lading-optimize-hunt

Coordinates optimization attempts. Captures baselines, implements changes, invokes review, and records outcomes.

1.37x

Quality

43%

Does it follow best practices?

Impact

88%

1.37x

Average score across 3 eval scenarios

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./.claude/skills/lading-optimize-hunt/SKILL.md

Quality

Content

77%

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

This is a well-structured coordination skill with strong actionability and workflow clarity. The phased approach with explicit validation checkpoints, cleanup steps, and clear handoff points makes it reliable for complex optimization workflows. Minor improvements could be made in conciseness by trimming the role clarification section, and progressive disclosure could benefit from verifiable bundle references.

Suggestions

Trim the 'Role: Coordinator and Recorder' section — the 'Hunt DOES / Hunt does NOT' lists largely restate what the phases already make clear, saving ~6 lines of context window.

Dimension	Reasoning	Score
Conciseness	The skill is mostly efficient but has some redundancy — the 'Hunt DOES / Hunt does NOT' section repeats information that's implicit in the workflow phases. The role clarification section could be tightened. However, the workflow phases themselves are lean and well-structured.	2 / 3
Actionability	Provides fully executable bash commands, specific file paths, concrete variable assignments, and exact command-line invocations. Each phase has copy-paste ready commands with clear parameter substitution patterns.	3 / 3
Workflow Clarity	Clear 6-phase sequential workflow with explicit validation checkpoints (ci/validate must pass before proceeding), cleanup of stale data before baselining, critical warnings about ordering, and clear error handling guidance (pre-existing bug documentation). The feedback loop for ci/validate failure is explicit.	3 / 3
Progressive Disclosure	References external slash commands (/lading-preflight, /lading-optimize-find-target, /lading-optimize-review) and template files appropriately, but the bundle has no files provided to verify these references. The skill itself is somewhat long and could potentially split the baseline capture details into a referenced file, though the inline approach is defensible for a workflow document.	2 / 3
	Total	10 / 12 Passed

Description

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

This description is too abstract and process-oriented, reading more like a workflow description than a skill description. It fails to specify what domain of optimization it covers, uses no natural trigger terms a user would say, and lacks any 'Use when...' guidance for skill selection.

Suggestions

Specify the domain of optimization (e.g., 'code performance optimization', 'database query optimization', 'build time optimization') to reduce ambiguity and conflict risk.

Add a 'Use when...' clause with natural trigger terms users would say, such as 'Use when the user asks to speed up code, improve performance, reduce latency, or optimize resource usage.'

Replace abstract process terms ('coordinates', 'invokes review', 'records outcomes') with concrete actions describing what the skill actually does, e.g., 'profiles code execution, identifies bottlenecks, refactors hot paths, and benchmarks improvements.'

Dimension	Reasoning	Score
Specificity	The description names some actions ('captures baselines', 'implements changes', 'invokes review', 'records outcomes') but they are generic process steps rather than concrete, domain-specific actions. It's unclear what kind of optimization or what specific operations are performed.	2 / 3
Completeness	While there is a vague 'what' (coordinates optimization attempts), there is no 'when' clause or explicit trigger guidance. The description never specifies when Claude should select this skill, which per the rubric caps completeness at 2, and the 'what' is also weak enough to warrant a 1.	1 / 3
Trigger Term Quality	The description lacks natural keywords a user would say. Terms like 'coordinates optimization attempts' and 'invokes review' are abstract process language, not terms users would naturally use when requesting help. There are no concrete trigger terms like 'performance', 'speed', 'benchmark', 'profiling', etc.	1 / 3
Distinctiveness Conflict Risk	The term 'optimization' is extremely broad and could apply to code optimization, database optimization, image optimization, performance tuning, SEO, or countless other domains. Without specifying the domain or type of optimization, this would easily conflict with many other skills.	1 / 3
	Total	5 / 12 Passed

Validation

100%

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 — 11 / 11 Passed

Validation for skill structure

No warnings or errors.

Repository: DataDog/lading
Commit: e98e305

Reviewed: 21 days ago

Table of Contents

Discovery Implementation Validation

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