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caipe-iocaipe-io/ai-platform-engineering/.claude/skills/speckit.plan/SKILL.md

speckit.plan

Skill444 starsChanged 4 days agoApache-2.0View on GitHub

You MUST consider the user input before proceeding (if not empty). Output: research.md with all NEEDS CLARIFICATION resolved Prerequisites: research.md complete Output: data-model.md, /contracts/*, quickstart.md, **db-migration.md when St

3 other projects use a version of this, 1 of them edited.

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The file

We split this skill into the rules it gives an agent

6 rules. About 0 are common, 3 are distinctive, and 3 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈3

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 3

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Setup: Run {SCRIPT} from repo root and parse JSON for FEATURE_SPEC, IMPL_PLAN, SPECS_DIR, BRANCH.

    line 34
  2. Very specific:

    Load context: Read FEATURE_SPEC and /memory/constitution.md.

    line 36
  3. Very specific:

    Execute plan workflow: Follow the structure in IMPL_PLAN template to:

    line 38
  4. Distinctive:

    Document the contract format appropriate for the project type

    line 86
  5. Distinctive:

    Update the appropriate agent-specific context file

    line 96
  6. Distinctive:

    Add only new technology from current plan

    line 97

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md
<!-- Generate implementation plan from a spec --><!-- AUTO-GENERATED — DO NOT EDIT --><!-- Source: .specify/templates/commands/plan.md --><!-- Regenerate: make generate-agent-files -->

description: Generate implementation plan from a spec handoffs:

  • label: Create Tasks agent: speckit.tasks prompt: Break the plan into tasks send: true
  • label: Create Checklist agent: speckit.checklist prompt: Create a checklist for the following domain... scripts: sh: scripts/bash/setup-plan.sh --json ps: scripts/powershell/setup-plan.ps1 -Json agent_scripts: sh: scripts/bash/update-agent-context.sh AGENT ps: scripts/powershell/update-agent-context.ps1 -AgentType AGENT

User Input

$ARGUMENTS

You MUST consider the user input before proceeding (if not empty).

Outline

  1. Setup: Run {SCRIPT} from repo root and parse JSON for FEATURE_SPEC, IMPL_PLAN, SPECS_DIR, BRANCH. For single quotes in args like "I'm Groot", use escape syntax: e.g 'I'''m Groot' (or double-quote if possible: "I'm Groot").
  2. Load context: Read FEATURE_SPEC and /memory/constitution.md. Load IMPL_PLAN template (already copied).
  3. Execute plan workflow: Follow the structure in IMPL_PLAN template to:
    • Fill Technical Context (mark unknowns as "NEEDS CLARIFICATION")
    • Fill Constitution Check section from constitution
    • Evaluate gates (ERROR if violations unjustified)
    • Phase 0: Generate research.md (resolve all NEEDS CLARIFICATION)
    • Phase 1: Generate data-model.md, contracts/, quickstart.md
    • Phase 1: Database migrations — When Storage is not N/A, generate db-migration.md (or e.g. mongodb-migration.md): required vs no-op, schema/index/backfill, rollback; link data-model.md. Stateless: omit file; note N/A in plan.md.
    • Phase 1: Update agent context by running the agent script
    • Re-evaluate Constitution Check post-design
  4. Stop and report: Command ends after Phase 2 planning. Report branch, IMPL_PLAN path, and generated artifacts.

Phases

Phase 0: Outline & Research

  1. Extract unknowns from Technical Context above:
    • For each NEEDS CLARIFICATION → research task
    • For each dependency → best practices task
    • For each integration → patterns task
  2. Generate and dispatch research agents:

    For each unknown in Technical Context:
      Task: "Research {unknown} for {feature context}"
    For each technology choice:
      Task: "Find best practices for {tech} in {domain}"
  3. Consolidate findings in research.md using format:
    • Decision: [what was chosen]
    • Rationale: [why chosen]
    • Alternatives considered: [what else evaluated]

Output: research.md with all NEEDS CLARIFICATION resolved

Phase 1: Design & Contracts

Prerequisites: research.md complete

  1. Extract entities from feature spec → data-model.md:
    • Entity name, fields, relationships
    • Validation rules from requirements
    • State transitions if applicable
  2. Define interface contracts (if project has external interfaces) → /contracts/:
    • Identify what interfaces the project exposes to users or other systems
    • Document the contract format appropriate for the project type
    • Examples: public APIs for libraries, command schemas for CLI tools, endpoints for web services, grammars for parsers, UI contracts for applications
    • Skip if project is purely internal (build scripts, one-off tools, etc.)
  3. Database migrations (when feature uses persisted storage):
    • Produce db-migration.md or project-specific name in the feature spec directory per plan template
  4. Agent context update:
    • Run {AGENT_SCRIPT}
    • These scripts detect which AI agent is in use
    • Update the appropriate agent-specific context file
    • Add only new technology from current plan
    • Preserve manual additions between markers

Output: data-model.md, /contracts/*, quickstart.md, db-migration.md when Storage ≠ N/A, agent-specific file

Key rules

  • Use absolute paths
  • ERROR on gate failures or unresolved clarifications

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