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ai-brainstorm

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آخر تحديث١٨ يوليو ٢٠٢٦ في ١٩:٣٦

Forces rigorous design interrogation BEFORE any code: explores approaches, surfaces ambiguity, gathers evidence, produces an approved spec that becomes the contract for /ai-plan. Trigger for 'lets add X', 'how should we handle Y', 'whats the best approach', 'I am thinking about', 'what should we build for'. Not for existing approved specs; use /ai-plan instead. Not for execution; use /ai-build instead.

التثبيت

التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.

تشغيل في Manus

المصدر

arcasilesgroup

arcasilesgroup/ai-engineering

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تشغيل في Manus

المهن ذات الصلةSOC

استنادا إلى تصنيف SOC المهني

مطوّرو البرمجياتمهن الحاسوب والرياضيات·SOC 15-1252

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SKILL.md

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المزيد من هذا المستودع

نفس المستودع

ai-advise

arcasilesgroup/ai-engineering

Proactive governance advisor — checks standards, decisions, and quality trends during development. Always advisory, NEVER blocks. Three modes: `advise` (post-edit), `gate` (pre-dispatch), `drift` (on-demand decision audit). Trigger for 'governance check', 'advise on this change', 'check for drift', 'is this aligned with active decisions', 'shift-left advisory'. Not for blocking gates — use /ai-verify. Not for narrative code review — use /ai-review.

2026-07-1853

ai-animation

arcasilesgroup/ai-engineering

Designs motion, transitions, and micro-interactions for UI components: spring animations, gestures, easing, staggers, scroll-triggered and loading/skeleton motion. Trigger for 'animate this', 'add transitions', 'micro-interactions for', 'gesture design', 'swipe to dismiss', 'easing for this', 'review this motion for polish'. Not for design systems; use /ai-design instead. Not for visual art; use /ai-visual instead. Not for testing animation code; use /ai-test instead.

2026-07-1853

ai-autopilot

arcasilesgroup/ai-engineering

Delivers large multi-concern specs and backlog runs autonomously: decomposes specs into sub-specs (or normalizes work items into a backlog DAG), deep-plans with parallel agents, builds a dependency DAG, implements in waves, runs a single final quality loop with one bounded quality-remediation pass (verify+guard+review on full changeset), delivers via PR. Trigger for 'implement spec-NNN end to end', 'autopilot this', 'autonomous delivery', 'decompose and ship', 'run the backlog', 'execute these GitHub issues', 'process the sprint backlog'. Invocation is the approval gate. Not for small or single-concern tasks; use /ai-build instead. Not for ambiguous requirements; use /ai-brainstorm first.

2026-07-1853

ai-board

arcasilesgroup/ai-engineering

Operates the project board (GitHub Projects v2 or Azure DevOps): discovers configuration after install (fields, state mappings, process templates) and syncs work-item state at lifecycle transitions. Trigger for 'set up the board', 'configure our ADO board', 'discover board fields', 'move this issue to in-review', 'update the board', 'mark as in progress', 'sync the work item state'. Two subcommands: `discover` (post-install configuration write) and `sync` (lifecycle state transitions). Auto-invoked via `sync` by /ai-brainstorm, /ai-build, and /ai-pr; fail-open. Not for backlog execution; use /ai-autopilot --backlog instead.

2026-07-1853

ai-branch-cleanup

arcasilesgroup/ai-engineering

Cleans branches safely: switches to the default branch, prunes merged and squash-merged branches, syncs to remote, sweeps stale specs, rotates `.ai-engineering/runtime/` per retention policy. Trigger for 'tidy up', 'tidy branches', 'sync to main', 'delete old branches', 'start fresh', 'rotate runtime'. Auto-invoked by /ai-pr after merge. Not for committing changes; use /ai-commit instead. Not for code-level dead-code removal; use /ai-simplify instead.

2026-07-1853

ai-build

arcasilesgroup/ai-engineering

Canonical implementation gateway: reads approved plan.md, resolves stack from manifest, deterministic-routes each task to its adapter, dispatches the build agent in an isolated worktree, runs TDD self-validation per task, then a single final quality loop with one bounded quality-remediation pass on the full changeset before /ai-pr. Trigger for 'go', 'start building', 'execute the plan', 'implement it', 'lets do this', 'build the plan', 'resume', 'continue'. Not without an approved plan; run /ai-plan first. Not for multi-concern specs needing decomposition; use /ai-autopilot instead. Not for a single function or subcomponent; use /ai-code.

2026-07-1853

name	ai-brainstorm
description	Forces rigorous design interrogation BEFORE any code: explores approaches, surfaces ambiguity, gathers evidence, produces an approved spec that becomes the contract for /ai-plan. Trigger for 'lets add X', 'how should we handle Y', 'whats the best approach', 'I am thinking about', 'what should we build for'. Not for existing approved specs; use /ai-plan instead. Not for execution; use /ai-build instead.
effort	mid
argument-hint	[feature or problem description] [work item ID] \| --consolidate-spec <slug>
model_tier	sonnet
mirror_family	antigravity-skills
generated_by	ai-eng sync
canonical_source	.claude/skills/ai-brainstorm/SKILL.md
edit_policy	generated-do-not-edit

Brainstorm

Design-interrogation skill: forces rigorous thinking BEFORE any code and produces an approved spec — the contract for /ai-plan.

Workflow

Principle: §10.6 SDD — this skill produces the spec. HARD GATE: no implementation runs until the operator explicitly approves it.

-1. Live-slot guard (D-167-05) — UNLESS --consolidate-spec, run python .ai-engineering/scripts/spec_lifecycle.py slot_status BEFORE any spec.md write (Step 6 and the Step 0b condensed path both write). If JSON reports occupied: true AND state ≠ shipped: surface spec_id/slug/state and ask the operator to consolidate first (/ai-brainstorm --consolidate-spec <slug>) or confirm the overwrite. On occupied: false, shipped, or any script error: proceed silently. Fail-open — advisory, never blocks interrogation.
0a. Fast-path --consolidate-spec <slug> — read .agents/skills/_shared/consolidate-spec.md and run the shared handler (resolve record, append _history.md row via spec_lifecycle.py mark_shipped, clear spec.md/plan.md to placeholders). STOP after consolidation — no interrogation. Fail-open on missing script.
0b. Auto-spec gate — follow handlers/auto-spec-gate.md. Classify the working-tree diff via ai_engineering.brainstorm.auto_spec_gate.classify_diff BEFORE interrogation. Any hard trigger OR any threshold exceeded → full interrogation (Step 1). Else → condensed-spec path: draft the minimum-viable spec from the diff, present for one-shot approval, STOP at Step 7. manifest.brainstorm.auto_spec_gate.enabled: false → no-op (fail-open to full interrogation).
0. Lifecycle bootstrap (before evidence sweep) — python .ai-engineering/scripts/spec_lifecycle.py start_new <slug> <title> mints/refreshes the DRAFT record at .ai-engineering/state/specs/<slug>.json. Fail-open: non-zero exit logs and continues.
1. Work-item context (only when a work-item ID is given, e.g. AB#100 / #45): a. read manifest.yml work_items for active provider + team config; b. fetch item + hierarchy — GitHub gh issue view <n> --json title,body,labels,milestone,assignees; Azure az boards work-item show --id <n> --expand relations -o json; c. walk Feature → User Story → Tasks (parent/child relations); d. use all standard + custom fields the platform provides; e. pre-fill refs in the generated spec frontmatter; f. /ai-board sync refinement <ref> to move the item to refinement (fail-open).
2. Enhance input — follow handlers/prompt-enhance.md to clarify and sharpen user input before interrogation.
3. Evidence sweep — when current state spans multiple repo / governance surfaces, dispatch parallel read-only ai-explore passes first, summarize, then sharpen the next question or spec boundary.
4. Interrogate — follow handlers/interrogate.md (see Questioning Rules).
5. Propose approaches — 2-3 options with trade-offs (never one). Scope was settled at Step 0b; no post-interrogation file-count heuristic.
6. Draft spec — write .ai-engineering/specs/spec.md, then run Step 0.5 section pre-flight (deterministic, BEFORE any LLM pass): ai-eng spec verify --sections .ai-engineering/specs/spec.md (spec-139 M7) scans the five required headers (## Summary, ## Goals, ## Non-Goals, ## Decisions, ## Risks) and emits JSON valid / missing_sections / present_sections. Exit 1 → patch missing headers, re-run. Only on valid=true proceed to LLM schema validation against .ai-engineering/reference/spec-schema.md.
7. Board sync (ready) — if a work-item ID was given at Step 1, /ai-board sync ready <ref> (fail-open).
8. Review spec — follow handlers/spec-review.md (max 3 iterations).
9. STOP — approval. On operator approval, call python .ai-engineering/scripts/spec_lifecycle.py approve <spec_id> (<spec_id> from frontmatter spec:, fallback slug:) to transition DRAFT→APPROVED and mirror status: approved into spec.md. Fail-open (D-161-08): non-zero exit logs, does NOT block STOP. Present the approval ask with the 6-field value block (Bottom line / Why it matters / What's done / Risk / Next / Details) per .ai-engineering/reference/value-lens.md at the resolved audience level (default full) — the spec text stays a carve-out. Then present the approved spec; user runs /ai-plan.

Questioning Rules

ONE question at a time — never batch. Prefer multiple choice (A/B/C) over open-ended.
Frame each question and option in plain terms (impact / effort / risk) per value-lens.md so a non-technical sponsor can answer.
Challenge vague language — "improve", "optimize", "clean up" are not requirements.
Push back on scope creep: "Is this in scope for v1?" Explore edge cases the user has not mentioned.
Max 10 questions per session; if you need more, the problem is too big — split it.
When input requires research to understand current state (audits, "is this working?", "how is X organized?"): gather data, present findings, THEN interrogate. Research precedes interrogation, it is not interrogation.

Common Mistakes

Writing implementation details in the spec — specs describe WHAT, not HOW.

Integration

Called by: user, or /ai-plan when requirements are unclear. Calls: handlers/auto-spec-gate.md (Step 0b classifier via ai_engineering.brainstorm.auto_spec_gate.classify_diff), handlers/prompt-enhance.md, handlers/interrogate.md, handlers/spec-review.md, /ai-board sync (refinement + ready), .ai-engineering/scripts/spec_lifecycle.py start_new (fail-open bootstrap) + approve (Step 9 DRAFT→APPROVED, fail-open). Transitions to: /ai-plan (ONLY — never directly to /ai-build).

Examples

User: "AB#456 says 'improve search performance' — what does that even mean?"

/ai-brainstorm AB#456

Pulls the work item, surfaces ambiguity, drives the user to specific measurable acceptance criteria, links the spec to the work item, and produces an approved spec at .ai-engineering/specs/spec.md with decisions recorded.

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