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
Install with Tessl CLI
npx tessl i github:microsoft/github-copilot-for-azure --skill markdown-token-optimizer83
Does it follow best practices?
If you maintain this skill, you can automatically optimize it using the tessl CLI to improve its score:
npx tessl skill review --optimize ./path/to/skillValidation for skill structure
Discovery
72%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 excels at trigger term coverage with natural user phrases but falls short on specificity of actions (only 'analyzes' is mentioned). The TRIGGERS format is unconventional and while effective for keyword matching, it lacks an explicit 'Use when...' clause that would clarify selection criteria for Claude.
Suggestions
Add specific concrete actions beyond 'analyzes' - e.g., 'Identifies verbose sections, removes redundant formatting, suggests concise alternatives, and reports token savings'
Convert the TRIGGERS list into a proper 'Use when...' clause - e.g., 'Use when users want to optimize markdown for token efficiency, reduce file size, or make content more concise for AI consumption'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (markdown files, token efficiency) and one action (analyzes), but lacks specific concrete actions like 'removes redundant formatting', 'compresses headers', or 'identifies verbose sections'. | 2 / 3 |
Completeness | The 'what' is present but weak (just 'analyzes'). The TRIGGERS list implies 'when' but there's no explicit 'Use when...' clause. Per rubric guidelines, missing explicit trigger guidance caps completeness at 2. | 2 / 3 |
Trigger Term Quality | Excellent coverage of natural terms users would say: 'optimize markdown', 'reduce tokens', 'token count', 'too many tokens', 'make concise', 'file too large'. These are realistic phrases users would naturally use when needing this skill. | 3 / 3 |
Distinctiveness Conflict Risk | Clear niche combining markdown + token optimization. The specific trigger terms like 'token bloat', 'token efficiency', 'optimize for AI' create a distinct profile unlikely to conflict with general markdown editing or generic file optimization skills. | 3 / 3 |
Total | 10 / 12 Passed |
Implementation
85%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, concise skill that demonstrates excellent token efficiency - practicing what it preaches. The workflow is clear and the progressive disclosure is exemplary. The main weakness is the lack of concrete examples showing what the output table or summary should look like, which would make the skill more immediately actionable.
Suggestions
Add a brief example showing the expected output format for the suggestions table (e.g., '| Line 5 | Emoji decoration | Remove 🎯 | ~2 tokens |')
Include a sample summary output format to clarify what 'Current/potential/savings' looks like in practice
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely lean content with no unnecessary explanation. Every section is minimal yet complete - no padding, no explaining what tokens are or why efficiency matters. | 3 / 3 |
Actionability | Provides a clear workflow structure but lacks concrete examples of output format, specific commands to run, or sample analysis. The 4-step process is described but not demonstrated with executable guidance. | 2 / 3 |
Workflow Clarity | Clear 4-step sequence (Count → Scan → Suggest → Summary) with explicit outputs for each step. The workflow is simple enough that validation checkpoints aren't critical, and the 'suggest only' rule provides appropriate guardrails. | 3 / 3 |
Progressive Disclosure | Excellent structure with concise overview and well-signaled one-level-deep references to ANTI-PATTERNS.md and OPTIMIZATION-PATTERNS.md. Content is appropriately split between overview and detailed reference files. | 3 / 3 |
Total | 11 / 12 Passed |
Validation
100%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.
- Reviewed
Table of Contents
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