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ai-coding-interview

Guide realistic AI-assisted coding interview rehearsals, timed mocks, and evidence-based scoring. Use for live backend AI coding interview practice that needs interviewer, coach, and coding-agent roles; do not use for ordinary feature implementation.

Quality

92%

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SKILL.md
Quality
Evals
Security

Quality

Content

92%Weight 40%Scale 1-5

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

An excellent process-oriented skill body: lean directive prose, verbatim templates and prompts, a fully sequenced workflow with explicit RED/GREEN validation checkpoints and time-boxed recovery rules, and clean progressive disclosure into three real, conditionally-loaded reference files. The only minor lever is trimming a few redundant rule statements to tighten token usage.

DimensionReasoningScore

Conciseness

The body is dense and directive with essentially no explanation of concepts Claude already knows — every section issues instructions ("Reject giant passive prompts", "Keep at most one slice in progress"). It is not a 5 because a ~190-line body for a multi-phase process retains a few statements that restate or could be tightened (e.g., plan-rendering rules appear across two sections, and the persistence paragraph mixes several rules).

4 / 5

Actionability

For an instruction-only skill the guidance is fully executable: a literal copy-paste plan template with slice IDs and statuses ("1. [IN PROGRESS] S1 - Contract and minimal end-to-end behavior"), verbatim bounded-direction prompts ("Implement only the RED test for S2. Run it and stop before production code."), a minute-by-minute 45-minute clock, a 7-step validation checklist, and named scoring dimensions with a 1-5 scale and N/A rule. No code is expected (instruction skill), and the specific examples cover the common cases, matching anchor 5.

5 / 5

Workflow Clarity

The multi-step process is clearly sequenced (mode selection → role boundaries → contract → visible plan → directed AI → RED/GREEN cycles → clock → production discussion → validation/scoring) with explicit validation checkpoints and feedback loops: "Run it and confirm RED for the expected reason", "Confirm GREEN, update the visible plan, and stop", mid-exercise checkpoints ("If the minimal path is not green by minute 22, cut optional scope immediately"), and re-render triggers for the plan. Anchor 5's explicit validation steps and error-recovery checkpoints are met; anchor 4's 'minor validation gaps' does not apply.

5 / 5

Progressive Disclosure

The body is an appropriately sized overview and all three bundle files exist and are exactly one level deep with no nested references. Each is clearly signaled with a conditional load condition: "read [references/candidate-led-pairing-prompt.md]" when preparing the initial prompt, "Read [references/productionization-walkthrough.md] when coaching or evaluating this follow-up", and "use [references/eval-plan.md]" for maintenance/benchmarking. This matches anchor 5's well-signaled, one-level-deep structure with easy navigation.

5 / 5

Total

19

/

20

Passed

Description

87%Weight 40%Scale 1-5

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

A strong description that states concrete capabilities, gives an explicit and specific 'Use for' trigger with a well-chosen negative trigger, and carves out a distinct niche. The only improvement space is broader synonym coverage in trigger terms.

DimensionReasoningScore

Specificity

Names the domain and several concrete actions — "Guide realistic AI-assisted coding interview rehearsals, timed mocks, and evidence-based scoring" — each a distinct, verifiable capability. It stops short of anchor 5 because only three actions are listed with no coverage of sub-activities (e.g., scorecards, exercise selection), a minor gap.

4 / 5

Completeness

Explicitly answers both: what ("Guide realistic AI-assisted coding interview rehearsals, timed mocks, and evidence-based scoring") and when ("Use for live backend AI coding interview practice that needs interviewer, coach, and coding-agent roles"), plus a concrete negative trigger ("do not use for ordinary feature implementation"). This matches anchor 5's requirement for concrete trigger phrases on both sides; anchor 4's 'when could be more explicit' does not apply since the when-clause names the exact context and required roles.

5 / 5

Trigger Term Quality

Natural phrases users would say are present: "coding interview", "timed mocks", "interview practice", "rehearsals", "backend". A few common variations are missing (e.g., "mock interview", "interview prep", "technical interview"), keeping it below anchor 5's comprehensive synonym coverage.

4 / 5

Distinctiveness Conflict Risk

The niche is sharply delimited — AI-assisted interview practice requiring three named roles — and the exclusion clause "do not use for ordinary feature implementation" prevents overlap with general coding skills. Minimal conflict risk, matching anchor 5.

5 / 5

Total

18

/

20

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.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

saski/arnesto

Path

.agents/skills/ai-coding-interview/SKILL.md

Reviewed

Table of Contents

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