Borrowing it
Nothing to install: this file belongs to alfredoperez/speckit-companion. 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/alfredoperez/speckit-companion/main/.codex/prompts/speckit.clarify.mdgit clone --depth 1 https://github.com/alfredoperez/speckit-companionWrote 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/alfredoperez/speckit-companion/speckit.clarify)<a href="https://agentmods.dev/commands/alfredoperez/speckit-companion/speckit.clarify"><img src="https://agentmods.dev/badge/commands/alfredoperez/speckit-companion/speckit.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/alfredoperez/speckit-companion/speckit.clarify"><img src="https://agentmods.dev/badge/commands/alfredoperez/speckit-companion/speckit.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 | $0.00027 | $0.02315 |
| Opus 5 | $0.00014 | $0.01157 |
| Sonnet 5 | $0.00005 | $0.00463 |
| Haiku 4.5 | $0.00003 | $0.00231 |
Grade A, and why
speckit.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 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
100% identical to speckit.clarify — 0 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 — 182 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
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 /speckit.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
/speckit.specifyor verify feature branch environment. - 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 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)
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 · 182 lines · 27 tokens per session scan A 61c0ad7aa1ae
speckit.clarify is a command published in the GitHub repository alfredoperez/speckit-companion (90 stars, last pushed yesterday), licensed MIT. It adds 27 tokens to every session and 2,315 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to speckit.clarify, differing in 0 lines, and is treated as a copy.
Other commands, from other repositories
migrate-to-typescript
Migrate JavaScript project to TypeScript.
svelte:component
Create new Svelte components with best practices, proper structure, and optional TypeScript support.
autospec.plan
Generate YAML implementation plan from feature specification.
code-locate
Given a behavior description, locate candidate code paths and line ranges in the active codebase that probably implement it. Output up to 10 candidates with HIGH/MEDIUM/LOW confidence, one-line rationale per candidate, and an explicit search trail; propose a SOURCEOFTRUTH.md update so the next workflow step (typically…
harvest-session-learnings
Scan the current working session and the active task's artifacts for reusable, generalizable lessons (what was tried, what failed and why, what surprised us, what the next task should do differently) and propose anchored entries to append to the task's LEARNINGS.md, the produce-side counterpart to the ADR-0017 consume…
screen-spec
Generate a screen specification from a Figma frame using MCP tools (getdesigncontext, getscreenshot). Produces a spec doc matching the SCREENSPEC.md template with layout sketch, components used, spacing observed, data dependencies, copy, accessibility notes, interactions, and error states. Use when documenting a…