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interview-me

Extracts what the user actually wants instead of what they think they should want. Achieves this through one-question-at-a-time interview until ~95% confidence about the underlying intent. Use when an ask is underspecified ("build me X" without "for whom" or "why now"), when the user explicitly invokes ("interview me", "grill me", "are we sure?", "stress-test my thinking"), or when you catch yourself silently filling in ambiguous requirements before any plan, spec, or code exists.

72

Quality

88%

Does it follow best practices?

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SecuritybySnyk

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The canonical home for this skill is interview-me in addyosmani/agent-skills

SKILL.md
Quality
Evals
Security

Quality

Content

77%Scale 1-3

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

This is a strong, well-crafted skill that provides highly actionable guidance for intent extraction through a structured interview process. Its greatest strengths are the concrete formats (hypothesis + confidence, Q + guess, 6-field restate), the testable stop condition, and the realistic before/after example. Its main weakness is verbosity — the explanatory sections (why one at a time, why attach a guess, common rationalizations, interaction with other skills) add significant token cost and could be trimmed or moved to reference files.

Suggestions

Move the 'Common Rationalizations' table and 'Interaction with Other Skills' section to a separate reference file to reduce token cost in the main skill body.

Trim the 'Why one at a time' and 'Why attach a guess' explanations to 1-2 lines each — Claude can infer the reasoning from the format requirements and examples.

DimensionReasoningScore

Conciseness

The skill is well-written and most content earns its place, but it's verbose in places — the 'Common Rationalizations' table, the extended 'Why one at a time' and 'Why attach a guess' explanations, and the 'Interaction with Other Skills' section add significant token cost. Some of this (e.g., explaining why batching is bad) is arguably teaching Claude things it could infer. However, the core process steps are tight and the example is genuinely useful.

2 / 3

Actionability

The skill provides highly concrete, executable guidance: exact formats for hypotheses, questions with guesses, restates with a specific 6-field structure, explicit stop conditions with a testable criterion ('can I predict reactions to the next three questions?'), and concrete examples of what counts as confirmation vs. not. The before/after example demonstrates the process with realistic dialogue.

3 / 3

Workflow Clarity

The 5-step process is clearly sequenced with explicit validation checkpoints: the 95% confidence stop condition is a testable gate, Step 5 has explicit criteria for what counts as confirmation vs. not, and there are feedback loops (fold corrections, restate, loop until explicit yes). The red flags and verification checklist provide additional guardrails. The process handles error recovery (e.g., 'three rounds without confidence rising → step back').

3 / 3

Progressive Disclosure

The content is well-structured with clear sections and headers, but it's a monolithic document with no references to external files. At ~300+ lines, some content (Common Rationalizations table, Interaction with Other Skills, the extended example) could be split into separate reference files. The skill mentions saving to 'docs/intent/[topic].md' but has no bundle files. For a skill of this length, the lack of any content splitting is a minor weakness.

2 / 3

Total

10

/

12

Passed

Description

100%Scale 1-3

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

This is a strong skill description that clearly articulates a specific capability (intent extraction through iterative interviewing), provides rich natural trigger terms users would actually say, and explicitly delineates three categories of when to activate. The description is concise yet comprehensive, with a well-defined niche that distinguishes it from adjacent skills like planning or code generation.

DimensionReasoningScore

Specificity

Lists multiple concrete actions: extracting underlying intent, conducting one-question-at-a-time interviews, reaching ~95% confidence threshold, and catching silently filled-in ambiguous requirements. These are specific, actionable behaviors.

3 / 3

Completeness

Clearly answers both 'what' (extracts underlying intent through iterative questioning) and 'when' (explicit 'Use when' clause covering three distinct trigger scenarios: underspecified asks, explicit user invocations, and catching ambiguous requirements).

3 / 3

Trigger Term Quality

Includes excellent natural trigger terms users would actually say: 'interview me', 'grill me', 'are we sure?', 'stress-test my thinking', 'build me X'. Also covers the implicit trigger of underspecified asks, which is a natural scenario description.

3 / 3

Distinctiveness Conflict Risk

This skill occupies a clear niche as a requirements elicitation/intent clarification tool, distinct from planning, coding, or specification skills. The specific trigger phrases ('interview me', 'grill me', 'stress-test my thinking') and the focus on pre-plan/pre-code questioning make it unlikely to conflict with other skills.

3 / 3

Total

12

/

12

Passed

Validation

100%

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

Validation11 / 11 Passed

Validation for skill structure

No warnings or errors.

Repository
finlay611/agent-skills
Reviewed

Table of Contents

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