failure-oriented-instrumentation
Selectively instruments code to capture runtime data for debugging failures and bugs. Use when investigating crashes, exceptions, unexpected behavior, test failures, or performance issues. Analyzes stack traces and error messages to identify suspicious code regions, then adds targeted logging, tracing, and assertions to capture variable values, execution paths, timing, and conditional branches. Supports Python, JavaScript/TypeScript, Java, and C/C++.
Install with Tessl CLI
npx tessl i github:ArabelaTso/Skills-4-SE --skill failure-oriented-instrumentation87
Does it follow best practices?
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npx tessl skill review --optimize ./path/to/skillValidation for skill structure
Discovery
100%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 an excellent skill description that hits all the key criteria. It provides specific concrete actions, includes a comprehensive set of natural trigger terms developers would use, explicitly states both what the skill does and when to use it, and carves out a distinct niche around runtime instrumentation for debugging. The description is well-structured and uses appropriate third-person voice throughout.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'instruments code', 'capture runtime data', 'analyzes stack traces and error messages', 'adds targeted logging, tracing, and assertions', 'capture variable values, execution paths, timing, and conditional branches'. Also specifies supported languages. | 3 / 3 |
Completeness | Clearly answers both what ('instruments code to capture runtime data', 'adds targeted logging, tracing, and assertions') AND when ('Use when investigating crashes, exceptions, unexpected behavior, test failures, or performance issues') with an explicit 'Use when...' clause. | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural terms users would say: 'debugging', 'failures', 'bugs', 'crashes', 'exceptions', 'unexpected behavior', 'test failures', 'performance issues', 'stack traces', 'error messages', 'logging', 'tracing'. These are all terms developers naturally use when seeking debugging help. | 3 / 3 |
Distinctiveness Conflict Risk | Clear niche focused on runtime instrumentation for debugging, distinct from general debugging skills or static analysis. The specific focus on 'selectively instruments code' and 'capture runtime data' creates a unique identity that wouldn't conflict with code review, testing, or general debugging skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
72%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 skill with strong actionability through concrete code examples and excellent progressive disclosure via language-specific reference files. The main weaknesses are moderate verbosity in the guidelines sections and missing explicit validation/feedback loops in the workflow for verifying instrumentation effectiveness.
Suggestions
Add a validation step after inserting instrumentation (e.g., 'Run once with minimal test input to verify logs appear before full reproduction')
Include a feedback loop: 'If captured data is insufficient to identify root cause, expand instrumentation scope and repeat steps 4-6'
Trim the 'What to Instrument' and 'What NOT to Instrument' sections - Claude understands these priorities; a brief bullet list would suffice
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is reasonably efficient but includes some unnecessary explanation (e.g., the detailed 'What to Instrument' priority lists and 'What NOT to Instrument' sections explain concepts Claude would understand). The workflow steps could be tightened. | 2 / 3 |
Actionability | Provides fully executable code examples in Java, Python, and JavaScript with specific instrumentation patterns. The Quick Start Examples are copy-paste ready and demonstrate concrete debugging scenarios. | 3 / 3 |
Workflow Clarity | The 6-step workflow is clearly sequenced, but lacks explicit validation checkpoints. Step 5 'Run and Collect Data' doesn't specify how to verify instrumentation is working correctly, and there's no feedback loop for when instrumentation reveals insufficient data. | 2 / 3 |
Progressive Disclosure | Excellent structure with clear overview, quick start examples inline, and well-signaled one-level-deep references to language-specific files (references/python.md, etc.). Content is appropriately split between overview and detailed references. | 3 / 3 |
Total | 10 / 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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