Borrowing it
Nothing to install: this file belongs to NotMyself/claude-win11-speckit-update-skill. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/NotMyself/claude-win11-speckit-update-skill/main/.claude/commands/speckit.plan.mdgit clone --depth 1 https://github.com/NotMyself/claude-win11-speckit-update-skillWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/notmyself/claude-win11-speckit-update-skill/speckit.plan)<a href="https://agentmods.dev/commands/notmyself/claude-win11-speckit-update-skill/speckit.plan"><img src="https://agentmods.dev/badge/commands/notmyself/claude-win11-speckit-update-skill/speckit.plan/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/notmyself/claude-win11-speckit-update-skill/speckit.plan"><img src="https://agentmods.dev/badge/commands/notmyself/claude-win11-speckit-update-skill/speckit.plan.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00014 | $0.00683 |
| Opus 5 | $0.00007 | $0.00342 |
| Sonnet 5 | $0.00003 | $0.00137 |
| Haiku 4.5 | $0.00001 | $0.00068 |
Grade A, and why
speckit.plan scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
89% identical to speckit.plan — 15 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
User Input
$ARGUMENTS
You MUST consider the user input before proceeding (if not empty).
Outline
-
Setup: Run
.specify/scripts/powershell/setup-plan.ps1 -Jsonfrom 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"). -
Load context: Read FEATURE_SPEC and
.specify/memory/constitution.md. Load IMPL_PLAN template (already copied). -
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: Update agent context by running the agent script
- Re-evaluate Constitution Check post-design
-
Stop and report: Command ends after Phase 2 planning. Report branch, IMPL_PLAN path, and generated artifacts.
Phases
Phase 0: Outline & Research
-
Extract unknowns from Technical Context above:
- For each NEEDS CLARIFICATION → research task
- For each dependency → best practices task
- For each integration → patterns task
-
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}" -
Consolidate findings in
research.mdusing 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
- Extract entities from feature spec →
data-model.md:- Entity name, fields, relationships
- Validation rules from requirements
- State transitions if applicable
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 81 lines · 14 tokens per session scan A d2e8f51636fc
speckit.plan is a command published in the GitHub repository NotMyself/claude-win11-speckit-update-skill (30 stars, last pushed 10mo ago), licensed MIT. It adds 14 tokens to every session and 683 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to speckit.plan, differing in 15 lines, and is treated as a copy.
Other commands, from other repositories
trex.project.init
Validate that this TRex-spawned project is in a healthy, working state. This command is idempotent — it succeeds if everything is already running and correct, and only fails if something is genuinely broken.
trex.kind.add-field
Add a new field to an existing Kind, updating all layers consistently.
trex.kind.new
Generate a complete CRUD entity (Kind) using the automated generator script.
trex.test.integration
Run integration tests against a real PostgreSQL database (provisioned automatically via testcontainers).
trex.db.setup
Start a fresh PostgreSQL container and run all migrations. If a database already exists, this will tear it down first.
trex.verify
Run all static analysis checks: go vet, format verification, and linting.