nimble-agent-builder

Nimble Agent Builder

Build, refine, and publish reusable extraction agents on the Nimble platform. Always finish with executed results or runnable code.

User request: $ARGUMENTS

Prerequisites

Pick CLI or MCP at session start — same skill, two transports. Once a transport is selected, stick with it for the session and don't re-probe on every command.

Try CLI first (exposes the full surface area — every flag, batch ops, file I/O):

nimble --version && echo "${NIMBLE_API_KEY:+API key: set}"

• CLI version + API key: set both print → proceed using nimble ... commands.

Try MCP fallback if CLI is missing:

claude mcp list 2>/dev/null | grep -q "nimble" && echo "MCP: connected" || echo "MCP: not connected"

• MCP connected → proceed using mcp__plugin_nimble_nimble__* tools instead of CLI.

Neither available → load rules/setup.md. The install path depends on the host:

• Any Claude product (Code, Cowork, claude.ai) → /plugin install nimble (one command, auto-registers MCP as a Connector, OAuth handles auth).
• Codex CLI or other terminal-only agents → npm i -g @nimble-way/nimble-cli + API key.
• Cursor / VS Code / generic MCP clients → paste the mcp.json snippet from rules/setup.md.

Plugin installed but connector not connected (typical Cowork / claude.ai): if mcp__plugin_nimble_nimble__* tools are listed, verify with one read-only mcp__plugin_nimble_nimble__nimble_agents_list probe before any work. An auth/not-connected error or an OAuth authorization URL means not connected — surface the verbatim connect steps from rules/setup.md and stop. Never substitute WebFetch, WebSearch, curl, or any other tool.

If a tool returns an OAuth "Authorize" link instead of data, present it exactly as given and stop. Never invent a "paste the URL back" / "I'll complete the connection" step — no such step exists — and never claim tools "will activate" then call them in the same turn.

---

Skill ecosystem

nimble-agent-builder and nimble-web-expert work as a pair in the Nimble toolkit:

Skill	Best for	Key commands
nimble-agent-builder (this skill)	Build reusable agents — create, refine, and publish named extraction templates with fixed schemas	CLI: generate, get-generation, publish
nimble-web-expert	Real-time data access — fetch any URL, search, map, crawl, run published agents	extract, search, map, crawl, agent run

Stay in nimble-agent-builder for

• Generating a new agent for a domain
• Refining or updating an existing agent (add fields, fix selectors, change schema)
• Publishing an agent
• Running a published agent via nimble agent run (CLI)
• Validating agent output quality
• Any task phrased as "build", "refine", "update", "add a field to", "publish"

When to route to nimble-web-expert

After publishing an agent — run it directly here via nimble agent run (CLI). Route to nimble-web-expert only when the workflow needs tools this skill doesn't have:

• Need a list of input URLs to feed into the agent? → Switch to nimble-web-expert, run nimble map --url <site> to crawl and generate the input list, then return here to run at scale.
• Need to search for input params? → Switch to nimble-web-expert, run nimble search, then return here with the results.

When the task is not about building an agent:

• One-off URL fetch, web search, site mapping, bulk crawl → nimble-web-expert
• Tell the user: "This is a direct data access task, not an agent-building task. Use nimble-web-expert for this."

When agent generation needs site investigation

If nimble_agents_generate or nimble_agents_update_from_agent cannot produce a working agent because the site's data structure is unknown (wrong selectors, missing XHR patterns, unexpected JS rendering):

Step 1 — Announce: "I can't generate a reliable agent without investigating the live page first. Spawning a site investigation..."

Step 2 — Spawn a Task agent (Task(subagent_type="general-purpose", run_in_background=False)):

Investigate {url} to find CSS selectors and/or XHR API endpoints needed to extract: {fields_needed}.

Use Playwright to probe the live page:
python3 << 'EOF'
from playwright.sync_api import sync_playwright
with sync_playwright() as p:
    browser = p.chromium.launch(headless=True)
    page = browser.new_page()
    api_calls = []
    page.on("request", lambda req: api_calls.append(req) if req.resource_type in ("xhr","fetch") else None)
    page.goto("{url}")
    page.wait_for_timeout(5000)
    for sel in ["[data-price]",".price","h1","[data-testid*=title]","[data-testid*=price]",".product-title"]:
        el = page.query_selector(sel)
        if el: print(f"SELECTOR: {sel!r} -> {el.inner_text()[:80]!r}")
    for req in api_calls[:20]:
        print(f"XHR: {req.method} {req.url[:120]}")
    browser.close()
EOF

Also run a quick nimble extract to check raw rendered HTML:
nimble extract --url "{url}" --render --format markdown | head -100

Return a structured report:
- SELECTORS: working CSS selectors for each required field
- XHR_URLS: any relevant API endpoints found
- RENDER_REQUIRED: yes/no
- SUGGESTED_EXTRACT_COMMAND: the nimble extract command that would work
- NOTES: login walls, pagination, lazy loading, or anything unusual

Do NOT use AskUserQuestion.

Step 3 — Use the report to retry agent generation: pass the selector/XHR context and suggested extract command in the nimble_agents_update_session call.

Step 4 — If investigation also fails: Tell the user: "This site requires complex browser automation that can't be captured in an agent at this stage. Use nimble-web-expert's Tier 4/5 commands with --browser-action or --network-capture to access the data directly."

---

Core principles

• Fastest path to data. Default route: discover agent → get schema → run → display results. Planning and generation are escalation paths.
• Always search existing agents first. Run nimble agent list --limit 100 --search "<domain or vertical>" (CLI) before considering generate. Hard rule.
• Update over generate — always. When a close-match agent exists (same domain/type, even if missing fields or different scope), update it rather than generating from scratch. Updating preserves proven extraction logic and is faster, cheaper, and more reliable. Only generate a new agent when the --search query returns 0 matches for the target domain. Never offer "Create new agent" as the recommended option when a close match exists.
• AskUserQuestion at every decision point in the foreground — no exceptions. Always present the standard AskUserQuestion prompts shown in each step. Never skip them, never auto-advance without asking. Never present choices as plain numbered lists. Constraints: 2–4 options, header max 12 chars, label 1–5 words. Recommended option goes first with "(Recommended)". Note: Task agents NEVER use AskUserQuestion — all decisions are pre-made before launching the Task.
• Schema before run — always. Run nimble agent get --template-name <name> (CLI) before nimble agent run. Present input parameters and output fields in markdown tables. This applies when switching agents too.
• Script generation (Step 2B) is ONLY for large-scale, high-volume tasks. Never generate code for normal interactive requests. Script mode requires ALL of: scale >50 items AND the user explicitly asks for code/script/CSV/batch output. Multi-source requests, dataset requests, and comparison requests do NOT automatically trigger script mode — run them interactively first. The default path is always: discover → run → display results.
• Verify response shape before script generation. Check skills and entity_type from nimble agent get --template-name (CLI) to determine REST API response nesting. See references/agent-api-reference.md > "Response shape inference" and references/sdk-patterns.md > "Response structure verification".
• google_search is not a general search tool. It is a SERP analysis agent for rank tracking and SEO analysis. For finding information, use nimble search (CLI). See references/error-recovery.md.
• All web search MUST use nimble search (CLI). Never use WebSearch, WebFetch, curl, or wget. See Guardrails.
• Agent creation/update runs in background Task agents. Generation takes 1-3 minutes — run the generate → poll → publish loop as a Task agent so the user isn't blocked. See Delegation model.

Delegation model

The foreground conversation orchestrates and presents results. Task agents handle long-running work (generation, discovery, script generation).

Foreground — direct CLI calls via Bash:

CLI command	Purpose	Max calls
nimble agent list --limit 100 --search "<domain>"	Route to existing agent	1 per domain
nimble agent get --template-name	Display schema before run	1 per agent
nimble agent run --agent --params	Interactive execution (≤5 items)	1 per item
nimble search	Web search / discovery	As needed

Task agents — long-running operations:

Phase	Task agent	Foreground does
Discovery (nimble search + nimble map)	Step 1D	Launch, present report
Agent create/update (nimble agent generate → get-generation poll → publish)	Step 3	Launch, present report
Script generation (write code to call existing agent)	Step 2B	Launch, present script

Agent creation runs in a Task agent because generation takes 1-3 minutes (poll loop). The Task agent uses CLI commands via Bash. If CLI is unavailable, it falls back to MCP tools.

For multi-source workflows, launch Task agents sequentially (one per source/phase). Gather reports, then present the combined plan.

CLI commands for Task agents

All Task agent prompts should include this block so the subagent knows the available commands:

**CLI tools (use via Bash):**
- `nimble agent list --limit 100 --search "<domain or vertical>"` — search agents by domain/vertical
- `nimble agent get --template-name <name>` — get agent schema
- `nimble agent run --agent <name> --params '{...}'` — run an agent
- `nimble agent generate --agent-name <name> --prompt "<prompt>" --url "<url>"` — create/refine agent
- `nimble agent generate --agent-name <name> --from-agent <name> --prompt "<prompt>"` — iterate on existing agent
- `nimble agent get-generation --generation-id <id>` — poll generation status
- `nimble agent publish --agent-name <name> --version-id <id>` — publish agent
- `nimble search --query "<query>"` — web search (deep by default)
- `nimble map --url <url> --limit 50` — discover URL patterns on a site

**MCP fallback (only if CLI is not installed):**
| CLI command | MCP tool |
|---|---|
| `nimble agent generate` | `mcp__plugin_nimble_nimble__nimble_agents_generate` |
| `nimble agent get-generation` | `mcp__plugin_nimble_nimble__nimble_agents_status` |
| `nimble agent publish` | `mcp__plugin_nimble_nimble__nimble_agents_publish` |

**CRITICAL: Prefer CLI for all operations. Use MCP only when CLI is unavailable. NEVER use WebSearch, WebFetch, curl, or wget. NEVER construct MCP endpoint URLs manually.**

Response shapes

Layer	Path	Shape	When used
CLI (nimble agent run) — SERP	data.parsing	list (array)	Interactive run (Step 2A)
CLI (nimble agent run) — PDP	data.parsing	dict (flat)	Interactive run (Step 2A)
REST API — ecommerce SERP	data.parsing	list (array)	Script generation (Step 2B)
REST API — non-ecommerce SERP	data.parsing.entities.{Type}	dict with nested arrays	Script generation (Step 2B)
REST API — PDP	data.parsing	dict (flat)	Script generation (Step 2B)

Always check typeof/isinstance before iterating REST responses.

Step 1: Route

From $ARGUMENTS, detect 3 things:

1. Clarity — clear (default) or needs-planning

Only needs-planning when ALL of these are absent: a target URL/site/domain, clear data to extract, a single well-scoped task. Most requests are clear.

2. Agent match — run nimble agent list --limit 100 --search "<domain or vertical>" (CLI, Bash). Use the user's named domain/site as the search term (e.g. --search "amazon", --search "jobs", --search "ecommerce"). This is ALWAYS the first action. For multi-source requests (e.g., "compare Amazon and Walmart prices"), run one search per source. If no match found for a source, route it to Discovery (Step 1D).

Result	Route
Exact match	Show schema summary + AskUserQuestion: "Use this agent" (Recommended) / "Create new agent" → Step 3
Close match (same domain/type, missing fields or different scope)	Show schema gaps + AskUserQuestion: "Update this agent" (Recommended) / "Create new agent". Always recommend update — it preserves existing extraction logic and is faster than generating from scratch.
2+ plausible matches	Show table + AskUserQuestion with top matches + "Update closest agent" (Recommended). Pick the agent with the most field overlap.
0 matches	Launch Discovery Task agent (Step 1D) → results inform Step 3. This is the ONLY case where generating a new agent is appropriate.

3. Execution mode — interactive (default) or script

Interactive is ALWAYS the default. Route to script generation (Step 2B) ONLY when BOTH conditions are met: (a) scale is explicitly >50 items or the user provides a batch input file, AND (b) the user explicitly asks for code, a script, a CSV export, or batch processing. Words like "dataset", "compare", "multi-source", or "2 sources" do NOT trigger script mode — run these interactively. Script generation writes code that calls an existing agent — it does not create new agents. If no agent exists yet, resolve that first (Step 3) before generating a script.

Step 1P: Plan mode (rare — only when needs-planning)

1. Clarify — AskUserQuestion to resolve critical unknowns (max 2 questions). Focus on: what site(s), what data fields, what output format.
2. Explore — nimble agent list --limit 100 --search "<domain>" (CLI, once per domain). For unfamiliar domains, launch Discovery Task agents (Step 1D).
3. Present plan — gap analysis table:

#	Site / Data Source	Agent	Status
1	amazon.com products	amazon-product-details	Existing
2	walmart.com products	—	Generate

4. Execute — Step 2 for existing agents, Step 3 for generations (as Task agents).

Step 1D: Discovery (Task agent — for unfamiliar domains)

Launch when nimble agent list --limit 100 --search "<domain>" returns 0 matches for the target domain and it needs exploration. Runs as Task(subagent_type="general-purpose", run_in_background=False). The foreground tells the user: "Exploring {domain} to understand available data..."

Task prompt template:

Explore {domain} for {user_intent}.

Use the Nimble CLI to discover the site structure and available data:

1. **Map the site** (understand URL patterns and sections):
   ```bash
   nimble map --url "https://{domain}" --limit 50

This reveals listing pages, detail pages, site sections, and URL patterns.

2. Search for real examples (deep content extraction):

   nimble search --query "{domain} {keywords}" --max-results 5
   

   This fetches and extracts full page content from each result, giving you product listings, field structures, and example data.

Return a structured report:

• DOMAIN: {domain}
• ESTIMATED_ITEMS: count matching query
• LISTING_URL_PATTERN: e.g., /category/filter?color=green
• DETAIL_URL_PATTERN: e.g., /p/{slug}-{SKU}.html
• AVAILABLE_FIELDS: list of extractable fields (name, price, description, materials, etc.)
• MISSING_FIELDS: fields the user wants but the site doesn't have (e.g., ratings, reviews)
• RECOMMENDED_APPROACH: generate custom agent / use existing agent from {alternative} / combine sources
• SAMPLE_URLS: 2–3 example URLs for agent generation
• LIMITATIONS: login walls, pagination limits, JS rendering, etc.

Do NOT use AskUserQuestion. Do NOT use nimble_find_search_agent, nimble_run_search_agent, or nimble_url_extract. Do NOT use WebSearch, WebFetch, or any non-Nimble-CLI search/fetch method.

On receiving the report, the foreground conversation:

1. Presents key findings to the user.
2. If data gaps exist (e.g., missing ratings), asks the user via `AskUserQuestion` how to proceed.
3. Routes to Step 3 (generate) with the discovery context, or Step 2 if existing agents cover the need.

## Step 2: Run existing agent

Two sub-paths based on execution mode.

### 2A: Interactive (small scale, display output)

**2A-1.** Run `nimble agent get --template-name <name>` (CLI). Present schema in markdown tables:

- **Input parameters:** name, required, type, description, example
- **Output fields:** key fields from `skills` dict

See **`references/agent-api-reference.md`** > "Input Parameter Mapping" for the full `input_properties` format and mapping rules.

**2A-2.** Always confirm before running via `AskUserQuestion`:

question: "Run {agent_name} with these parameters?" header: "Confirm" options:

• label: "Run agent (Recommended)" description: "Execute {agent_name} with {summary of inferred parameters}"
• label: "Change parameters" description: "Adjust input parameters before running"
• label: "Create new agent" description: "Create a custom agent instead (Step 3)"

**2A-3.** Run `nimble agent run --agent <name> --params '{...}'` (CLI). Present results as markdown table. Always ask what to do next:

question: "What next?" header: "Next step" options:

• label: "Done" description: "Finish with these results"
• label: "Run again" description: "Re-run with different parameters"

Do NOT offer script generation as a next step unless the user explicitly mentions needing large-scale extraction (>50 items) or batch processing. Script generation is not a natural follow-up to interactive runs.

**Bulk (2–5 URLs):** Run per URL, aggregate results, handle individual failures without aborting. See **`references/batch-patterns.md`** > "Interactive batch extraction".

### 2B: Script generation (ONLY for large-scale, high-volume tasks)

**This step is ONLY reached when the user explicitly needs to process >50 items at scale or requests batch code/script generation.** Normal requests — even multi-source or "dataset" requests — are handled interactively via Step 2A. Writes a runnable script that calls an existing Nimble agent at scale via the SDK/REST API. This does NOT create new agents — the agent must already exist. Runs as a Task agent. The foreground infers language, launches the agent, and presents the generated script for confirmation.

**2B-1.** Infer language from project context (foreground, before launching):

| Project file                                 | Language          |
| -------------------------------------------- | ----------------- |
| `pyproject.toml`, `requirements.txt`, `*.py` | Python            |
| `package.json`, `tsconfig.json`              | TypeScript/Node   |
| `go.mod`                                     | Go (REST API)     |
| None of the above                            | Default to Python |

**2B-2.** Launch script generation Task agent: `Task(subagent_type="general-purpose", run_in_background=False)`.

**Task prompt template:**

Write a {language} script that calls existing Nimble agent(s) at scale via SDK/REST API.

Use the Nimble CLI to inspect agent schemas (via Bash, NOT MCP):

nimble agent get --template-name <agent_name>

This returns the full input/output schema for the agent.

CRITICAL: Use CLI (Bash) for all Nimble operations. NEVER use WebSearch, WebFetch, curl, or wget. NEVER construct MCP endpoint URLs manually.

Existing agents to call: {agent_names} User intent: {user_prompt} Output format: {csv/json/etc} Scale: {number of items/queries}

This is SCRIPT GENERATION — writing code that calls existing agents. Do NOT create new agents (no nimble_agents_generate/update/publish). The agents listed above already exist.

Steps:

1. Run nimble agent get --template-name <agent> (CLI) for each agent to inspect input_properties and skills.
2. Read the reference files:
   • references/sdk-patterns.md (Python) or references/rest-api-patterns.md (other languages)
   • references/batch-patterns.md (for multi-store normalization)
3. Write a complete, ready-to-run script with:
   • Smoke test first — validate a single query before full batch. Abort on failure.
   • Progress reporting — compact single-line status after each poll cycle.
   • Pagination handling for large result sets.
   • Multi-store field normalization (if applicable).
   • Output to {format}.
   • Incremental file writes for large pipelines (50+ jobs).

Return the complete script and a brief summary of:

• Agent schemas used (input params, key output fields)
• Normalization mappings (if multi-store)
• Total estimated API calls

Do NOT use AskUserQuestion. Do NOT use nimble_find_search_agent or nimble_run_search_agent. Do NOT call nimble_agents_generate, nimble_agents_update_from_agent, nimble_agents_update_session, or nimble_agents_publish. Do NOT use WebSearch, WebFetch, bash curl, or any non-MCP search/fetch method.

**2B-3.** Present the generated script and confirm execution via `AskUserQuestion` (foreground):

question: "Run this script?" header: "Confirm" options:

• label: "Run script (Recommended)" description: "Execute the generated script"
• label: "Edit first" description: "Review and modify the script before running"

**No agent validation step here.** The 50-input validation flow (Step 3) is only for agent creation/update. Script generation uses an existing, already-validated agent — just write the script and run it.

## Step 3: Update existing agent or create new (on the Nimble platform)

Updates an existing agent (preferred) or creates a new one on Nimble's platform. **Default to update** when a close-match agent was found in Step 1 — pass the existing agent name to the Task agent so it uses `nimble_agents_update_from_agent` instead of `nimble_agents_generate`. Only create a new agent when Step 1 returned 0 matches. This is NOT code/script generation — it creates/modifies an extraction definition callable via Step 2A or 2B. ALWAYS runs as a Task agent (`run_in_background=False`).

### 3-1. Create a stable `session_id` (UUID v4).

### 3-2. Ask the user ONCE (foreground only — agent creation/update ONLY, never for script generation):

question: "Run refinement-validation before publishing?" header: "Validate" options:

• label: "Yes, validate (Recommended)" description: "Discovery → generate → validate 50 inputs (80% pass) → publish. Auto-retries on failure."
• label: "No, generate only" description: "Generate → publish immediately without validation testing"

### 3-3. Launch Task agent

Set `refine_validate` to the user's choice. Launch `Task(subagent_type="general-purpose", run_in_background=False, max_turns=50)` using the prompt template from **`references/generate-update-and-publish.md`** (includes MCP tool registry, lifecycle phases, and all rules). Tell the user: "Agent generation started. I'll report results when complete."

The Task agent executes a closed-loop lifecycle: Discovery → Create/Update → Poll → Validate → Publish → Report. On failure, it auto-triggers an update loop (max 2 cycles, 15-minute wall-clock timeout). See the reference file for complete details.

### 3-4. Present report

When the Task agent completes, present the report. On success, route to Step 2A or 2B. On failure after max cycles, offer:

question: "Agent validation did not reach 80% pass rate. How to proceed?" header: "Next step" options:

• label: "Publish anyway" description: "Publish with current pass rate ({rate}%)"
• label: "Update agent" description: "Provide specific instructions to refine the agent"

## Step 4: Final response

End with a concise summary table:

| Field             | Value                      |
| ----------------- | -------------------------- |
| Agent(s) used     | `agent_name`               |
| Source            | Existing / Generated       |
| Records extracted | count                      |
| Output            | Displayed / `filename.csv` |

Include the extraction results (or top N if large).

## Additional references

Load reference files **only during large-scale script generation (Step 2B)** or agent creation (Step 3). Do NOT load these for interactive runs (Step 2A) — MCP tool schemas are sufficient.

**For script generation (Step 2B) only:**

- **`references/sdk-patterns.md`** — Running agents, async endpoint, batch pipelines, incremental file writes.
- **`references/rest-api-patterns.md`** — REST API patterns for TypeScript, Node, curl, and other non-Python languages.
- **`references/batch-patterns.md`** — Multi-store comparison, normalization, interactive batch, codegen walkthrough.

**For agent creation/update (Step 3) only:**

- **`references/generate-update-and-publish.md`** — Full agent creation/update lifecycle: discovery, creation, polling, SDK validation (50 inputs, 80% threshold), publish, reporting, update loop.

**General (any step, load as needed):**

- **`references/agent-api-reference.md`** — MCP tools reference plus input parameter mapping.
- **`references/error-recovery.md`** — Error handling and recovery patterns.

## Guardrails

- **Agent creation/update runs in Task agents.** Generation takes 1-3 minutes — use a background Task agent for the generate → poll → publish loop. See [Delegation model](#delegation-model).
- **All operations use CLI (Bash).** MCP tools are a fallback only when CLI is unavailable.
- **All web search MUST use `nimble search` (CLI).** NEVER use `WebSearch`, `WebFetch`, `curl`, or `wget` — in foreground or Task agents.
- **Every Task agent prompt MUST include the CLI commands block** (see [Delegation model](#delegation-model)).
- **Never** use `nimble_find_search_agent`, `nimble_run_search_agent`, or any WSA template tools.
- **Update state machine:** use `nimble agent generate --from-agent` to iterate on existing agents. Each iteration creates a new generation to poll.
- **Hard 429 rule.** On quota errors: stop, report exhaustion. Do not retry or switch tools.
- Published agents are automatically forked when updated. UBCT-based agents cannot be updated — generate a new one instead.
- **Never load SDK/batch references for interactive runs (Step 2A).** CLI output from `nimble agent get --template-name` is sufficient. Load references only for Step 2B (script generation) and Step 3 (agent creation).
- Present results in markdown tables. Never show raw JSON.