clarify

clarify is a command for Claude Code from geq1fan/context-mcp. It costs 27 tokens per session (2,000 once invoked), scanned A, a copy of speckit.clarify, MIT.

A command that finds unclear or missing decisions in a feature specification by asking up to five focused questions and recording the answers in the specification.

In plain words
What is it for?
Use it before creating an implementation plan when the feature requirements are incomplete or open to interpretation.
Why use it?
It reduces ambiguity before planning and implementation, which can prevent avoidable rework.

Command for Claude Code

Install

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.

agentmods
npx agentmods add commands/geq1fan/context-mcp/clarify
Clone the repo
git clone --depth 1 https://github.com/geq1fan/context-mcp

Made for: Claude Code.

Wrote 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.

agentmods badge for clarify

README.md
[![agentmods](https://agentmods.dev/badge/commands/geq1fan/context-mcp/clarify.svg)](https://agentmods.dev/commands/geq1fan/context-mcp/clarify)
Your own site
<a href="https://agentmods.dev/commands/geq1fan/context-mcp/clarify"><img src="https://agentmods.dev/badge/commands/geq1fan/context-mcp/clarify.svg" alt="Measured on agentmods" height="20"></a>
Per session 27 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 2,000 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 84% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00027 $0.02000
Opus 5 $0.00014 $0.01000
Sonnet 5 $0.00005 $0.00400
Haiku 4.5 $0.00003 $0.00200

Measured 4d ago against content hash bfd6d5329d20, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 4d 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.

Origin

This is a copy

84% identical to speckit.clarify — 95 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.

.claude/commands/clarify.md · 159 lines

How it starts

The opening of the file, as written. The whole thing — 159 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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:

  1. Run .specify/scripts/powershell/check-prerequisites.ps1 -Json -PathsOnly from repo root once (combined --json --paths-only mode / -Json -PathsOnly). Parse minimal JSON payload fields:

    • FEATURE_DIR
    • FEATURE_SPEC
    • (Optionally capture IMPL_PLAN, TASKS for future chained flows.)
    • If JSON parsing fails, abort and instruct user to re-run /specify or verify feature branch environment.
  2. 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)
  3. 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.

Read the full file on GitHub · 159 lines

Changes

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.

  1. 4d ago First seen · 159 lines · 27 tokens per session scan A bfd6d5329d20

Subscribe to this mod's changes

clarify is a command published in the GitHub repository geq1fan/context-mcp (5 stars, last pushed 10mo ago), licensed MIT. It adds 27 tokens to every session and 2,000 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 84% identical to speckit.clarify, differing in 95 lines, and is treated as a copy.