lading-optimize-review

Reviews optimization patches using a 5-persona peer review system. Requires unanimous approval backed by benchmarks.

Quality

67%

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Passed

No known issues

Fix and improve this skill with Tessl

tessl review fix ./.claude/skills/lading-optimize-review/SKILL.md

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 content is highly actionable with concrete commands, thresholds, and a well-sequenced multi-phase workflow backed by real template files. Its only weakness is minor redundancy (a duplicated outcomes table and lightly padded Kani notes).

Suggestions

Remove the duplicated Outcomes table (keep it only in Phase 4: Decision) to save tokens.

Trim the Kani-constraints prose to a one-line note plus the fallback rule, since the tradeoffs are largely inferable.

Consider moving the per-persona checklists into a references file to keep SKILL.md as a lean overview.

Dimension	Reasoning	Score
Conciseness	The body is mostly efficient (tables, checklists, code blocks) but the Outcomes table is duplicated in Phase 1 and Phase 4 and the Kani-constraints prose restates context Claude can infer, so it could be tightened.	2 / 3
Actionability	It gives fully executable commands (cargo criterion, hyperfine --runs 30, ci/kani), explicit numeric thresholds, and copy-paste-ready template paths, matching the executable-guidance anchor.	3 / 3
Workflow Clarity	Phases 1-5 are clearly sequenced with explicit validation checkpoints and reject feedback loops (missing baseline -> REJECT, duplicate -> REJECT, bug -> REJECT), satisfying the checklist-with-validation anchor.	3 / 3
Progressive Disclosure	Detailed report templates are split into real one-level-deep bundle files (assets/approved.template.yaml, assets/rejected.template.yaml) referenced via a clearly signaled table, and the body stays an organized overview.	3 / 3
	Total	11 / 12 Passed

Description

50%

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 conveys the skill's purpose and rigor but reads as jargon-heavy and lacks an explicit use-when trigger. It is distinguishable but not sharply differentiated from related review skills.

Suggestions

Add a 'Use when...' clause naming natural user triggers (e.g., 'Use when reviewing an optimization patch for lading, validating benchmark gains, or approving a performance change').

Replace jargon like '5-persona peer review system' and 'unanimous approval' with user-facing terms ('five-way expert review', 'all reviewers must approve').

Include the lading/Rust domain in the description to reduce overlap with generic review skills.

Dimension	Reasoning	Score
Specificity	Names the domain and a couple of concrete requirements ('Reviews optimization patches', 'Requires unanimous approval backed by benchmarks') but does not enumerate multiple specific actions, falling short of the comprehensive-action anchor.	2 / 3
Completeness	It clearly states what the skill does but provides no 'Use when...' clause or equivalent trigger guidance, so the 'when' is only implied and completeness is capped at 2.	2 / 3
Trigger Term Quality	Terms like 'optimization patches', '5-persona peer review system', and 'unanimous approval' are relevant but skew technical, missing common natural phrasings a user would actually say.	2 / 3
Distinctiveness Conflict Risk	It occupies a niche (benchmark-backed patch review) but omits the lading/Rust context and could overlap with sibling review skills, so it is only somewhat distinct.	2 / 3
	Total	8 / 12 Passed

Validation

93%

Warnings & errors only

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

Validation — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: DataDog/lading
Commit: 5761294

Reviewed: 8 days ago

Table of Contents

Discovery Implementation Validation

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