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speckit-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.

53

Quality

61%

Does it follow best practices?

Impact

No eval scenarios have been run

SecuritybySnyk

Passed

No known issues

Fix and improve this skill with Tessl

tessl review fix ./.claude/skills/speckit-clarify/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

55%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

This skill is highly actionable and has excellent workflow clarity with well-defined sequential steps, validation checkpoints, and error recovery paths. However, it suffers significantly from verbosity—the hook-checking logic is duplicated nearly verbatim for pre/post execution, the taxonomy is exhaustively listed, and many behavioral rules could be compressed. The monolithic structure with no progressive disclosure makes it token-expensive and hard to navigate.

Suggestions

Extract the hook-checking logic (repeated for pre and post execution) into a shared reference file like `HOOKS.md` and reference it twice with a single line each, saving ~40 lines of duplication.

Move the ambiguity taxonomy categories into a separate `TAXONOMY.md` reference file, keeping only a brief summary in the main skill body (e.g., '11 categories covering functional scope, data model, UX, NFRs, integrations, edge cases, constraints, terminology, completion signals, and placeholders—see TAXONOMY.md').

Compress the questioning loop mechanics: Claude can infer standard interaction patterns from a concise description rather than having every sub-case (multiple-choice vs short-answer, recommendation formatting, acceptance keywords) spelled out in full detail.

Add clear section navigation headers or a brief table of contents at the top to help locate the Pre-Execution, Outline steps, and Post-Execution sections quickly.

DimensionReasoningScore

Conciseness

The skill is extremely verbose at ~200+ lines with extensive procedural detail that could be significantly compressed. Many sections over-explain processes (e.g., the full hook checking logic is repeated nearly identically for pre and post execution, the taxonomy list is exhaustive when Claude could infer most categories, and the questioning loop mechanics are spelled out in excessive detail).

1 / 3

Actionability

The skill provides highly concrete, executable guidance: specific shell commands (`check-prerequisites.sh --json --paths-only`), exact markdown formatting for questions and tables, precise file paths, explicit answer acceptance patterns ('yes', 'recommended', 'suggested'), and detailed integration rules for where to place clarifications in the spec. Every step is copy-paste actionable.

3 / 3

Workflow Clarity

The 8-step workflow is clearly sequenced with explicit validation checkpoints after each write (step 6), error recovery paths (disambiguation retries, early termination signals), and a final validation pass. The sequential questioning loop has clear entry/exit conditions, and the incremental save-after-each-answer approach provides robust feedback loops for this multi-step interactive process.

3 / 3

Progressive Disclosure

The entire skill is a monolithic wall of text with no references to external files despite the complexity warranting it. The hook-checking logic (repeated twice), the taxonomy list, and the questioning format rules could all be extracted to separate reference files. No bundle files are provided to support decomposition, and the content is not structured for easy navigation.

1 / 3

Total

8

/

12

Passed

Description

67%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is strong in specificity and distinctiveness, clearly articulating a well-defined workflow of identifying gaps in feature specs, asking clarification questions, and updating the spec. Its main weaknesses are the lack of an explicit 'Use when...' clause and limited trigger term coverage that would help Claude match this skill to natural user requests about spec refinement or requirements clarification.

Suggestions

Add a 'Use when...' clause with explicit triggers, e.g., 'Use when the user asks to review, refine, or clarify a feature spec, PRD, or requirements document.'

Include common user-facing synonyms and variations such as 'requirements', 'PRD', 'product spec', 'spec review', 'ambiguous requirements', or 'refine specification' to improve trigger term coverage.

DimensionReasoningScore

Specificity

Lists multiple specific concrete actions: 'identify underspecified areas', 'asking up to 5 highly targeted clarification questions', and 'encoding answers back into the spec'. These are clear, concrete actions with specific constraints (up to 5 questions).

3 / 3

Completeness

Clearly answers 'what does this do' (identify underspecified areas, ask clarification questions, encode answers into spec), but lacks an explicit 'Use when...' clause or equivalent trigger guidance, which caps this at 2 per the rubric guidelines.

2 / 3

Trigger Term Quality

Contains relevant terms like 'feature spec', 'clarification questions', and 'underspecified', but misses common user variations like 'requirements', 'PRD', 'spec review', 'ambiguous requirements', or 'refine spec'. Users might not naturally say 'underspecified areas'.

2 / 3

Distinctiveness Conflict Risk

The combination of feature spec refinement through targeted clarification questions and encoding answers back into the spec is a very specific niche. It's unlikely to conflict with general coding, documentation, or other skills due to its narrow focus on spec clarification workflows.

3 / 3

Total

10

/

12

Passed

Validation

81%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation9 / 11 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_version

'metadata.version' is missing

Warning

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

9

/

11

Passed

Repository
mixpanel/mixpanel-headless
Reviewed

Table of Contents

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