write-spec
Skill4 starsChanged 3 months agoMITView on GitHub
Convert clarify-scope discussion into a structured spec file at .ai/tasks/<name>.md. Run after clarify-scope resolves assumptions and scope. Captures decisions before they're lost in conversation history.
28 other projects use a version of this.
Adopted by
Most recently taken up by
- allemaar/open-skillstheir own version, first seen Nov 8, 2026
- nisarth/claude-skills-storetheir own version, first seen Nov 8, 2026
- dzhng/jevgreptheir own version, first seen Nov 7, 2026
- 0xdeafcafe/skillstheir own version, first seen Nov 6, 2026
- dzhng/skillstheir own version, first seen Nov 4, 2026
- anthod0/skillstheir own version, first seen Nov 2, 2026
Used with
Projects with it also use implement-spec-with-codex, close-spec and 10 more.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
dzhngSkillimplement-spec-with-codex
Run [implement-spec](../implement-spec/SKILL.md) with Codex doing the implementation passes while you orchestrate, integrate, and review. Use when the user asks to implement a spec with Codex, or to run implement-spec with Codex as the implementer.
Also in 4 projects that use this
dzhngSkillclose-spec
When a spec under specs/<feature>/ is done shipping (a write-spec build, or any planned task that produced a spec), archive it to specs/done/ and rewrite it from a build-plan into a durable rationale — the why, the principles, the invariants — pointing back to the real code for the how. Use when implementation has landed and the plan no longer matches what shipped, or the user says a feature/spec is finished. Pairs with [write-spec](../write-spec/SKILL.md) (the plan this closes) and [review](../review/SKILL.md) (run before closing).
Also in 4 projects that use this
wolverin0Skillaudit-decisions
Standardize remediation decisions, baseline-policy entries, and legal-review banners for accepted, deferred, or policy-blocked audit findings.
Also in 4 projects that use this
transitive-bullshitSkillcompare-screenshots
Compare screenshots against the intended design, distinguishing approved references from historical baselines. Use for iterative implementation matching, before/after visual review, objective image telemetry, or checking a lone capture for flat, empty, or badly framed content.
Also in 4 projects that use this
LucasMatuszewskiSkillaudit-agents
Audit and maintain agent configuration files (.claude/agents, AGENTS.md, CLAUDE.md, agent-memory). Checks for duplicates, outdated info, naming conflicts, wrong technical facts, and files over 200 lines. Fixes all issues and commits.
Also in 4 projects that use this
antonio-orionusSkillfind-state-machines
Report every implicit state machine in a project, with its states, transitions and flaws.
Also in 3 projects that use this
- +6 more from dzhng
Other versions28 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 28
- anthropics/knowledge-work-plugins★ 35ksame name
- foambubble/foam★ 17ksame name
- dzhng/jevgrep★ 3.2ksame name
- LiberatedPixelCup/Universal-LPC-Spritesheet-Character-Generator★ 1.9ksame name
- dzhng/skills★ 1.5ksame name
- me2resh/apexyard★ 542same name
- AlexPEClub/ai-coding-starter-kit★ 382same name
- dzhng/yap★ 112same name
- dzhng/duet-agent★ 55same name
- smixs/disruptor-skills★ 19same name
18 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository12 other files it gives its agents.
- CLAUDE.mdCLAUDE.md
- .github/copilot-instructions.mdCopilot instructions
- clarify-scopeSkill
- create-pr-descriptionSkill
- debugSkill
- implement-tddSkill
- plan-tasksSkill
- review-prSkill
- setup-ai-contextSkill
- shipSkill
- sync-ai-contextSkill
- write-test-scenariosSkill
The file
We split this skill into the rules it gives an agent
6 rules. 0 are common, 4 are distinctive, and 2 are very specific to this project.
0
Common
Most of the 27 edited versions we read keep these.
4
Distinctive
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 10
Run immediately after clarify-scope completes — don't let context drift away in chat history.
- Distinctive: line 55
Does not break anything currently working (regression)
- Distinctive: line 63
Before saving, ask yourself:
- Distinctive: line 72
If any checkbox is NO → fix before saving.
- Distinctive: line 76
After creating the spec:
- Very specific: line 77
Update .ai/status.md — add the task to "In Progress" with the start date
“Common” is measured: kept by at least 6 in 10 of the 27 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Write Spec
Convert the discussed context (assumptions, resolved questions, confirmed scope) into a structured spec file at .ai/tasks/<task-name>.md.
Run immediately after clarify-scope completes — don't let context drift away in chat history.
Run
Step 1 — Name the task
The name must be: short, kebab-case, describing a specific action.
# Good
add-password-reset
refactor-auth-middleware
fix-order-status-race-condition
# Bad
feature ← too generic
fix-bug ← doesn't say what bug
update-stuff ← meaninglessStep 2 — Fill each section from the discussion context
Open .ai/tasks/TEMPLATE.md as reference, create a new file .ai/tasks/<task-name>.md.
Goal — taken from the discussed objective. Must satisfy both:
- Specific: know exactly what needs to be done
- Measurable: know when it's "done"
Bad example: "Improve the auth system" Good example: "User can reset password via email, receives link within 60s, link expires after 1h"
Scope — list specific files/modules from confirmed assumptions:
- src/auth/password-reset.ts (create new)
- src/api/routes/auth.ts (add endpoint)
- src/email/templates/ (add template)Out of scope — taken from what was explicitly said NOT to do, or inferred from scope boundaries. This is the most important section for preventing scope creep later.
Test plan — minimum 3 cases:
- Happy path (main flow works correctly)
- Edge case (boundary input, concurrent request...)
- Does not break anything currently working (regression)
Constraints — from critical questions answered in clarify-scope. If the user said "backward-compatible" or "must not invalidate existing sessions" → record it here.
Open questions — if any questions remain unresolved after clarify-scope, record them here with a [BLOCKING] tag if they must be answered before coding.
Step 3 — Check spec quality
Before saving, ask yourself:
[ ] Goal: after reading it, do you immediately know what "done" looks like?
[ ] Out of scope: if someone wants to add X to scope, can you reject it using this file?
[ ] Test plan: is there at least 1 case for "does not break what's currently working"?
[ ] Constraints: have the critical questions from clarify-scope been captured?If any checkbox is NO → fix before saving.
Step 4 — Update status
After creating the spec:
- Update
.ai/status.md— add the task to "In Progress" with the start date - Confirm with user: "Spec created at
.ai/tasks/<task-name>.md. Proceed with implementation?"
When NOT to run this skill
- Task is small and clear (as assessed by clarify-scope) → no spec needed, proceed directly
- Spec already exists in
.ai/tasks/→ update that file instead of creating a new one
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

