lading-optimize-find-target
Finds a valid optimization target in lading. Returns a filled target.yaml template with pattern, technique, target, file, bench, and fingerprint. Use before /lading-optimize-hunt or when selecting a new optimization target.
84
81%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Quality
Discovery
85%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 is a well-structured skill description that clearly defines what it does and when to use it, with strong specificity about the output format and workflow positioning. Its main weakness is that the trigger terms are heavily domain-specific jargon, which could make it harder for users unfamiliar with the exact terminology to naturally trigger this skill. Overall it is a solid description for a niche, specialized skill.
Suggestions
Add more natural language trigger terms that users might say, such as 'find what to optimize', 'select performance target', or 'identify optimization opportunity in lading'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: finding a valid optimization target, returning a filled target.yaml template, and specifies the fields included (pattern, technique, target, file, bench, fingerprint). | 3 / 3 |
Completeness | Clearly answers both what ('Finds a valid optimization target in lading, returns a filled target.yaml template with specific fields') and when ('Use before /lading-optimize-hunt or when selecting a new optimization target') with explicit trigger guidance. | 3 / 3 |
Trigger Term Quality | Includes some relevant terms like 'optimization target', 'lading', 'target.yaml', but these are domain-specific jargon. Missing more natural user phrases like 'find bottleneck', 'performance target', or 'what to optimize'. Users may not naturally say 'optimization target in lading'. | 2 / 3 |
Distinctiveness Conflict Risk | Highly specific to the 'lading' domain with distinct triggers like 'target.yaml', 'lading-optimize-hunt', and the specific template fields. Very unlikely to conflict with other skills. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
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-crafted, highly actionable skill with excellent workflow clarity across seven distinct phases. The content provides concrete paths, commands, a comprehensive pattern reference table, and a precise output format. The main weakness is that the document is fairly long and could benefit from splitting the pattern table or detailed procedures into referenced files for better progressive disclosure.
Suggestions
Consider extracting the Known Patterns table into a separate referenced asset file (e.g., `assets/known-patterns.md`) to reduce the main skill's token footprint and improve progressive disclosure.
Tighten Phase 3 prose — the explanation of what to extract from history entries could be condensed into a bullet list without the explanatory framing sentences.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is mostly efficient and avoids explaining basic concepts, but the Known Patterns table and the detailed multi-phase structure are somewhat verbose. The pattern table earns its place as a reference, but some prose (e.g., Phase 3 explanation of what to extract from history) could be tightened. | 2 / 3 |
Actionability | Highly actionable throughout: concrete glob paths, specific script commands, exact file locations, a complete pattern table with named patterns and techniques, explicit procedures for each phase, and a precise YAML output template. Claude knows exactly what to do at every step. | 3 / 3 |
Workflow Clarity | Seven clearly sequenced phases with explicit validation/filtering steps (Phase 5 filters, Phase 6 ranks with defined criteria and tiebreakers). The 'must follow exactly' procedure in Phase 4 includes a mandatory exhaustive cross-product check. Phase 5 provides a clear stop condition. The workflow handles the full pipeline from discovery to output. | 3 / 3 |
Progressive Disclosure | The skill references external files (db.yaml, profile-modules script, fingerprint configs) appropriately, but the body itself is a long monolithic document (~100+ lines of dense content). The Known Patterns table and detailed phase descriptions could potentially be split into referenced assets. However, for a single skill file with no bundle, the section-based organization is reasonable. | 2 / 3 |
Total | 10 / 12 Passed |
Validation
90%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 10 / 11 Passed | |
- Repository
- DataDog/lading
- Commit
01ef8d7
- Reviewed
Table of Contents
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