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improve-agent-native

Check if a repository or agent-facing product surface is ready for AI coding agents. Use when you need to audit repo agent compatibility, review AGENTS.md, find missing test/build commands, evaluate docs quality, assess tool/action parity, or produce a file-evidence scorecard with specific fixes.

Quality

86%

Does it follow best practices?

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SKILL.md
Quality
Evals
Security

Quality

Content

73%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.

A well-structured, highly actionable runbook with an exemplary reference-routing scheme and strong validation feedback loops. Its main weakness is redundancy — the untrusted-content and evidence-lane policies are repeated across multiple sections, which inflates token cost without adding guidance value.

Suggestions

Consolidate the untrusted-content/safety-boundary policy into one canonical section — it is currently stated in Outputs, Failure Mode, and Validation with near-identical wording.

Move the edge-case output policies (PR closeout lanes, proof_gap rules, safety_boundary literal classifications) into a reference file and keep only a one-line pointer in Outputs.

Replace indirect meta-instructions like "Conclude the assessment by saying that validation blocks false readiness" with direct statements of what the output must contain, cutting phrasing overhead.

DimensionReasoningScore

Conciseness

The body avoids basic-concept padding and uses terse imperative prose, but the untrusted-content/safety-boundary policy is stated four times (Outputs twice, Failure Mode, and Validation), and local-vs-hosted/PR-closeout reporting is repeated across Outputs and Execution Boundaries. This is more than the minor trimming of anchor 4, though not the padded verbosity of anchor 2.

3 / 5

Actionability

Provides a concrete YAML output schema, copy-pasteable validation commands (e.g., "./bin/ask skills audit Skills/agent-ops/improve-agent-native --level strict --json --robot"), and per-task reference routing. Minor gaps remain: the 0-100 score computation is fully delegated to a reference with no in-body summary, and some workflow steps stay at judgment level ("2-3 surfaces that directly affect the requested decision").

4 / 5

Workflow Clarity

The 8-step workflow is clearly sequenced with explicit validation checkpoints and feedback loops ("preserve the failed command shape and the final passing command", "allow one corrected invocation", "A genuine target validation failure blocks the affected claim"), plus a dedicated Failure Mode section for error recovery. This matches the top anchor's validate-fix-retry structure.

5 / 5

Progressive Disclosure

References are one level deep, well-signaled with explicit load conditions ("Load references/harness-readiness-rubric.md when scoring or benchmarking"), and all cited files exist in the bundle. However, the body is a full ~210-line runbook rather than an overview: edge-case output policy (safety-boundary classifications, PR closeout, proof-gap rules) inlined in Outputs is content that could live in a reference file, keeping it below anchor 5.

4 / 5

Total

16

/

20

Passed

Description

92%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: it explicitly states what the skill does and when to use it, with a comprehensive list of concrete audit actions and natural trigger terms. The only weakness is slightly thin synonym coverage in the trigger phrases.

DimensionReasoningScore

Specificity

The description lists multiple specific concrete actions — "audit repo agent compatibility, review AGENTS.md, find missing test/build commands, evaluate docs quality, assess tool/action parity, or produce a file-evidence scorecard with specific fixes" — giving comprehensive coverage of the skill's capabilities. Anchor 4 ('minor gaps in coverage') does not fit since the action list spans the skill's full scope.

5 / 5

Completeness

Clearly answers both 'what' ("Check if a repository or agent-facing product surface is ready for AI coding agents... produce a file-evidence scorecard with specific fixes") and 'when' with an explicit "Use when you need to..." clause listing concrete triggers. Both halves are explicit, matching the top anchor exactly.

5 / 5

Trigger Term Quality

Good keyword coverage with natural phrases users would say ("audit repo agent compatibility", "AGENTS.md", "test/build commands", "docs quality", "scorecard"), but a few common variations are missing from the description itself (e.g., "AI-ready", "agent-friendly", or product names like Claude/Copilot/Codex/MCP, which only appear in the body). Anchor 5 requires comprehensive synonyms, which this does not quite reach.

4 / 5

Distinctiveness Conflict Risk

Clear niche (agent-native readiness auditing) with distinct triggers ("AGENTS.md", "tool/action parity", "agent compatibility") and minimal conflict risk; even the broadly-worded "evaluate docs quality" trigger is scoped by the agent-readiness framing. Not score 4 because no closely related skill would plausibly capture these triggers.

5 / 5

Total

19

/

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

jscraik/Agent-Skills

Path

Skills/agent-ops/improve-agent-native/SKILL.md

Reviewed

Table of Contents

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