Borrowing it
Nothing to install: this file belongs to knitli/codeweaver. 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/knitli/codeweaver/main/.claude/commands/clarify.mdgit clone --depth 1 https://github.com/knitli/codeweaverWrote 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/knitli/codeweaver/clarify)<a href="https://agentmods.dev/commands/knitli/codeweaver/clarify"><img src="https://agentmods.dev/badge/commands/knitli/codeweaver/clarify/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/knitli/codeweaver/clarify"><img src="https://agentmods.dev/badge/commands/knitli/codeweaver/clarify.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 | $0.00000 | $0.02045 |
| Opus 5.5 | $0.00000 | $0.00818 |
| Sonnet 5.5 | $0.00000 | $0.00409 |
| Haiku 4.5 | $0.00000 | $0.00204 |
Grade A, and why
clarify 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 1mo 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
81% identical to speckit.clarify — 100 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 — 166 lines — stays where its author put it; the contents beside it link to each section on GitHub.
description: Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
The user input to you can be provided directly by the agent or as a command argument - you MUST consider it before proceeding with the prompt (if not empty).
User input:
$ARGUMENTS
Goal: Detect and reduce ambiguity or missing decision points in the active feature specification and record the clarifications directly in the spec file.
Note: This clarification workflow is expected to run (and be completed) BEFORE invoking /plan. If the user explicitly states they are skipping clarification (e.g., exploratory spike), you may proceed, but must warn that downstream rework risk increases.
Execution steps:
-
Run
.specify/scripts/bash/check-prerequisites.sh --json --paths-onlyfrom repo root once (combined--json --paths-onlymode /-Json -PathsOnly). Parse minimal JSON payload fields:FEATURE_DIRFEATURE_SPEC- (Optionally capture
IMPL_PLAN,TASKSfor future chained flows.) - If JSON parsing fails, abort and instruct user to re-run
/specifyor verify feature branch environment.
-
Load the current spec file. Perform a structured ambiguity & coverage scan using this taxonomy. For each category, mark status: Clear / Partial / Missing. Produce an internal coverage map used for prioritization (do not output raw map unless no questions will be asked).
Functional Scope & Behavior:
- Core user goals & success criteria
- Explicit out-of-scope declarations
- User roles / personas differentiation
Domain & Data Model:
- Entities, attributes, relationships
- Identity & uniqueness rules
- Lifecycle/state transitions
- Data volume / scale assumptions
Interaction & UX Flow:
- Critical user journeys / sequences
- Error/empty/loading states
- Accessibility or localization notes
Non-Functional Quality Attributes:
- Performance (latency, throughput targets)
- Scalability (horizontal/vertical, limits)
- Reliability & availability (uptime, recovery expectations)
- Observability (logging, metrics, tracing signals)
- Security & privacy (authN/Z, data protection, threat assumptions)
- Compliance / regulatory constraints (if any)
Integration & External Dependencies:
- External services/APIs and failure modes
- Data import/export formats
- Protocol/versioning assumptions
Edge Cases & Failure Handling:
- Negative scenarios
- Rate limiting / throttling
- Conflict resolution (e.g., concurrent edits)
Constraints & Tradeoffs:
- Technical constraints (language, storage, hosting)
- Explicit tradeoffs or rejected alternatives
Terminology & Consistency:
- Canonical glossary terms
- Avoided synonyms / deprecated terms
Completion Signals:
- Acceptance criteria testability
- Measurable Definition of Done style indicators
Misc / Placeholders:
- TODO markers / unresolved decisions
- Ambiguous adjectives ("robust", "intuitive") lacking quantification
For each category with Partial or Missing status, add a candidate question opportunity unless:
- Clarification would not materially change implementation or validation strategy
- Information is better deferred to planning phase (note internally)
-
Generate (internally) a prioritized queue of candidate clarification questions (maximum 5). Do NOT output them all at once. Apply these constraints:
- Maximum of 5 total questions across the whole session.
- Each question must be answerable with EITHER:
- A short multiple‑choice selection (2–5 distinct, mutually exclusive options), OR
- A one-word / short‑phrase answer (explicitly constrain: "Answer in <=5 words").
- Only include questions whose answers materially impact architecture, data modeling, task decomposition, test design, UX behavior, operational readiness, or compliance validation.
- Ensure category coverage balance: attempt to cover the highest impact unresolved categories first; avoid asking two low-impact questions when a single high-impact area (e.g., security posture) is unresolved.
- Exclude questions already answered, trivial stylistic preferences, or plan-level execution details (unless blocking correctness).
- Favor clarifications that reduce downstream rework risk or prevent misaligned acceptance tests.
- If more than 5 categories remain unresolved, select the top 5 by (Impact * Uncertainty) heuristic.
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.
- 1mo ago First seen · 166 lines · 0 tokens per session scan A b2ef10a23077
clarify is a command published in the GitHub repository knitli/codeweaver (12 stars, last pushed 4mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 2,045 tokens. A static security scan graded it A with 0 findings. It is 81% identical to speckit.clarify, differing in 100 lines, and is treated as a copy.
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