meta-cognition-parallel
EXPERIMENTAL: Three-layer parallel meta-cognition analysis. Triggers on: /meta-parallel, 三层分析, parallel analysis, 并行元认知
69
62%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./skills/meta-cognition-parallel/SKILL.mdQuality
Discovery
47%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 relies heavily on jargon and command-based triggers without explaining what the skill actually does. While it has clear distinctiveness through unique terminology and explicit trigger commands, users unfamiliar with 'meta-cognition analysis' would have no idea what capabilities this skill provides or when to use it naturally.
Suggestions
Replace abstract terminology with concrete actions: specify what 'three-layer parallel meta-cognition analysis' actually produces (e.g., 'Analyzes problems from three perspectives simultaneously: logical, emotional, and strategic').
Add natural language trigger scenarios beyond slash commands: include a 'Use when...' clause describing situations like 'when user wants multi-perspective analysis' or 'when exploring complex decisions'.
Explain the output or benefit: what does the user get from this analysis? Add specifics like 'generates structured comparison of viewpoints' or 'produces decision matrix'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description uses vague, abstract language ('Three-layer parallel meta-cognition analysis') without explaining what concrete actions the skill performs. No specific capabilities like 'analyzes X', 'generates Y', or 'processes Z' are listed. | 1 / 3 |
Completeness | The 'when' is partially addressed via 'Triggers on:' with specific commands, but the 'what' is extremely weak - 'meta-cognition analysis' doesn't explain what the skill actually does or produces. Missing explicit 'Use when...' guidance for natural language scenarios. | 2 / 3 |
Trigger Term Quality | Includes explicit trigger terms ('/meta-parallel', '三层分析', 'parallel analysis', '并行元认知') but these are technical jargon rather than natural phrases users would typically say. The bilingual coverage is good but terms are specialized. | 2 / 3 |
Distinctiveness Conflict Risk | The highly specific trigger commands ('/meta-parallel') and unique terminology ('Three-layer parallel meta-cognition') make this unlikely to conflict with other skills. The niche is clearly defined even if poorly explained. | 3 / 3 |
Total | 8 / 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-structured experimental skill with excellent workflow clarity and actionability - the dual-mode execution (agent vs inline) is clearly documented with specific steps and output templates. The main weakness is length; the detailed layer analysis templates and output formats make the skill verbose, though the content itself is useful rather than redundant explanation.
Suggestions
Extract the detailed layer analysis templates (Steps 2-4 in inline mode) to separate reference files like `layer-templates.md` to reduce main skill length
Consider consolidating the three nearly-identical layer output templates into a single parameterized template with layer-specific focus areas listed separately
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is moderately efficient but includes some redundancy - the inline mode templates repeat similar structures across layers, and the concept diagram, while helpful, adds tokens. The test scenarios and error handling sections are appropriately concise. | 2 / 3 |
Actionability | Provides fully concrete guidance with specific file paths to check, exact Task() syntax for agent launching, complete markdown templates for each layer's output, and executable code pattern examples in the final output template. | 3 / 3 |
Workflow Clarity | Excellent multi-step workflow with clear sequencing (Steps 1-5 for inline mode, Steps 1-4 for agent mode), explicit mode detection checkpoint at the start, and clear synthesis step that combines all layer results. The dual-mode execution paths are well-documented. | 3 / 3 |
Progressive Disclosure | Content is reasonably structured with clear sections, but the skill is quite long (~300 lines) with detailed templates that could be split into separate reference files. The inline mode analysis templates especially could be externalized to reduce the main skill's length. | 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
- actionbook/rust-skills
- Commit
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