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codex-review

OpenAI Codex CLI code review with GPT-5.2-Codex, CI/CD integration

52

Quality

65%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

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tessl review fix ./skills/codex-review/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

63%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.

The body is exceptionally actionable and well-sequenced, with concrete executable commands and configurations throughout. Its main weaknesses are verbosity — large CI/CD pipeline and config examples inlined in the main file — and the absence of any progressive disclosure via reference files.

Suggestions

Move the full GitHub Actions, GitLab, and Jenkins pipeline examples into a references/ci-cd.md file and keep only one canonical example inline with a clearly signaled link.

Trim or relocate the shell-completions section, the Claude-vs-Codex comparison table, and the per-project TOML examples to reference files to cut the main body substantially.

Add an explicit validation step after running a review (e.g., check that the JSON findings conform to the schema before posting to CI) to strengthen workflow feedback loops.

DimensionReasoningScore

Conciseness

The ~500-line body inlines three complete CI pipeline configs (GitHub Actions, GitLab, Jenkins), shell completion setup, full TOML config examples, and a Claude-vs-Codex comparison table — several padded sections that belong in reference files — plus time-sensitive version pins (Node 22, gpt-5.2-codex), matching anchor 2 (noticeably verbose) rather than anchor 3's 'mostly efficient'.

2 / 5

Actionability

Everything is copy-paste ready: exact install and auth commands, complete codex exec invocations with flags, a working JSON output schema, full CI workflow YAMLs, and a concrete troubleshooting table — fully executable and covering the common cases, matching anchor 5.

5 / 5

Workflow Clarity

Prerequisites → install → verify (codex --version) → auth → usage is clearly sequenced, with troubleshooting and security sections providing error-recovery guidance; minor validation gaps remain (no checkpoint on review output itself), fitting anchor 4 rather than anchor 5's explicit feedback loops.

4 / 5

Progressive Disclosure

Section structure and navigation within SKILL.md are good, but all content is inlined with no reference files at all — the CI pipeline examples, config reference, and comparison table clearly belong in separate one-level-deep references — matching anchor 3 ('content that should be separate is inline') rather than anchor 2, since the in-body structure is not minimal.

3 / 5

Total

14

/

20

Passed

Description

53%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.

The description is accurate and clearly scoped to a specific tool and model, but it reads more like a tagline than a trigger-optimized skill description. It lacks an explicit 'Use when...' clause and natural trigger phrases, capping both completeness and trigger-term quality at the midpoint.

Suggestions

Add an explicit trigger clause to the description, e.g. "Use when the user asks for Codex-powered review, a second-opinion review, or review of a PR/diff in CI".

Include natural user phrasings such as "review my PR", "review the diff", or "security review" alongside "code review" to improve trigger-term coverage.

Mention one or two more concrete capabilities (e.g., structured JSON findings output, GitHub @codex review comments) to lift specificity from 1-2 actions toward comprehensive coverage.

DimensionReasoningScore

Specificity

"OpenAI Codex CLI code review with GPT-5.2-Codex, CI/CD integration" names the domain and two concrete facets (specialized model, CI/CD integration), but omits capabilities the body documents such as JSON schema output, GitHub @codex review, and headless mode — matching anchor 3 (domain plus 1-2 concrete actions, not comprehensive) rather than anchor 4's 'several specific actions'.

3 / 5

Completeness

The 'what' is explicit (Codex CLI code review with a named model and CI/CD integration), but the description itself contains no 'Use when...' clause or equivalent trigger guidance, which per the rubric caps completeness at 3; it is not 2 because the 'what' is clear rather than vague.

3 / 5

Trigger Term Quality

"code review", "Codex", and "CI/CD" are natural terms a user might say, but common variations like "review my PR", "review the diff", or "security review" are missing, fitting anchor 3 (some relevant keywords, missing common variations) rather than anchor 4's good-but-gapped coverage.

3 / 5

Distinctiveness Conflict Risk

"OpenAI Codex CLI" and "GPT-5.2-Codex" carve a distinct niche with minimal conflict risk, but a generic "review my code" request could plausibly trigger this skill when a built-in review skill is intended (the body itself includes a Claude-vs-Codex comparison), matching anchor 4 (mostly distinct, minor overlap) rather than anchor 5's minimal conflict risk.

4 / 5

Total

13

/

20

Passed

Validation

87%

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

Validation — 14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (511 lines); consider splitting into references/ and linking

Warning

frontmatter_unknown_keys

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

Warning

Total

14

/

16

Passed

Repository

alinaqi/maggy

Path

skills/codex-review/SKILL.md

Reviewed

Table of Contents

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