mvp-spec

Chains /mvp → /spec — analyzes an app from video/screenshots/description, then generates implementation stories. Triggers: analyze and spec, product analysis to stories, app teardown to specs, full an

SKILL.md

GitHub

instructions: | You are an autonomous analysis-to-spec agent. Do NOT ask the user questions. Run the full pipeline below without pausing between phases.

INPUT: $ARGUMENTS

The user will provide one or more of:

  1. A video file or screen recording of an application.
  2. Screenshots of an application.
  3. A URL or description of the application.
  4. Any combination of the above.

============================================================ PHASE 1: PRODUCT ANALYSIS (/mvp)

Follow the instructions defined in the /mvp skill exactly. Produce all sections of the /mvp output (Application Overview, Feature Inventory, MVP Definition, Architecture Assessment, UX/Design Analysis, Improvements, Story Candidates, Summary).

Store the full output — you will use the Story Candidates and Feature Inventory in Phase 2.

Do NOT stop here. Continue immediately to Phase 2.

============================================================ PHASE 2: STORY GENERATION (/spec)

Take every Story Candidate identified in Phase 1 and generate a full engineering spec for each one by following the /spec skill instructions exactly.

For each story:

  • Use the feature context from the Phase 1 analysis as input
  • The /spec skill will auto-detect whether each story is full-stack, BE-only, or FE-only
  • Full-stack stories will produce both a BE and FE story with linked routes and schemas

Order stories by implementation dependency — foundational stories (auth, models, core APIs) first, then features that build on them.

============================================================ OUTPUT

When both phases are complete, print a summary:


Spec Complete

Product: [app name / description] Stories generated: [N] (BE: [N], FE: [N])

Implementation order:

  1. [Story title] — [why first]
  2. [Story title] — [why next]
  3. ...

Next steps: - Run /arch-review [story] to review a story before implementing - Run /review-implement [story] to review and implement in one pass - Run /iterate [story] to implement with autonomous refinement

STRICT RULES:

  • Do NOT skip Phase 1 and jump to story generation.
  • Do NOT ask the user for input between phases.
  • Every story in Phase 2 must trace back to a feature or story candidate from Phase 1.
  • All rules from /mvp and /spec apply to their respective phases.

============================================================ SELF-HEALING VALIDATION (max 3 iterations)

After completing all phases, validate the combined output:

  1. Re-run the specific checks that originally found issues to confirm fixes.
  2. Run the project's test suite to verify fixes didn't introduce regressions.
  3. Run build/compile to confirm no breakage.
  4. If new issues surfaced from fixes, add them to the fix queue.
  5. Repeat the fix-validate cycle up to 3 iterations total.

STOP when:

  • Zero Critical/High issues remain
  • Build and tests pass
  • No new issues introduced by fixes

IF STILL FAILING after 3 iterations:

  • Document remaining issues with full context
  • Classify as requiring manual intervention or architectural changes

============================================================ SELF-EVOLUTION TELEMETRY

After producing output, record execution metadata for the /evolve pipeline.

Check if a project memory directory exists:

  • Look for the project path in ~/.claude/projects/
  • If found, append to skill-telemetry.md in that memory directory

Entry format:

### /mvp-spec — {{YYYY-MM-DD}}
- Outcome: {{SUCCESS | PARTIAL | FAILED}}
- Self-healed: {{yes — what was healed | no}}
- Iterations used: {{N}} / {{N max}}
- Bottleneck: {{phase that struggled or "none"}}
- Suggestion: {{one-line improvement idea for /evolve, or "none"}}

Only log if the memory directory exists. Skip silently if not found. Keep entries concise — /evolve will parse these for skill improvement signals.