Speckit validator skill
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Validate a Spec-Driven Development (SpecKit) project end-to-end. Runs a deterministic rule engine (file structure, spec/plan/tasks schema, cross-references, env security, code quality, CI config) and produces a scored, graded report — optionally enriched with AI analysis. Use whenever someone asks to "validate", "audit", "health-check", or "lint" a SpecKit / SDD project, or to verify spec ↔ code alignment.
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SKILL.md
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SpecKit Validator Skill
A portable, config-driven validation skill for any Spec-Driven Development project. It combines a deterministic Python engine (objective, repeatable pass/fail checks) with an optional AI analysis pass (qualitative review of spec quality, spec↔code alignment, and long-term health).
When to use this skill
Trigger this skill when the user wants to:
- Validate / audit / health-check a SpecKit or SDD project
- Verify all SDD artefacts exist and are well-formed (spec, plan, tasks, checklist, constitution)
- Check that the specification's requirements are actually implemented in code
- Confirm no secrets are committed and CI gates are present
- Generate a quality/health report for a project or PR
How it works
┌──────────────────────────────────────────────────────────────┐
│ 1. Deterministic Engine → python -m speckit_validator │
│ 8 check groups, ~40–70 checks, weighted 0–100 score │
├──────────────────────────────────────────────────────────────┤
│ 2. AI Analysis (optional) → read artefacts, score 5 lenses │
│ spec quality · spec↔code · constitution · copilot · health │
├──────────────────────────────────────────────────────────────┤
│ 3. Report → Markdown saved to │
│ .specify/validation-report.md │
└──────────────────────────────────────────────────────────────┘
Procedure (follow in order)
Step 1 — Run the deterministic engine
From the project root, run and capture both formats:
python -m speckit_validator --root . --format json
python -m speckit_validator --root .
Parse the JSON: score, grade, is_healthy, p1_failures, checks[].
If Python is unavailable, perform the checks manually using docs/checks-reference.md
as the rule catalogue and mark each finding [MANUAL].
Step 2 — Read the SpecKit artefacts
Read (note any that are missing):
.specify/specification.md, .specify/plan.md, .specify/tasks.md,
.specify/checklist.md, .specify/memory/constitution.md,
.github/copilot-instructions.md, and the primary source + test files.
Step 3 — AI analysis (5 lenses)
- Specification Quality — clarity, completeness, measurability (score 1–5 per dimension)
- Spec ↔ Code Alignment — table mapping every FR-xxx to its implementation (file:line) or ❌
- Constitution Compliance — sample naming, provider abstraction, test strategy, secrets
- Copilot Chat Readiness — is
copilot-instructions.mdaccurate and complete? (score 1–5) - Long-Term Health — spec-drift risk, test fragility, biggest technical debt
Step 4 — Compose and save the report
Merge deterministic results + AI findings into one Markdown report with an
Executive Summary and a Prioritised Action Plan (P1 blocking / P2
recommended / P3 optional). Save it to the report_path from config
(default .specify/validation-report.md) using the editFiles tool.
Rules
- Every claim must cite a file, a check name, or a direct quote. No fabrication.
- P1 deterministic failures are always blocking — never downgrade them.
- The FR alignment table must cover every FR, not a sample.
- If a file is missing, say so explicitly rather than omitting the section.
Configuration
Drop a speckit-validator.config.json at the project root to tailor the rules
to your stack (see speckit-validator.config.example.json and
docs/customization.md). With no config, generic SpecKit defaults apply.
Entry points
| Context | How to invoke |
|---|---|
| CLI / terminal | python -m speckit_validator --root . |
| Make | make validate (after install) |
| GitHub Copilot Chat | attach prompts/validate-speckit.prompt.md or #validate-speckit |
| Claude / Claude Code | reference this SKILL.md |
| Shell wrapper | scripts/validate.sh / scripts/validate.ps1 |