quality-gate-size-analysis
Analyze static quality gate on-disk size changes, correlate with Confluence exception records and GitHub PRs by milestone
52
57%
Does it follow best practices?
Impact
—
No eval scenarios have been run
Advisory
Suggest reviewing before use
Optimize this skill with Tessl
npx tessl skill review --optimize ./.claude/skills/quality-gate-size-analysis/SKILL.mdQuality
Discovery
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 is highly specific and distinctive, naming concrete actions and a unique combination of tools (quality gates, Confluence, GitHub PRs). However, it critically lacks a 'Use when...' clause, making it unclear when Claude should select this skill. The trigger terms are domain-specific jargon that may not match how users naturally phrase requests.
Suggestions
Add an explicit 'Use when...' clause, e.g., 'Use when the user asks about binary size regressions, quality gate failures related to size, or needs to cross-reference size changes with Confluence exceptions or GitHub milestones.'
Include natural language trigger variations users might say, such as 'size regression', 'build size analysis', 'binary bloat', 'size budget', or 'artifact size tracking'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: analyzing static quality gate on-disk size changes, correlating with Confluence exception records, and correlating with GitHub PRs by milestone. These are distinct, concrete operations. | 3 / 3 |
Completeness | Describes what the skill does but completely lacks a 'Use when...' clause or any explicit trigger guidance for when Claude should select this skill. Per the rubric, a missing 'Use when...' clause caps completeness at 2, and since the 'when' is entirely absent, this scores a 1. | 1 / 3 |
Trigger Term Quality | Contains some relevant domain-specific keywords like 'quality gate', 'on-disk size', 'Confluence', 'GitHub PRs', 'milestone', but these are fairly technical terms. Missing common user-facing variations or simpler trigger phrases a user might naturally say (e.g., 'size regression', 'binary size', 'build size tracking'). | 2 / 3 |
Distinctiveness Conflict Risk | Highly specific niche combining static quality gates, on-disk size changes, Confluence exception records, and GitHub PRs by milestone. This very particular combination of tools and concerns makes it extremely unlikely to conflict with other skills. | 3 / 3 |
Total | 9 / 12 Passed |
Implementation
64%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a solid, domain-specific skill with excellent actionability — concrete metric names, CQL queries, CLI commands, and specific pitfalls that would be impossible for Claude to know without instruction. Its main weaknesses are the lack of validation checkpoints in a multi-step workflow involving multiple external data sources, and the monolithic structure that could benefit from splitting detailed reference material into supporting files.
Suggestions
Add explicit validation checkpoints after each data-fetching step (e.g., 'Verify N exception pages returned before proceeding' or 'Confirm metric data covers the expected time range')
Add a feedback loop for the correlation step — e.g., 'If a spike has no matching PR within ±2 days, widen the search window or check for batch merges'
Consider extracting the report template (Step 5) and the pitfalls section into separate reference files to improve progressive disclosure
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is reasonably efficient and provides domain-specific details Claude wouldn't know (metric names, Confluence folder IDs, tag naming conventions). However, some sections are slightly verbose — e.g., the introductory sentence restates the title, and some steps could be tightened. The pitfalls section is genuinely valuable and earns its tokens. | 2 / 3 |
Actionability | The skill provides concrete, executable queries (Datadog metric queries, CQL search syntax, exact gh CLI commands with JSON output fields), specific field names to extract from Confluence pages, and precise tag/filter values. The commands are copy-paste ready with clear placeholders. | 3 / 3 |
Workflow Clarity | The 5-step workflow is clearly sequenced and logically ordered (collect metrics → fetch exceptions → pull PRs → correlate → report). However, there are no explicit validation checkpoints or feedback loops — e.g., no step to verify Datadog query results before proceeding, no validation that Confluence pages were parsed correctly, no error handling for missing milestones or failed API calls. | 2 / 3 |
Progressive Disclosure | The content is well-structured with clear headers and sub-steps, but it's a monolithic document with no references to supporting files. The detailed Confluence extraction instructions, GitHub search patterns, and report template could be split into separate reference files. For a skill of this length (~120 lines of substantive content), some progressive disclosure would improve navigability. | 2 / 3 |
Total | 9 / 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/datadog-agent
- Commit
a41685a
- Reviewed
Table of Contents
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