Traces regressions to the specific commit, change, or code path that introduced the behavioral breakage. Use when a previously passing test or feature now fails, when the user asks what change caused a regression, or when bisecting a regression across commits.
Install with Tessl CLI
npx tessl i github:santosomar/general-secure-coding-agent-skills --skill regression-root-cause-analyzer100
Quality
100%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
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 a well-crafted skill description that excels across all dimensions. It clearly specifies the concrete action (tracing regressions to commits/changes), uses natural developer terminology, includes an explicit 'Use when...' clause with multiple trigger scenarios, and carves out a distinct niche that won't conflict with general debugging or testing skills.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'Traces regressions to the specific commit, change, or code path that introduced the behavioral breakage.' This clearly describes what the skill does with concrete technical actions. | 3 / 3 |
Completeness | Clearly answers both what ('Traces regressions to the specific commit, change, or code path') AND when ('Use when a previously passing test or feature now fails, when the user asks what change caused a regression, or when bisecting'). | 3 / 3 |
Trigger Term Quality | Includes natural keywords users would say: 'regression', 'previously passing test', 'feature now fails', 'what change caused', 'bisecting'. These are terms developers naturally use when debugging regressions. | 3 / 3 |
Distinctiveness Conflict Risk | Clear niche focused specifically on regression tracing and bisecting. The triggers are distinct from general debugging or testing skills, with specific focus on 'previously passing' scenarios and commit-level analysis. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
100%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is an exemplary skill that maximizes signal-to-noise ratio. It provides concrete, executable guidance with clear workflows, validation checkpoints, and edge case handling. The worked example demonstrates the complete process with real commands, and the output format ensures consistent handoff to downstream skills.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely lean and efficient. No explanation of what git bisect is or how regressions work conceptually—assumes Claude knows. Every table, command, and section earns its place with actionable content. | 3 / 3 |
Actionability | Fully executable commands throughout (git bisect start, git bisect run, pytest invocations). The worked example is copy-paste ready with real commands and demonstrates the complete workflow from start to fault localization. | 3 / 3 |
Workflow Clarity | Clear 3-step sequence with explicit validation checkpoints ('Verify both bounds before bisecting'). Includes feedback loops for edge cases (re-verify bounds, bisect again). The 'Do not' section reinforces critical validation steps. | 3 / 3 |
Progressive Disclosure | Well-structured with clear sections (steps, worked example, edge cases, output format). Appropriate length for a single file. References to related skills (bug-to-patch-generator, bug-localization) are one-level deep and clearly signaled. | 3 / 3 |
Total | 12 / 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.
Table of Contents
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.