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codex-skill

Leverage OpenAI Codex/GPT models for autonomous code implementation, code review, and plan review. Triggers: "codex", "use gpt", "gpt-5", "let openai", "full-auto", "adversarial review", "second opinion review", "用codex", "让gpt实现", "对抗式审查", "让codex审查计划", "第二意见". Use this skill whenever the user wants to delegate coding tasks to OpenAI models, run code or plan reviews via codex, get a second-opinion review from a different model, or execute tasks in a sandboxed environment.

74

Quality

92%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

85%

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

The body is highly actionable with real codex commands and flags, well-sequenced workflows with validation checkpoints and error-recovery feedback loops, and clean one-level-deep progressive disclosure to four verified reference files. Its main weakness is conciseness: a generic Best Practices section and repeated danger-full-access warnings add tokens that don't earn their place.

Suggestions

Trim or remove the 'Best Practices' section (Speed/Scope/Quality) — items like 'Make reasonable assumptions', 'Follow existing code patterns and conventions', and 'Run relevant tests after making changes' are things Claude already knows and add no codex-specific value.

Consolidate the danger-full-access guidance into one place (e.g., the Operating Modes entry) and reference it from the other sections instead of restating the full warning in Security & Trust Boundaries, Core Principles, and When to Interrupt.

Replace the generic five-step Execution Workflow (Parse/Plan/Execute/Verify/Report) with codex-specific steps, or fold it into the existing mode-selection and long-running guidance to avoid restating generic project hygiene.

DimensionReasoningScore

Conciseness

The body is operationally dense and avoids explaining basics Claude already knows, but the generic 'Best Practices' section ('Make reasonable assumptions', 'Follow existing code patterns', 'Run relevant tests') restates knowledge Claude already has, and the danger-full-access warning is repeated across Security, Core Principles, Operating Modes, and When to Interrupt — so it is mostly efficient but could be tightened rather than fully lean.

2 / 3

Actionability

Commands are concrete and copy-paste ready — 'codex exec --full-auto "..."', 'codex exec review --uncommitted', 'codex exec -i mockup.png --full-auto "..."', 'codex exec resume --last "..."' — with specific flags and gotchas like '< /dev/null' for non-TTY hangs, matching the fully-executable anchor rather than the pseudocode anchor at 2.

3 / 3

Workflow Clarity

Sub-workflows are clearly sequenced with explicit checkpoints and feedback loops: the numbered Error Handling cases (auth failure → stop and report; unparseable JSON → show raw + error; non-blocking → continue and report), the review-handling STOP-before-editing gate, and the scope-estimation decision for long runs — matching the clear-sequence-with-validation anchor rather than the sequence-with-gaps anchor at 2.

3 / 3

Progressive Disclosure

A 'Reference Files' section signals four one-level-deep references with 'Read this when…' triggers (cli-reference.md, prompting-patterns.md, review-workflows.md, examples.md), all of which exist on disk alongside assets/review-output.schema.json, matching the clear-overview-with-well-signaled-references anchor rather than the inline-could-be-split anchor at 2.

3 / 3

Total

11

/

12

Passed

Description

100%

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 specific, complete, and distinctive: it states concrete capabilities, provides an explicit Triggers list with natural and multilingual terms, and includes a clear 'Use this skill whenever...' clause answering both what and when. Voice is third-person with no first/second-person penalty.

DimensionReasoningScore

Specificity

Names the domain ('OpenAI Codex/GPT models') and lists multiple concrete actions — 'autonomous code implementation, code review, and plan review' plus 'second-opinion review' and 'execute tasks in a sandboxed environment' — matching the anchor for multiple specific concrete actions rather than the partial domain-only anchor below.

3 / 3

Completeness

Clearly answers both what ('Leverage OpenAI Codex/GPT models for autonomous code implementation, code review, and plan review') and when ('Use this skill whenever the user wants to delegate coding tasks...') with explicit triggers, matching the what-AND-when anchor rather than the what-only anchor at 2.

3 / 3

Trigger Term Quality

An explicit Triggers list covers natural phrases a user would say ('codex', 'use gpt', 'gpt-5', 'second opinion review', 'adversarial review') with multilingual variants, giving good coverage rather than just 'some relevant keywords'.

3 / 3

Distinctiveness Conflict Risk

It carves a clear niche (delegating to OpenAI/Codex models for a second-opinion or sandboxed execution) with distinct triggers unlikely to fire for unrelated skills, rather than the generic 'could overlap' anchor at 2.

3 / 3

Total

12

/

12

Passed

Validation

93%

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

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

allowed_tools_field

'allowed-tools' contains unusual tool name(s)

Warning

Total

15

/

16

Passed

Repository
feiskyer/claude-code-settings
Reviewed

Table of Contents

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