# Spec

> Chains /mvp and /backend-spec to analyze an app from video, screenshots, or description, then generate implementation stories from the analysis.

- Skill: `tinh2/spec` (Agent Skill)
- Install (CLI): `npx skillmds@latest add tinh2/spec`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tinh2/spec/raw
- Safety review: CAUTION (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools, Product & Planning, API Design, PRDs & Specs
- Tags: Backend Spec, Jira, Mvp, Product Analysis, Spec, Story Generation
- Author: tinh2 (https://skillmd.com/u/tinh2)
- Updated: 2026-08-22
- Page: https://skillmd.com/skills/tinh2/spec

---


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  (/backend-spec)
============================================================

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

For each story:
- Use the feature context from the Phase 1 analysis as input
- Generate the full Jira-format spec (description, acceptance criteria, routes, dev notes, schemas)
- Prefix each story with BE: or FE: as appropriate

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


============================================================
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

============================================================
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
platforms:
- CLAUDE_CODE
---


============================================================
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:
```
### /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.

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 `/backend-spec` apply to their respective phases.

NEXT STEPS:

- "Run `/review-implement` to review and implement a story in one pass."
- "Run `/arch-review` to review a story's architecture before implementing."
- "Run `/iterate` to implement a story with autonomous refinement."