From galeharness-cli
Generate and critically evaluate grounded improvement ideas for the current project. Use when asking what to improve, requesting idea generation, exploring surprising improvements, or wanting the AI to proactively suggest strong project directions before brainstorming one in depth. Triggers on phrases like 'what should I improve', 'give me ideas', 'ideate on this project', 'surprise me with improvements', 'what would you change', or any request for AI-generated project improvement suggestions rather than refining the user's own idea.
npx claudepluginhub wangrenzhu-ola/galeharnesscodingcli --plugin galeharness-cliThis skill uses the workspace's default tool permissions.
**Note: The current year is 2026.** Use this when dating ideation documents and checking recent ideation artifacts.
Creates isolated Git worktrees for feature branches with prioritized directory selection, gitignore safety checks, auto project setup for Node/Python/Rust/Go, and baseline verification.
Executes implementation plans in current session by dispatching fresh subagents per independent task, with two-stage reviews: spec compliance then code quality.
Dispatches parallel agents to independently tackle 2+ tasks like separate test failures or subsystems without shared state or dependencies.
Note: The current year is 2026. Use this when dating ideation documents and checking recent ideation artifacts.
gh:ideate precedes gh:brainstorm.
gh:ideate answers: "What are the strongest ideas worth exploring?"gh:brainstorm answers: "What exactly should one chosen idea mean?"gh:plan answers: "How should it be built?"This workflow produces a ranked ideation artifact in docs/ideation/. It does not produce requirements, plans, or code.
Use the platform's blocking question tool when available (AskUserQuestion in Claude Code, request_user_input in Codex, ask_user in Gemini, ask_user in Pi (requires the pi-ask-user extension)). Otherwise, present numbered options in chat and wait for the user's reply before proceeding.
Ask one question at a time. Prefer concise single-select choices when natural options exist.
<focus_hint> #$ARGUMENTS </focus_hint>
Interpret any provided argument as optional context. It may be:
DX improvementsplugins/galeharness-cli/skills/low-complexity quick winstop 3, 100 ideas, or raise the barIf no argument is provided, proceed with open-ended ideation.
gh:brainstorm defines the selected one precisely enough for planning. Do not skip to planning from ideation output.Config:
At the start of execution, use your native file-read tool to read .compound-engineering/config.local.yaml from the repository root. If the file is missing in the current worktree, check the main repository root (the parent of .git/worktrees). If the file is missing or unreadable, do not block the workflow — proceed silently with default settings.
If the config file contains language: en, write documents in English.
If the file is missing, contains language: zh-CN, or has no language key, write documents in Chinese (default).
Before any other action, log the skill start event so this execution appears on the task board:
gale-task log skill_started --skill gh:ideate --title "<focus-or-topic>" to register this execution on the task board.gale-task is not on PATH or the command fails, skip and continue — this must never block the skill.Look in docs/ideation/ for ideation documents created within the last 30 days.
Treat a prior ideation doc as relevant when:
If a relevant doc exists, ask whether to:
If continuing:
Infer three things from the argument:
Issue-tracker intent triggers when the argument's primary intent is about analyzing issue patterns: bugs, github issues, open issues, issue patterns, what users are reporting, bug reports, issue themes.
Do NOT trigger on arguments that merely mention bugs as a focus: bug in auth, fix the login issue, the signup bug — these are focus hints, not requests to analyze the issue tracker.
When combined (e.g., top 3 bugs in authentication): detect issue-tracker intent first, volume override second, remainder is the focus hint. The focus narrows which issues matter; the volume override controls survivor count.
Default volume:
Honor clear overrides such as:
top 3100 ideasgo deepraise the barUse reasonable interpretation rather than formal parsing.
Before Phase 1, query the vector memory database for related ideation and improvement ideas:
Extract a search query from the focus hint:
Run (requires env vars HKT_MEMORY_API_KEY, HKT_MEMORY_BASE_URL, HKT_MEMORY_MODEL):
hkt-memory retrieve \
--query "<extracted query>" \
--layer all --limit 10 --min-similarity 0.35 \
--vector-weight 0.7 --bm25-weight 0.3
If results returned, prepare context for Phase 1 and Phase 2:
## Related ideation from HKTMemory
Source: vector database. Treat as additional context, not primary evidence.
[results here, each tagged with (similarity: X.XX)]
Use this to:
If no results or command error, proceed silently.
Before generating ideas, gather codebase context.
Run agents in parallel in the foreground (do not use background dispatch — the results are needed before proceeding):
Quick context scan — dispatch a general-purpose sub-agent using the platform's cheapest capable model (e.g., model: "haiku" in Claude Code) with this prompt:
Read the project's AGENTS.md (or CLAUDE.md only as compatibility fallback, then README.md if neither exists), then discover the top-level directory layout using the native file-search/glob tool (e.g.,
Globwith pattern*or*/*in Claude Code). Return a concise summary (under 30 lines) covering:
- project shape (language, framework, top-level directory layout)
- notable patterns or conventions
- obvious pain points or gaps
- likely leverage points for improvement
Keep the scan shallow — read only top-level documentation and directory structure. Do not analyze GitHub issues, templates, or contribution guidelines. Do not do deep code search.
Focus hint: {focus_hint}
Learnings search — dispatch galeharness-cli:learnings-researcher with a brief summary of the ideation focus.
Issue intelligence (conditional) — if issue-tracker intent was detected in Phase 0.2, dispatch galeharness-cli:issue-intelligence-analyst with the focus hint. If a focus hint is present, pass it so the agent can weight its clustering toward that area. Run this in parallel with agents 1 and 2.
If the agent returns an error (gh not installed, no remote, auth failure), log a warning to the user ("Issue analysis unavailable: {reason}. Proceeding with standard ideation.") and continue with the existing two-agent grounding.
If the agent reports fewer than 5 total issues, note "Insufficient issue signal for theme analysis" and proceed with default ideation frames in Phase 2.
Consolidate all results into a short grounding summary. When issue intelligence is present, keep it as a distinct section so ideation sub-agents can distinguish between code-observed and user-reported signals:
Slack context (opt-in) — never auto-dispatch. Route by condition:
galeharness-cli:slack-researcher with the focus hint in parallel with other Phase 1 agents. Include findings in the grounding summary.Do not do external research in v1.
Generate the full candidate list before critiquing any idea.
Document Language: When language: zh-CN (or default), write all prose content in Chinese. Keep section headers (## Survivors, ## Rejected Ideas, etc.) and YAML frontmatter keys in English. Translate paragraphs, list items, and table content. Do NOT translate code blocks, inline code, file paths, or URLs.
Dispatch 3-4 parallel ideation sub-agents on the inherited model (do not tier down -- creative ideation needs the orchestrator's reasoning level). Omit the mode parameter so the user's configured permission settings apply. Each targets ~8-10 ideas (yielding ~30 raw ideas, ~20-25 after dedupe). Adjust per-agent targets when volume overrides apply (e.g., "100 ideas" raises it, "top 3" may lower the survivor count instead).
Give each sub-agent: the grounding summary, the focus hint, the per-agent volume target, and an instruction to generate raw candidates only (not critique). Each agent's first few ideas tend to be obvious -- push past them. Ground every idea in the Phase 1 scan.
Assign each sub-agent a different ideation frame as a starting bias, not a constraint. Prompt each to begin from its assigned perspective but follow any promising thread -- cross-cutting ideas that span multiple frames are valuable.
Frame selection:
Ask each sub-agent to return a compact structure per idea: title, summary, why_it_matters, evidence/grounding hooks, optional boldness or focus_fit signal.
After all sub-agents return:
After merging and synthesis, read references/post-ideation-workflow.md for the adversarial filtering rubric, presentation format, artifact template, handoff options, and quality bar. Do not load this file before Phase 2 agent dispatch completes.
After the ideation artifact is written to docs/ideation/:
hkt-memory store \
--content "<full ideation document>" \
--title "Ideation: [document title]" \
--topic "ideation" \
--layer all
Stored ideation to HKTMemoryNote: This enables future ideation sessions to discover and build upon these ideas through Phase 0.5's retrieve step.
After the ideation workflow is fully complete, log the completion event:
gale-task log skill_completed to record the completion event.gale-task is not on PATH or the command fails, skip and continue — this must never block the skill.