BYOD HTTP source that is either user-provided or explicitly validated and wired into the feed pipeline. Agent knowledge, LLM output, WebSearch snippets, random/synthetic generators, and user-pasted snapshots are not legitimate data sources — regardless of whether they appear as HTML literals, feed-script literals, backfilled history, or agent-authored opinion columns. When the SDK has no coverage for the requested domain, report the gap and stop; do not manufacture plausible-looking data.

Data Sourcing

1. All quantitative data displayed in charts, tables, or metric cards MUST originate from Alva feeds (SDK modules or BYOD via require("net/http")). Never hardcode data as inline JavaScript literals in playbook HTML.

2. Playbook HTML MUST fetch data at runtime from feed output paths:

   const resp = await fetch(
     "$ALVA_ENDPOINT/api/v1/fs/read?path=/alva/home/<user>/feeds/<name>/v1/data/<group>/<output>/@last/<n>",
   );
   const data = await resp.json();
   renderChart(data);
   

   Static content (labels, colors, layout config) is fine. Quantitative data is not — it must flow through the feed pipeline.

3. Verification claims and quoted tool outputs must reflect actual tool calls. Do not describe a screenshot you did not take ("the dashboard looks good"). When citing a tool-returned value such as published_url, feed_id, or an ALFS path, copy it verbatim from the response. The user-facing share link is the canonical https://alva.ai/u/<username>/playbooks/<playbook_name> URL; published_url is the deployed HTML URL used for verification steps such as screenshots. Do not present one as if it were the other. If you need a value, re-read the tool response first.

Prohibited Data Sources for Charts, Tables, and Query Answers

1. WebSearch / WebFetch results must NOT be embedded as data. Web search is only legitimate for: reading documentation, finding API endpoints for BYOD, understanding user requirements. It may help you discover a legitimate BYOD source, but discovered values themselves must never be quoted as the answer or injected as static data literals in feed scripts or playbook HTML. This rule applies even when Alva API auth fails — in that case, report the failure and stop; do NOT substitute a web-sourced value.

2. LLM / ADK output must NOT be presented as factual sourced data. ADK is for reasoning, classification, summarization, and synthesis of real data — not for generating numbers, statistics, events, or reports that claim to be from real sources. If ADK produces quantitative output, it must be clearly labeled as "AI-generated analysis".

3. Agent training knowledge must NOT fill data gaps. If an SDK does not have the requested data type, report the gap as a blocker. Do not invent data from your own knowledge to fill the hole.

Feed Scope Isolation

Do not reference other feeds or other playbooks' feeds. When building a playbook, only read from feeds created for this playbook in the current session.

Reference an existing feed only when the user explicitly asks for it (e.g. "reuse my btc-ema feed", "pull data from @alice/macro-dashboard"). Otherwise, build the playbook's own feeds from scratch so its data lineage is self-contained and the playbook remains portable.

Qualitative analysis (ratings, theses, outlook text) is not data and must not appear as feed output columns or "data" fields in HTML tables. If the user asks for a rating, either compute it from SDK fundamentals with the formula shown, or place it in a clearly labelled "AI analysis" section separated from data-driven metrics.

SDK Coverage Gaps

1. When an SDK partition lacks the requested data type, reduce scope:

   • Omit the missing data section from the playbook and note the gap (e.g. "ECB, BOJ, BOE rates — data source not yet available").
   • If the user has provided a specific data source URL, use BYOD (require("net/http")) to fetch from it.
   • Do NOT hardcode point-in-time values in HTML — they become stale immediately and violate content legitimacy rules.
   • Do NOT fabricate events or fill gaps from agent knowledge.

2. When >20% of requested symbols fail SDK lookup, report a data-quality blocker. Do not silently substitute with estimated or fabricated values marked live: false.

Release Gate: --feeds Is a Declaration, Not a Shortcut

alva release playbook --feeds '[]' is only valid when the released HTML renders zero quantitative values at runtime (landing pages, UI-only demos). If the HTML shows any numbers, charts, tables, or metric cards, the release MUST reference deployed feeds in --feeds and the HTML MUST fetch() them at runtime. alva run is a test run of a feed, not a substitute for deploying one — if you used alva run to source data, deploy that same logic as a feed and reference it.

Thematic Ticker Curation

When building sector or thematic dashboards with curated ticker lists:

1. Do NOT rely on agent knowledge for ticker-to-sector mapping.
2. After assembling the list, cross-check each ticker's sector using an SDK call (e.g. getStockCompanyDetail) to verify it belongs to the intended segment.
3. Remove mismatches before building the feed. A single wrong ticker (e.g. a cybersecurity company in a battery segment) can distort the entire analysis.

Description and Provenance Accuracy

1. Playbook descriptions and methodology sections must only list data sources that were actually called successfully. Do not claim "Brave Search", "ClinicalTrials.gov", or any other source unless the feed script actually fetches from it at runtime.

2. Update frequency claims must match actual deployment. If cronjob deployment failed, do not claim "updated every N hours" in the playbook description. Either fix the cronjob or remove the claim.

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Narrative Voice Rules

All user-facing prose the agent writes — playbook description and display_name, hand-written HTML copy (hero text, intro cards, methodology modal body, chart footnotes, rationale paragraphs), and any ADK system prompts that produce narrative (TLDRs, digests, why-it-matters, delta bodies, push-line headlines) — must follow narrative-voice.md. Read that file before writing or generating any user-facing prose.

The rules ban a specific list of AI-tell tokens and shapes (significance inflation, negative parallelism, rule-of-three, generic closers, em-dash overuse) and ship as a copy-paste system-prompt block with embedded few-shots for ADK calls. Pure structured fields (numbers, tickers, dates, enum labels, button labels) are exempt.

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Capabilities & Common Workflows

1. ALFS (Alva FileSystem)

The foundation of the platform. ALFS is a cloud filesystem with per-user isolation. Every user has a private home directory; all paths are private by default and only accessible by the owning user. Public read access can be explicitly granted on specific paths via grant. Scripts, data feeds, playbook assets, and shared libraries all live on ALFS.

Key operations: read, write, mkdir, stat, readdir, remove, rename, copy, symlink, chmod, grant, revoke.

In shell and documentation, wrap ALFS path arguments in single quotes (e.g. '~/feeds/...', '/alva/home/...') so they are not confused with paths on your local machine. See Filesystem.

2. JS Runtime

Run JavaScript on Alva Cloud in a sandboxed V8 isolate. Code executed via alva run runs entirely on Alva's servers -- it cannot access the host machine's filesystem, environment variables, or processes. The runtime has access to ALFS, data skills via HTTP, runtime libraries, LLM access, and the Feed SDK.

3. Data Skills

Financial data APIs across 16+ domains, served by the Arrays backend ($ARRAYS_ENDPOINT, defaults to https://data-tools.prd.space.id). To find the right API for a task, use the alva skills CLI (public, no auth):

1. Discover available data skills: alva skills list — returns all data skills with their names and descriptions. Use this to find the skill that matches your data need.
2. Fetch the skill summary: alva skills summary --name <skill> — returns the endpoints table for that domain.
3. Fetch endpoint detail: alva skills endpoint --name <skill> --file <file> — use the File value from the summary endpoints table (e.g. rates, macro-index-historical, earnings-calendar) to get full parameters, response fields, and examples. The File column is the endpoint slug; the Path column is the REST URL path and is NOT accepted here (e.g. for treasury rates, use --file rates, not --file macro/treasury-rates).
4. Call Arrays data endpoints with Authorization: Bearer <ARRAYS_JWT>. In runtime code, load the token via secret.loadPlaintext('ARRAYS_JWT'). The token is verified during preflight (see Arrays JWT Check); if a call returns 401, re-run alva arrays token ensure. Do not use X-API-Key header.

Data skill doc lookup is mandatory. Always fetch the endpoint detail before writing code that calls it. Do not guess paths, parameter names, or response shapes from memory. The doc lookup ensures you use the correct endpoint and handle the actual response format.

Enforcement: Before any Arrays data HTTP call or alva run that hits one, you MUST have completed alva skills endpoint --name <skill> --file <file> for that endpoint in this session (passing the File column value, not the Path). If the call fails with an unexpected shape, re-fetch the endpoint detail rather than guessing.

Runtime Libraries

Built-in modules that run inside the jagent V8 runtime via require(). These are not data APIs — they are pure computation and utility libraries available in every script execution.

Module group	Description
feed_widgets	Per-handle/channel rolling subscriptions — news, Twitter/X, YouTube, Reddit, podcasts (e.g. getTwitterFeed). Twitter also has historical backfill over a time window (getTwitterBackfill, Pro-gated). For topic/keyword search, use Content Search.
unified_search	Web search and URL scraping tools (X/Grok, Google, Brave, serper, decodo)
technical_indicator_calculation_helpers	50+ pure calculation helpers (RSI, MACD, Bollinger, etc.)

To discover available modules and their documentation:

• alva sdk partitions — list all runtime module groups
• alva sdk partition-summary --partition <name> — one-line summaries per group
• alva sdk doc --name <module> — full doc for a specific runtime module

Pick a module group → partition-summary to see modules → sdk doc for full documentation.

For unstructured content — news articles, social discussions, videos, podcasts — see Content Search below.

You can also bring your own data by uploading files to ALFS or fetching from external HTTP APIs within the runtime.

Content Search

Search across Twitter/X, news, Reddit, YouTube, podcasts, and general web. Use whenever the playbook needs content beyond structured data SDKs — from targeted queries ("what are people saying about NVDA earnings") to broad discovery ("trending crypto discussions this week"), including social discussions, market narratives, news coverage, sentiment, analyst commentary, and community reactions.

Content search modules live in the unified_search runtime-library partition. Discover them via the same partition API as the other runtime libraries (GET /api/v1/sdk/partitions/unified_search/summary → module listing; GET /api/v1/sdk/doc?name=... → full per-module documentation). See search.md for per-source SDK usage, enrichment patterns, and gotchas.

4. Altra (Alva Trading Engine)

A feed-based event-driven backtesting engine for quantitative trading strategies. A trading strategy IS a feed: all output data (targets, portfolio, orders, equity, metrics) lives under a single feed's ALFS path. Altra supports historical backtesting and continuous live paper trading, with custom indicators, portfolio simulation, and performance analytics.

5. Deploy on Alva Cloud

Once your data analytics scripts and feeds are ready, deploy them as scheduled cronjobs on Alva Cloud. They run continuously on your chosen schedule (e.g. every hour, every day). All data is private by default; grant public access to specific paths so anyone -- or any playbook page -- can read the data.

User scope enforcement: All write, deploy, and release operations MUST target only the requesting user's namespace. Before any fs/write, draft/playbook, or release/playbook call, verify the target path and username match the authenticated user (from alva whoami). If you have access to multiple API keys (e.g. from prior sessions), identify the requesting user and scope all operations to that user only. Do NOT write to or release playbooks under other users' namespaces unless the request explicitly asks for cross-user operations (e.g. remix with lineage).

Signal feeds require Altra: Any feed that produces signal/targets or signal/alerts output MUST use FeedAltra. Manual signal construction (building target records without Altra) bypasses bar alignment, portfolio simulation, and look-ahead bias prevention. Use FeedAltra even for simple signal logic — it ensures correct timestamps and prevents forward-looking bugs. This applies to ALL feed types that produce signal output — including monitoring feeds, alert feeds, and notification feeds, not just backtest strategies. If the feed pushes signals to Telegram or triggers alerts, it MUST use FeedAltra.

Push notifications for followers: Feeds can produce actionable, subscription-worthy signals that get pushed to playbook followers via Telegram. To make a feed push-capable:

1. Add a signal/targets output to the feed script (see feed-sdk.md Pattern D) and write signal records using the Altra target format ({date, instruction, meta}), where meta.reason is the human-readable message followers will see.
2. Set --push-notify in the alva deploy create command, or update the existing cronjob with alva deploy update --id ID --push-notify.

The platform reads /data/signal/targets after each successful execution and pushes the signal content to all eligible followers.

AlvaAsk + owner notifications: Feeds can use @alva/alvaask to call Alva's agent and push the result to the feed owner — useful for scheduled reports, heartbeat monitoring, and proactive alerts. Write to notify/message (see feed-sdk.md Pattern E):

const result = ask("Brief crypto market update with key levels.");
await ctx.self.ts("notify", "message").append([{
  date: Date.now(),
  title: "Daily Briefing",
  text: result.text,
}]);

The platform reads /data/notify/message/@last/1 and pushes title + text to the owner on all connected channels (Telegram, Discord, Web). No playbook or followers required.

See Step 9 below for the full post-release subscription flow.

6. Build the Playbook Web App

After your data pipelines are deployed and producing data, build the playbook's web interface. Create HTML5 pages with Alva Design System that read from Alva's data gateway and visualize the results. Follow the Alva Design System for styling, layout, and component guidelines. Unless the user explicitly asks for a static snapshot, default to a live playbook. Data fetching requirement: Apply the Content Legitimacy Rules when building the UI. All quantitative data in charts, tables, or metric cards must come from feed outputs read at runtime (no inline literals for data).

7. Release

Common steps (all users)

1. Write HTML to ALFS: alva fs write --path '~/playbooks/{name}/index.html' --file ./index.html --mkdir-parents

2. Create playbook draft: alva release playbook-draft — creates DB records, writes draft files and playbook.json to ALFS automatically. This request must include both the URL-safe name and the human-readable display_name. Use [subject/theme] [analysis angle/strategy logic], put the subject/theme first, and keep it within 40 characters. Avoid personal markers such as My, Test, or V2, and generic-only titles such as Stock Dashboard or Trading Bot. Trading symbols: If the playbook involves specific trading assets, include "trading_symbols" in the request — an array of base asset tickers (e.g. ["BTC", "ETH"], ["NVDA", "AAPL"]). The backend resolves each symbol to a full trading pair object and stores the result in the playbook metadata. Max 50 symbols per request. Unknown symbols are silently skipped.

3. Screenshot: Take a screenshot to verify the released playbook renders correctly from the deployed published URL (for example, https://<username>.playbook.alva.ai/<playbook_name>/v1.0.0/index.html):

   alva screenshot --url <published_url> --out /tmp/screenshot.png
   

   The CLI handles authentication automatically. See screenshot.md for full parameter details.

Pro users (subscription_tier = "pro")

1. Show draft link: Output the playbook URL — https://alva.ai/u/<username>/playbooks/<playbook_name>. The draft is accessible only to the creator.
2. Ask: "Your playbook is ready. Would you like to publish it publicly, or keep it private for now?"
   • Publish → call alva release playbook → output the public URL.
   • Keep private → done. Remind the user that only they can access the draft URL.

Free users (subscription_tier = "free")

1. Publish directly: Call alva release playbook — free playbooks are always public. Output the public URL: https://alva.ai/u/<username>/playbooks/<playbook_name>
2. Upsell only on friction: Do not proactively suggest upgrading. But when the user's experience is degraded because of free-tier limitations — wanting private playbooks, hitting the cronjob cap, resource limits, or any other pro-gated feature — acknowledge the limitation and offer the upgrade path: "This feature is available on the Pro plan. You can upgrade at https://alva.ai/pricing to [specific benefit, e.g. keep playbooks private / deploy more cronjobs / ...]."

Use the playbook name and the username from alva whoami to construct the canonical share URL. Use published_url from the release response for verification steps such as screenshots; do not present it as the share link.

Pre-Release Validation

Before calling alva release playbook, verify all of the following:

1. Deployment coverage: Every feed the released playbook reads at runtime must have had a successful alva deploy create AND its feed_id must appear in --feeds. alva run is a test step, not a deployment — a run-tested but undeployed feed has no data at its public @last path and the HTML will fail to read it.
2. Cronjobs are active: All feeds referenced by the playbook have successfully deployed cronjobs.
3. HTML fetches from feeds: The playbook HTML reads quantitative data from feed output paths at runtime (not from inline literals), consistent with the Content Legitimacy Rules.
4. Data is fresh: Read the latest data point from each referenced feed (via @last/1) and check its timestamp. If the latest timestamp is older than 2x the cron interval, warn the user that the playbook will display stale data.
5. Description is accurate: Update frequency claims match actual cronjob status. Data source claims match actual SDK/BYOD calls in the feed script.
6. Target user is correct: The playbook is being released under the requesting user's namespace (see user scope enforcement above).

8. Remix (Create from Existing Playbook)

Users can remix any published playbook to create a customized version. The Remix prompt uses the format @{owner}/{name} to identify the source playbook — e.g. Playbook(@alice/btc-momentum). The agent reads the source playbook's feed scripts (strategy logic) and HTML (dashboard UI), customizes them per the user's request, and deploys a new playbook under their own namespace. If the user does not specify what to change, the agent should ask before proceeding.

See remix-workflow.md for the full step-by-step guide. alva remix commands are exclusively for lineage registration — to read any playbook's files, use alva fs read.

9. Post-release push notification flow

After a playbook is released or kept as draft (Step 7 complete), proactively evaluate whether any deployed feeds produce push-worthy content. Do not wait for the user to ask.

Identify push-worthy feeds

Scan the feeds backing this playbook and classify each:

• Push-worthy (recommend): price signals, crossover/breakout alerts, trading instructions, anomaly detection, periodic research summaries — anything actionable and time-sensitive.
• Not push-worthy (skip): static fundamentals, historical snapshots, low-frequency reference data.

If no feed qualifies, skip this flow entirely.

Check Telegram binding

Read telegram_username from the session (Pre-flight Step 3):

• Connected (non-null) → proceed to recommend.
• Not connected (null) → tell the user: "To receive push notifications, connect your Telegram at https://alva.ai/settings. After connecting, I can set up push alerts for [specific feed description]." Then skip the rest of this flow. The user can return to this later.

Recommend specific feeds

Present a concrete recommendation, not a generic "want push?":

"This playbook's BTC EMA crossover signal feed produces actionable alerts when the trend flips. Want to enable Telegram push notifications for it?"

• User says yes → add signal/targets output to the feed (see feed-sdk.md Pattern D), set push_notify: true on the cronjob, and confirm.
• User says no → accept and move on. Do not ask again.
• User requests push for a different feed → honor their choice and configure accordingly.

If the feed already has signal/targets and push_notify: true, skip — it's already configured.

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Detailed sub-documents (read these for in-depth reference):

Document	Contents
references/api/*.md	Split REST API reference docs (user info, filesystem, run, deploy, release, SDK, screenshots, trading, and errors)
jagent-runtime.md	Writing jagent scripts: module system, built-in modules, async model, constraints
feed-sdk.md	Feed SDK guide: creating data feeds, time series, upstreams, state management
altra-trading.md	Altra backtesting engine: strategies, features, signals, testing, debugging
deployment.md	Deploying scripts as cronjobs for scheduled execution
design-system.md	Alva Design System entry point: tokens, typography, layout; links to widget, component, and playbook specs
remix-workflow.md	Remix: create a new playbook from an existing template
adk.md	Agent Development Kit: adk.agent() API, tool calling, ReAct loop, examples
search.md	Content search SDKs: per-source usage, enrichment patterns, and gotchas for Twitter/X, news, Reddit, YouTube, podcasts, and web
secret-manager.md	Secret upload, CRUD API, and runtime usage via require("secret-manager")
narrative-voice.md	Voice rules for user-facing prose: banned tokens/shapes, copy-paste ADK system-prompt block with few-shots
language.md	Canonical user-facing vocabulary: automation, feed, playbook — and what to avoid

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CLI Reference

Important: Always read the reference docs before making CLI calls. Use alva <command> --help for quick flag/usage reminders.

Reference docs:

• User Info — alva whoami
• Secrets — alva secrets
• Filesystem — alva fs
• Run — alva run
• Deploy Cronjob — alva deploy
• Release — alva release
• Remix — alva remix
• SDK — alva sdk
• Playbook Comments — alva comments
• Screenshot — alva screenshot
• Trading — alva trading
• Notifications — alva notifications
• Error Responses

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Runtime Modules Quick Reference

Scripts executed via alva run run in a sandboxed V8 isolate on Alva's servers -- they cannot access the host machine's filesystem, environment variables, or shell. Host-agent permissions still apply. See jagent-runtime.md for full details.

Module	require()	Description
alfs	require("alfs")	Filesystem (uses absolute paths '/alva/home/<username>/...')
env	require("env")	userId, username, args from request
secret-manager	require("secret-manager")	Read user-scoped third-party secrets stored in Alva Secret Manager
net/http	require("net/http")	fetch(url, init) for async HTTP requests
@alva/algorithm	require("@alva/algorithm")	Statistics
@alva/feed	require("@alva/feed")	Feed SDK for persistent data pipelines + FeedAltra trading engine
@alva/adk	require("@alva/adk")	Agent SDK for LLM requests — agent() for LLM agents with tool calling
@test/suite	require("@test/suite")	Jest-style test framework (describe, it, expect, runTests)

Runtime libraries: Built-in computation modules available via require() (e.g. @alva/technical-indicators/rsi:v1.0.0). Version suffix is optional (defaults to v1.0.0). To discover function signatures, use alva sdk doc --name "...". Module groups: feed_widgets, technical_indicator_calculation_helpers, unified_search.

Data APIs: Financial data (crypto, stock, macro, ETF) is fetched via HTTP from the Arrays backend — see the Data Skills section. Load ARRAYS_JWT via secret.loadPlaintext('ARRAYS_JWT') and call Arrays endpoints with Authorization: Bearer <ARRAYS_JWT>.

Secret Manager: use const secret = require("secret-manager"); then secret.loadPlaintext("OPENAI_API_KEY"). This returns a string when present or null when the current user has not uploaded that secret.

Key constraints: No top-level await (wrap script in (async () => { ... })();). No Node.js builtins (fs, path, http). Module exports are frozen.

---

Feed SDK Quick Reference

See feed-sdk.md for full details.

Feeds are persistent data pipelines that store time series data, readable via filesystem paths.

const { Feed, feedPath, makeDoc, num } = require("@alva/feed");
const http = require("net/http");
const secret = require("secret-manager");
const { indicators } = require("@alva/algorithm");

const ARRAYS_JWT = secret.loadPlaintext("ARRAYS_JWT");
const ARRAYS_BASE = "https://data-tools.prd.space.id";

const feed = new Feed({ path: feedPath("btc-ema") });

feed.def("metrics", {
  prices: makeDoc("BTC Prices", "Close + EMA10", [num("close"), num("ema10")]),
});

(async () => {
  await feed.run(async (ctx) => {
    const raw = await ctx.kv.load("lastDate");
    const lastDateMs = raw ? Number(raw) : 0;

    const now = Math.floor(Date.now() / 1000);
    const start =
      lastDateMs > 0 ? Math.floor(lastDateMs / 1000) : now - 30 * 86400;

    const resp = await http.fetch(
      `${ARRAYS_BASE}/api/v1/crypto/ohlcv?symbol=BTCUSDT&start_time=${start}&end_time=${now}&interval=1h&limit=10000`,
      { headers: { Authorization: "Bearer " + ARRAYS_JWT } }
    );
    const bars = JSON.parse(await resp.text()).data.slice().reverse();
    const closes = bars.map((b) => b.close);
    const ema10 = indicators.ema(closes, { period: 10 });

    const records = bars
      .map((b, i) => ({
        date: b.date,
        close: b.close,
        ema10: ema10[i] || null,
      }))
      .filter((r) => r.date > lastDateMs);

    if (records.length > 0) {
      await ctx.self.ts("metrics", "prices").append(records);
      await ctx.kv.put("lastDate", String(records[records.length - 1].date));
    }
  });
})();

Feed output is readable at (ALFS — quote in CLI): '~/feeds/btc-ema/v1/data/metrics/prices/@last/100'

---

Data Modeling Patterns

All data produced by a feed should use feed.def() + ctx.self.ts().append(). Do not use alfs.writeFile() for feed output data.

Pattern A -- Snapshot (latest-wins): For data that represents current state (company detail, ratings, price target consensus). Use start-of-day as the date so re-runs overwrite.

const today = new Date();
today.setHours(0, 0, 0, 0);
await ctx.self
  .ts("info", "company")
  .append([
    { date: today.getTime(), name: company.name, sector: company.sector },
  ]);

Read @last/1 for current snapshot, @last/30 for 30-day history.

Pattern B -- Event log: For timestamped events (insider trades, news, senator trades). Each event uses its natural date. Same-date records are auto-grouped.

const records = trades.map((t) => ({
  date: new Date(t.transactionDate).getTime(),
  name: t.name,
  type: t.type,
  shares: t.shares,
}));
await ctx.self.ts("activity", "insiderTrades").append(records);

Pattern C -- Tabular (versioned batch): For data where the whole set refreshes each run (top holders, EPS estimates). Stamp all records with the same run timestamp; same-date grouping stores them as a batch.

const now = Date.now();
const records = holdings.map((h, i) => ({
  date: now,
  rank: i + 1,
  name: h.name,
  marketValue: h.value,
}));
await ctx.self.ts("research", "institutions").append(records);

Data Type	Pattern	Date Strategy	Read Query
OHLCV, indicators	Time series (standard)	Bar timestamp	@last/252
Company detail, ratings	Snapshot (A)	Start of day	@last/1
Insider trades, news	Event log (B)	Event timestamp	@last/50
Holdings, estimates	Tabular (C)	Run timestamp	@last/N

See feed-sdk.md for detailed data modeling examples and deduplication behavior.

---

Deploying Feeds

Every feed follows a 6-step lifecycle including every newly created feed or re-created feed:

1. Write -- define schema + incremental logic with ctx.kv

2. Upload — write script to '~/feeds/<name>/v1/src/index.js'

3. Test — alva run --entry-path '~/feeds/<name>/v1/src/index.js' to verify output. For SDK modules you haven't used before in this session, first run a shape-check snippet to verify response structure:

   const r = await mod.someFunction({ symbol: "AAPL" });
   console.log(JSON.stringify(r).slice(0, 500));
   

   Verify the actual response nesting (e.g. {success, response: {rates:[]}} vs flat array) matches your feed script's parsing logic before proceeding. alva run is a test step — it does NOT write to the production @last path. Never skip alva deploy below on the assumption that the run "already produced the data".

4. Grant -- make feed data publicly readable:

   alva fs grant --path '~/feeds/<name>' --subject "special:user:*" --permission read
   

   Grant on the feed root path (not on data/). Subject format: special:user:* (public), special:user:+ (authenticated only), user:<id> (specific user).

5. Deploy -- alva deploy create for scheduled execution

6. Release -- alva release feed to register the feed in the database (requires the cronjob_id from the deploy step)

Pre-Release Verification

Before calling alva release feed or alva release playbook, verify these prerequisites:

1. Grant check — confirm special:user:* read permission exists on the feed path. If missing, run the grant step now.
2. Data check — fetch the feed data path without authentication and confirm HTTP 200 (not 403).
3. HTML check (playbook only) — confirm the playbook HTML file exists in ALFS at the expected path.

If the build was interrupted and resumed, re-run this checklist from the top. Do not assume prior steps completed successfully.

Data Type	Recommended Schedule	Rationale
Stock OHLCV + technicals	0 */4 * * * (every 4h)	Markets update during trading hours
Company detail, price targets	0 8 * * * (daily 8am)	Changes infrequently
Insider/senator trades	0 8 * * * (daily 8am)	SEC filings are daily
Earnings estimates	0 8 * * * (daily 8am)	Updated periodically

See deployment.md for the full deployment guide and API reference.

---

Error Transparency

When SDK modules fail or are unavailable, you MUST be transparent with the user. Do not silently fall back to inferior data sources.

Pro / Subscription-Gated SDKs

When an SDK module returns a Pro-only or subscription error:

1. Inform the user which module is unavailable and why (subscription tier).
2. Assess scope impact — determine whether the gated module is the sole data source for the playbook, or one of several.
   • Partial dependency (other free-tier modules can still power most of the playbook): proceed with a reduced-scope build. Omit the gated section and note it in the playbook (e.g. "Congressional trading data requires Pro — section omitted"). Deliver what you can.
   • Full dependency (the entire playbook hinges on this module): tell the user specifically: "This data requires a Pro subscription. You can upgrade, or provide a custom data source URL and I'll wire it up via BYOD." Do NOT leave an open-ended question; give exactly these two actionable options.
3. Never stop with zero output. If you can build any useful subset of the requested playbook with free-tier modules, do so.
4. Never silently substitute with LLM-fabricated data.

Coverage Limitations

When the user requests data outside Alva's supported asset classes (e.g. forex pairs, which are not in the Data Skills catalog), state the limitation upfront rather than discovering it through failed searches. Suggest BYOD alternatives if a public API exists.

---

Debugging Feeds

Resetting Feed Data (development only)

During development, use the CLI to clear stale or incorrect data. Do not use this in production.

# Clear a specific time series output
alva fs remove --path '~/feeds/my-feed/v1/data/market/ohlcv' --recursive

# Clear an entire group (all outputs under "market")
alva fs remove --path '~/feeds/my-feed/v1/data/market' --recursive

# Full reset: clear ALL data + KV state (removes the data mount, re-created on next run)
alva fs remove --path '~/feeds/my-feed/v1/data' --recursive

Inline Debug Snippets

Test data skill response shapes before building a full feed:

alva run --code '(async()=>{const http=require("net/http");const secret=require("secret-manager");const jwt=secret.loadPlaintext("ARRAYS_JWT");const r=await http.fetch("https://data-tools.prd.space.id/api/v1/crypto/ohlcv?symbol=BTCUSDT&start_time=1735689600&end_time=1735776000&interval=1h&limit=5",{headers:{Authorization:"Bearer "+jwt}});console.log(JSON.stringify(JSON.parse(await r.text()).data[0]));})();'

---

Memory

You have a persistent, file-based memory system on ALFS at '~/memory/'. This directory is created automatically when the user's account is provisioned. Use it to accumulate knowledge about the user across conversations — their identity, preferences, investment style, and any context that would be useful in future sessions.

Memory files are user-visible and editable. The user can read, modify, or delete any memory file through the Alva dashboard or ALFS API. Write memories as if the user will read them.

Storage layout

ALFS paths — use single quotes in the shell (example: '~/memory/MEMORY.md').

~/memory/
├── MEMORY.md     # Concise index — read at the start of every conversation
└── user.md       # User profile, preferences, expertise, investment style

MEMORY.md is the entrypoint. Read it at the start of every conversation to discover what's stored. Keep it concise — under 200 lines. Each entry is one line linking to a topic file:

- [user.md](user.md) — User identity, investment style, knowledge level
- [market-views.md](market-views.md) — Current macro thesis, conviction trades

Topic files (like user.md) hold the actual content. They are read on demand when relevant to the user's request.

user.md — Who is this user

Persistent facts about the user. Update when you learn something new.

# User Profile

> Auto-maintained by Alva Agent. You can edit directly.

## Identity

- Name:
- Role: <!-- e.g. Independent Trader, PM at Fund, Research Analyst, Student -->
- Timezone:
- Language:

## Investment Style

- Markets: <!-- e.g. US Equities, Crypto, Macro, Commodities -->
- Strategy: <!-- e.g. Momentum, Mean Reversion, Fundamental, Event-driven -->
- Holding period: <!-- Intraday / Swing / Position / Long-term -->
- Risk tolerance: <!-- Conservative / Moderate / Aggressive -->
- Watching:

## Knowledge

- Level: <!-- Beginner / Intermediate / Advanced / Professional -->
- Strong: <!-- e.g. Technical analysis, On-chain, Macro -->
- Learning:
- External tools: <!-- e.g. TradingView, Bloomberg, Dune -->

## Preferences

- Communication style: <!-- e.g. terse / detailed / visual -->
- Notification channel:

When to update: User shares personal info, corrects a preference, reveals expertise level, states investment convictions, or you learn something that changes how you should work with them.

Additional topic files

Create new files in '~/memory/' for knowledge that doesn't fit in user.md — market convictions, strategy assumptions, portfolio rules. Add a pointer to MEMORY.md for each new file.

What NOT to save

• Ephemeral conversation details (current debugging session, temp state)
• Things derivable from code or ALFS files
• Raw data or large outputs (store on ALFS as feed data, not in memory)
• Anything already in the Alva skill docs
• Market data that changes every minute (save your interpretation, not the data)

Writing rules

1. Read '~/memory/MEMORY.md' first — check if a relevant file already exists
2. Update existing file if the topic matches. Don't create duplicates
3. Create new file only if no existing file covers the topic
4. Update MEMORY.md — add a one-line entry for each new file
5. Keep MEMORY.md as a concise index — one line per file, under 120 characters
6. Every write → confirm in chat: 📌 Memory updated: {one-sentence summary}

Reading rules

• Every conversation start: Read '~/memory/MEMORY.md' via ALFS. Then read user.md and any topic files relevant to the user's request.
• User references prior work: "that strategy from last time" / "the rules we discussed" → read the relevant memory file.
• User explicitly asks: "do you remember" / "check my profile" → you must read.
• User says to ignore memory: Proceed as if '~/memory/' is empty.

Memory is a claim, not truth

Memory records what was true when the memory was written. Before acting on a memory:

• Memory names a feed or playbook → verify it exists on ALFS before referencing it.
• Memory names a cronjob or parameter → verify current state before recommending changes.
• Memory records a market view → treat as the user's last-known position, not current fact.
• Memory records user preferences → apply directly (these are stable).

If a memory conflicts with what the user just told you, trust what the user says now — and update the memory.

---

Secret Manager

Use Alva Secret Manager whenever a playbook or runtime script needs a third-party credential such as an LLM API key, search token, exchange key, or webhook secret.

• Preferred upload path: ask the user to add or edit the secret in the web UI at https://alva.ai/apikey. Assume this page is available.
• Do not ask the user to paste sensitive third-party secrets into chat when the web upload flow is feasible.
• Do not hardcode secrets in source code, ALFS files, .env, shell snippets, or released playbook assets.
• Runtime access: load secrets inside Alva Cloud code with require("secret-manager").loadPlaintext("NAME").
• loadPlaintext(name) returns the plaintext string when present, or null when the secret is missing for the current user.
• If a required secret is missing, stop and tell the user exactly which secret name to upload at https://alva.ai/apikey.
• For agent-managed setup, inspection, or cleanup, authenticated CRUD endpoints are available via alva secrets.

Read secret-manager.md whenever the task involves uploading, naming, rotating, listing, or using third-party secrets.

---

Altra Trading Engine Quick Reference

Always use Altra for backtesting. Altra handles bar.endTime timestamps, data alignment, and portfolio simulation automatically. Do not manually loop over SDK data (e.g. getCryptoKline) to evaluate trading conditions — this leads to incorrect timestamps and look-ahead bias. Use Altra for all strategies regardless of complexity; it supports any interval ("1min" to "1w") and any combination of OHLCV + external data via registerRawData.

After a successful backtest, you should package the results in a form the user can use. That may be a playbook, a dashboard, or a concise analytical summary, depending on the request. A backtest that only prints raw console output is usually incomplete — see Request Routing above.

See altra-trading.md for full details.

const { FeedAltraModule } = require("@alva/feed");
const { FeedAltra, e, Amount } = FeedAltraModule;
const { AltraModule } = require("@alva/graph");
const { createArraysOhlcvProvider } = AltraModule;
const secret = require("secret-manager");

const ARRAYS_JWT = secret.loadPlaintext("ARRAYS_JWT");
const ohlcvProvider = createArraysOhlcvProvider({ jwt: ARRAYS_JWT });

const altra = new FeedAltra(
  {
    path: "~/feeds/my-strategy/v1",
    startDate: Date.parse("2025-01-01T00:00:00Z"),
    portfolioOptions: { initialCash: 1_000_000 },
    simOptions: { simTick: "1min", feeRate: 0.001 },
    perfOptions: { timezone: "UTC", marketType: "crypto" },
  },
  ohlcvProvider,
);

const dg = altra.getDataGraph();
dg.registerOhlcv("BINANCE_SPOT_BTC_USDT", "1d"); // any interval: "1min" to "1w"
dg.registerFeature({ name: "rsi" /* ... */ });

altra.setStrategy(strategyFn, {
  trigger: { type: "events", expr: e.ohlcv("BINANCE_SPOT_BTC_USDT", "1d") },
  inputConfig: {
    ohlcvs: [{ id: { pair: "BINANCE_SPOT_BTC_USDT", interval: "1d" } }],
    features: [{ id: "rsi" }],
  },
  initialState: {},
});

(async () => {
  await altra.run(Date.now());
})();

---

ADK (Agent Development Kit) Quick Reference

See adk.md for the full API, tool-calling patterns, memory patterns, and implementation examples.

ADK is a universal agent development kit that runs inside the Jagent V8 runtime. Use it to build LLM-powered agents that can reason over tasks, call tools, gather context from multiple sources, and return structured outputs.

It is best suited for workflows where the "thinking" step cannot be expressed as pure deterministic code, such as research synthesis, document analysis, classification, and summarization over real upstream data.

When to Use ADK

Use ADK when you need an agent to:

• Fetch real data through tools, APIs, SDKs, or files
• Reason over multiple inputs before producing an answer
• Synthesize findings into structured notes, summaries, or classifications
• Power periodic research or analysis workflows that run on a schedule
• Add an LLM-driven transformation step inside a larger data pipeline

When NOT to Use ADK

ADK must never be used to fabricate data that should come from real sources. Specifically:

• Do NOT use ADK to generate hiring statistics, financial events, analyst reports, or any quantitative data that claims to originate from a real data pipeline.
• Do NOT present ADK-generated content as if it were sourced from SDKs, APIs, or databases.
• If a data source is unavailable, report the limitation as a blocker — do not use ADK as a fallback data generator.

ADK output that involves reasoning over real data (sentiment classification, trend summarization) is fine, but must be labeled as AI-generated analysis.

---

Deployment Quick Reference

See deployment.md for full details.

Deploy feed scripts or tasks as cronjobs for scheduled execution:

alva deploy create --name btc-ema-update --path '~/feeds/btc-ema/v1/src/index.js' --cron "0 */4 * * *"

Cronjobs execute the script via the same jagent runtime as alva run. Min interval: 1 minute.

Name format: All resource names (cronjobs, feeds, playbooks) must be 1–63 lowercase alphanumeric characters or hyphens, and cannot start or end with a hyphen (DNS label format). Example: btc-ema-update, not BTC EMA Update.

After deploying a cronjob, register the feed, create a playbook draft, then release the playbook for public hosting. The playbook HTML must already be written to ALFS at '~/playbooks/{name}/index.html' via fs/write before releasing.

Important: Feed names and playbook names must be unique within your user space. Before creating a new feed or playbook, use alva fs readdir --path '~/feeds' or alva fs readdir --path '~/playbooks' to check for existing names and avoid conflicts.

# 1. Release feed (register in DB, link to cronjob)
alva release feed --name btc-ema --version 1.0.0 --cronjob-id 42 \
  --description "Fetches BTC/USDT 1h klines from Binance and emits the 20-period EMA as a time series"
# → {"feed_id":100,"name":"btc-ema","feed_major":1}

# 2. Create playbook draft (creates DB record + ALFS draft files automatically)
#    Include trading_symbols when the playbook involves specific assets.
alva release playbook-draft --name btc-dashboard --display-name "BTC Trend Dashboard" --description "BTC market dashboard" --feeds '[{"feed_id":100}]' --trading-symbols '["BTC"]'
# → {"playbook_id":99,"playbook_version_id":200}

# 3. Release playbook (reads HTML from ALFS, uploads to CDN, writes release files automatically)
alva release playbook --name btc-dashboard --version v1.0.0 --feeds '[{"feed_id":100}]' --changelog "Initial release"
# → {"playbook_id":99,"version":"v1.0.0","published_url":"https://alice.playbook.alva.ai/btc-dashboard/v1.0.0/index.html"}

# After release, output the canonical share link to the user:
# https://alva.ai/u/<username>/playbooks/<playbook_name>
# e.g. https://alva.ai/u/alice/playbooks/btc-dashboard

---

Alva Design System

Always read design-system.md first — it covers tokens, typography, theme, and page-level layout. Then read only the spec you need:

1. Generating a widget or chart → design-widgets.md
2. Using a component (Button, Tag, Dropdown, Tab, etc.) → design-components.md
3. Building a trading strategy playbook → design-playbook-trading-strategy.md
4. Only need global rules → stay in design-system.md

---

Filesystem Layout Convention

Path (ALFS — quote in CLI)	Purpose
'~/tasks/<name>/src/'	Task source code
'~/feeds/<name>/v1/src/'	Feed script source code
'~/feeds/<name>/v1/data/'	Feed synth mount (auto-created by Feed SDK)
'~/playbooks/<name>/'	Playbook web app assets
'~/data/'	General data storage
'~/library/'	Shared code modules

Prefer using the Feed SDK for all data organization, including point-in-time snapshots. Store snapshots as single-record time series rather than raw JSON files via alfs.writeFile(). This keeps all data queryable through a single consistent read pattern (@last, @range, etc.).

---

Common Pitfalls

• @last returns chronological (oldest-first) order, consistent with @first and @range. No manual sorting needed.
• Time series reads return flat JSON records. Paths with @last, @range, etc. return JSON arrays of flat records like [{"date":...,"close":...,"ema10":...}]. Regular paths return file content with Content-Type: application/octet-stream.
• last(N) limits unique timestamps, not records. When multiple records share a timestamp (grouped via append()), auto-flatten may return more than N individual records.
• The data/ in feed paths is the synth mount. feedPath("my-feed") gives '~/feeds/my-feed/v1', and the Feed SDK mounts storage at <feedPath>/data/. Don't name your group "data" or you'll get data/data/....
• Public reads require absolute paths. Unauthenticated reads must use '/alva/home/<username>/...' (not '~/...'). Discover your username via alva whoami.
• Top-level await is not supported. Wrap async code in (async () => { ... })();.
• require("alfs") uses absolute paths. Inside the V8 runtime, alfs.readFile() needs full paths like '/alva/home/alice/...'. Get your username from require("env").username.
• No Node.js builtins. require("fs"), require("path"), require("http") do not exist. Use require("alfs") for files, require("net/http") for HTTP.
• Altra run() is async. FeedAltra.run() returns a Promise<RunResult>. Always await it: const result = await altra.run(endDate);
• Altra lookback: feature vs strategy. Feature lookback controls how many bars the feature computation sees. Strategy lookback controls how many feature outputs the strategy function sees. They are independent.
• Quote ~ paths to prevent shell expansion. The shell expands bare ~ to your local home (e.g. /Users/alice/), not the ALFS home ('/alva/home/alice/'). Always quote paths: --path '~/feeds/...'.
• Home directory not provisioned? If you get PERMISSION_DENIED on all ALFS operations (including '~/'), your home directory was not created during sign-up. Call alva fs mkdir --path '~/' to provision it. This is idempotent and safe to call anytime.
• Cronjob path must point to an existing script. The deploy API validates the entry_path exists via filesystem stat before creating the cronjob.
• Always create a draft before releasing. alva release playbook requires the playbook to already exist (created via alva release playbook-draft).
• Create new playbooks from scratch unless you are doing a version update. Only version updates may refer to an existing playbook. For all other new playbooks, do not read existing ones.
• ECharts: use type: 'time' for date axes. Do not pass raw epoch millisecond values as category labels — users will see numbers like 1773840600000 instead of dates. Use type: 'time' axis, which handles formatting automatically, or format dates before passing to a category axis.
• ECharts graph: validate node/edge data. For type: 'graph' series with layout: 'none', verify every edge source/target matches an existing node name, no duplicate node names exist, and node names don't contain special characters that break ECharts internals. Add a try/catch wrapper around chart initialization with a fallback message if rendering fails.
• ECharts sizing: allocate sufficient height. Heatmaps need height = max(300px, numRows * 40px). Primary charts on overview tabs should be at least 400px tall and visually dominant over metric cards. Do not compress charts to fit everything above the fold.
• Separate lastDate watermarks per data source. When a feed combines multiple data sources with different update frequencies (e.g. ETF OHLCV + VIX + CPI), use a separate ctx.kv key for each source's watermark (e.g. lastDate_etf, lastDate_vix, lastDate_cpi). A shared watermark causes slower-updating sources to be permanently filtered out after the first run.

---

Resource Limits

Resource	Limit
V8 heap per execution	2 GB
Write payload	10 MB max per request
HTTP response body	128 MB max
Min cron interval	1 minute