Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add commands/microsoft/agent365-dotnet/speckit.checklistgit clone --depth 1 https://github.com/microsoft/Agent365-dotnetWhat 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 | $0.00013 | $0.03735 |
| Opus 5 | $0.00006 | $0.01868 |
| Sonnet 5 | $0.00003 | $0.00747 |
| Haiku 4.5 | $0.00001 | $0.00374 |
Grade A, and why
speckit.checklist 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 2d 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
88% identical to checklist — 107 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 — 295 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Checklist Purpose: "Unit Tests for English"
CRITICAL CONCEPT: Checklists are UNIT TESTS FOR REQUIREMENTS WRITING - they validate the quality, clarity, and completeness of requirements in a given domain.
NOT for verification/testing:
- ❌ NOT "Verify the button clicks correctly"
- ❌ NOT "Test error handling works"
- ❌ NOT "Confirm the API returns 200"
- ❌ NOT checking if code/implementation matches the spec
FOR requirements quality validation:
- ✅ "Are visual hierarchy requirements defined for all card types?" (completeness)
- ✅ "Is 'prominent display' quantified with specific sizing/positioning?" (clarity)
- ✅ "Are hover state requirements consistent across all interactive elements?" (consistency)
- ✅ "Are accessibility requirements defined for keyboard navigation?" (coverage)
- ✅ "Does the spec define what happens when logo image fails to load?" (edge cases)
Metaphor: If your spec is code written in English, the checklist is its unit test suite. You're testing whether the requirements are well-written, complete, unambiguous, and ready for implementation - NOT whether the implementation works.
User Input
$ARGUMENTS
You MUST consider the user input before proceeding (if not empty).
Execution Steps
-
Setup: Run
.specify/scripts/powershell/check-prerequisites.ps1 -Jsonfrom repo root and parse JSON for FEATURE_DIR and AVAILABLE_DOCS list.- All file paths must be absolute.
- 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").
-
Clarify intent (dynamic): Derive up to THREE initial contextual clarifying questions (no pre-baked catalog). They MUST:
- Be generated from the user's phrasing + extracted signals from spec/plan/tasks
- Only ask about information that materially changes checklist content
- Be skipped individually if already unambiguous in
$ARGUMENTS - Prefer precision over breadth
Generation algorithm:
- Extract signals: feature domain keywords (e.g., auth, latency, UX, API), risk indicators ("critical", "must", "compliance"), stakeholder hints ("QA", "review", "security team"), and explicit deliverables ("a11y", "rollback", "contracts").
- Cluster signals into candidate focus areas (max 4) ranked by relevance.
- Identify probable audience & timing (author, reviewer, QA, release) if not explicit.
- Detect missing dimensions: scope breadth, depth/rigor, risk emphasis, exclusion boundaries, measurable acceptance criteria.
- Formulate questions chosen from these archetypes:
- Scope refinement (e.g., "Should this include integration touchpoints with X and Y or stay limited to local module correctness?")
- Risk prioritization (e.g., "Which of these potential risk areas should receive mandatory gating checks?")
- Depth calibration (e.g., "Is this a lightweight pre-commit sanity list or a formal release gate?")
- Audience framing (e.g., "Will this be used by the author only or peers during PR review?")
- Boundary exclusion (e.g., "Should we explicitly exclude performance tuning items this round?")
- Scenario class gap (e.g., "No recovery flows detected—are rollback / partial failure paths in scope?")
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.
- 2d ago First seen · 295 lines · 13 tokens per session scan A b06b595abdd1
speckit.checklist is a command published in the GitHub repository microsoft/Agent365-dotnet (29 stars, last pushed 1mo ago), licensed MIT. It adds 13 tokens to every session and 3,735 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to checklist, differing in 107 lines, and is treated as a copy.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
constitution
Create or update the project constitution from interactive or provided principle inputs.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.