markdown-token-optimizer

Analyzes markdown files for token efficiency. TRIGGERS: optimize markdown, reduce tokens, token count, token bloat, too many tokens, make concise, shrink file, file too large, optimize for AI, token efficiency, verbose markdown, reduce file size

1.37x

Quality

86%

Does it follow best practices?

Impact

84%

1.37x

Average score across 3 eval scenarios

Securityby

Passed

No findings from the security scan

Quality

Content

82%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 a concise, well-structured overview with a clear four-step workflow and properly split one-level-deep references. It could improve by adding an example output table and removing the duplicate reference listing.

Suggestions

Add a small example of the suggested output table (location, issue, fix, savings) so the Suggest step is copy-paste concrete rather than described.

Remove the duplicate reference listing: signal ANTI-PATTERNS.md and OPTIMIZATION-PATTERNS.md once, either inline in the Workflow or in the References section, not both.

Add an explicit verification cue in the Workflow (e.g. confirm suggestions preserve meaning before presenting) to add a validation checkpoint.

Dimension	Reasoning	Score
Conciseness	The body is lean and assumes Claude's competence (e.g. 'Calculate tokens (~4 chars = 1 token)', 'Table with location, issue, fix, savings estimate') with no padding or explanation of concepts Claude already knows, matching the lean-and-efficient anchor.	5 / 5
Actionability	Workflow steps give concrete specifics (token approximation, output table schema, summary fields), but no executable command or example output table is provided; guidance is mostly actionable with a minor gap, fitting the 'mostly executable, minor gaps' anchor.	4 / 5
Workflow Clarity	The four steps (Count, Scan, Suggest, Summary) are clearly sequenced and the skill is explicitly suggest-only/non-destructive so the destructive-cap does not apply, but there is no explicit verification that suggestions preserve meaning, leaving a minor validation gap at level 4.	4 / 5
Progressive Disclosure	Structure is clean with one-level-deep references to real files (ANTI-PATTERNS.md, OPTIMIZATION-PATTERNS.md) confirmed present in references/, but the same two references are signalled both inline in the Workflow section and again in a dedicated References section, a minor organization redundancy that keeps it at 4 rather than 5.	4 / 5
	Total	17 / 20 Passed

Description

90%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 lean, third-person, and answers both what and when with an extensive natural trigger list. Its only weakness is that the capability statement is a single action rather than an enumerated set of concrete operations.

Dimension	Reasoning	Score
Specificity	The lead clause 'Analyzes markdown files for token efficiency' names the domain and one concrete action (analyze), but the description does not enumerate multiple distinct capabilities, fitting the '1-2 concrete actions, not comprehensive' anchor rather than the multi-action level 4/5.	3 / 5
Completeness	It explicitly answers 'what' ('Analyzes markdown files for token efficiency') and 'when' via a concrete TRIGGERS clause, in third person, satisfying the anchor for clearly and explicitly answering both what and when with concrete trigger phrases.	5 / 5
Trigger Term Quality	The TRIGGERS list covers comprehensive natural phrasings users would actually say ('optimize markdown', 'reduce tokens', 'too many tokens', 'make concise', 'shrink file', 'file too large', 'verbose markdown', 'reduce file size') including synonyms, matching the comprehensive-coverage anchor.	5 / 5
Distinctiveness Conflict Risk	Markdown token optimization is a clear niche with distinct, specific triggers ('token bloat', 'optimize for AI', 'verbose markdown') unlikely to fire for unrelated skills, matching the clear-niche/minimal-conflict anchor.	5 / 5
	Total	18 / 20 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 — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: microsoft/github-copilot-for-azure
Path: .github/skills/markdown-token-optimizer/SKILL.md
Commit: a3d0496

Reviewed: about 12 hours ago

Table of Contents

Discovery Implementation Validation

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