check-duplicates
Check for duplicate or similar cases. Use before deep analysis to avoid investigating the same incident twice. Takes a CASE_ID and returns list of similar cases.
Install with Tessl CLI
npx tessl i github:dandye/ai-runbooks --skill check-duplicates87
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
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-crafted skill description that clearly communicates its purpose, input/output, and when to use it. The explicit 'Use before deep analysis' clause provides excellent workflow context. Minor improvement could come from expanding trigger terms to include more natural user language variations.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists specific concrete actions: 'Check for duplicate or similar cases', 'Takes a CASE_ID', 'returns list of similar cases'. Clear input/output behavior is described. | 3 / 3 |
Completeness | Clearly answers both what ('Check for duplicate or similar cases', 'Takes a CASE_ID and returns list of similar cases') AND when ('Use before deep analysis to avoid investigating the same incident twice'). | 3 / 3 |
Trigger Term Quality | Includes relevant terms like 'duplicate', 'similar cases', 'CASE_ID', and 'incident', but missing common variations users might say like 'dupe check', 'already investigated', 'existing case', or 'repeat incident'. | 2 / 3 |
Distinctiveness Conflict Risk | Clear niche focused on case deduplication with specific trigger context (before deep analysis, CASE_ID input). Unlikely to conflict with general search or analysis skills due to the specific workflow positioning. | 3 / 3 |
Total | 11 / 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 clearly defines inputs, outputs, and workflow for checking duplicate cases. The main weakness is Step 2's lack of concrete code for processing the API response, which leaves some ambiguity in implementation. Overall, it effectively communicates the task with good organization.
Suggestions
Add executable code in Step 2 showing how to extract similar case IDs from the API response (e.g., `similar_ids = response.get('similar_cases', [])`)
Consider adding an example response structure from the API call to clarify what data is returned
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is lean and efficient, presenting only necessary information without explaining concepts Claude already knows. Every section serves a clear purpose with no padding or unnecessary context. | 3 / 3 |
Actionability | Provides a concrete function call with parameters, but Step 2 'Extract the list of similar case IDs from the response' is vague and lacks executable code showing how to parse the response. The usage pattern is also somewhat abstract. | 2 / 3 |
Workflow Clarity | Clear sequential workflow with explicit decision points (if duplicates found vs not found). The usage pattern provides a logical flow with branching conditions and specific actions for each outcome. | 3 / 3 |
Progressive Disclosure | For a simple, single-purpose skill under 50 lines, the content is well-organized with clear sections (Inputs, Workflow, Outputs, Usage Pattern). No external references needed and structure is appropriate for the scope. | 3 / 3 |
Total | 11 / 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 | |
- Reviewed
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