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debugging-toolkit-smart-debug

Diagnose software failures using observability data, ranked hypotheses, and targeted instrumentation. Use when the cause is unclear or an issue requires systematic investigation.

52

Quality

66%

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SecuritybySnyk

Low

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tessl review fix ./skills/debugging-toolkit-smart-debug/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

48%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body presents a well-sequenced ten-step debugging workflow with validation criteria, but is undermined by boilerplate filler sections, persona padding, a non-executable pseudocode example, and inline content that belongs in reference files. Actionable core guidance is diluted by descriptive 'AI does X' narration.

Suggestions

Delete the filler sections (Use/Do not use headers that restate the name, the expert-persona line, the generic Instructions bullets) and the duplicate "$ARGUMENTS" block at line 196.

Replace the pseudocode example with either a real executable snippet or a concrete inline walkthrough using actual commands/queries, and rewrite 'AI does X' steps as instructions to the reader.

Move the observability tool catalogs and the worked example into references/ files (e.g. OBSERVABILITY.md, EXAMPLE.md) linked one level deep, and add an explicit feedback loop in the validation step (if criteria fail, revisit hypotheses).

DimensionReasoningScore

Conciseness

Several padded sections add no value: the "Use this skill when"/"Do not use this skill when" headers just restate the skill name, "You are an expert AI-assisted debugging specialist..." is persona padding, the "Instructions" section is generic bullets, "$ARGUMENTS" appears twice (lines 26 and 196), and tool-name catalogs (Sentry, Rollbar, Bugsnag, etc.) list things Claude already knows — matching 'noticeably verbose; several unnecessary explanations or padded sections'.

2 / 5

Actionability

There is concrete guidance (strategy-selection mapping, hypothesis field template, output format, success criteria), but the worked example is pseudocode against non-existent APIs (aiAnalyze, getSentryIssue, getDataDogTraces) and many steps describe what "AI" does ("AI suggests optimal breakpoint/logpoint locations", "Use Task tool (subagent_type='debugger')") rather than instructing — matching 'some concrete guidance but incomplete; pseudocode instead of executable code'.

3 / 5

Workflow Clarity

The ten steps are clearly sequenced (triage → observability → hypotheses → strategy → instrumentation → RCA → fix → validation → prevention) with an explicit validation section and success criteria, matching 'clear sequence with most checkpoints present'; below anchor 5 because there is no explicit feedback loop (e.g. if validation fails, return to hypothesis testing).

4 / 5

Progressive Disclosure

No bundle files exist and everything is inlined across ~197 lines; structure via numbered workflow sections is decent, but content that clearly belongs in separate reference files (observability tool catalogs, the long worked example, per-step AI-subagent details) is inlined, matching 'some structure but could be better organized; content that should be separate is inline'.

3 / 5

Total

12

/

20

Passed

Description

67%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A solid description with explicit what/when structure and concrete method names, written in proper third person. Its main weaknesses are missing the most natural trigger terms (debug, bug, root cause) and an abstract when-clause instead of concrete trigger phrases.

Suggestions

Add natural trigger terms users actually say — e.g. "Use when the user mentions debugging, a bug, errors, root cause analysis, or intermittent failures".

Make the when-clause more concrete by naming entry points such as stack traces, production alerts, or failing tests rather than the abstract "requires systematic investigation".

Consider naming 1-2 more outcome actions (e.g. "identifies root cause and proposes a validated fix") to round out coverage for a top specificity score.

DimensionReasoningScore

Specificity

"Diagnose software failures using observability data, ranked hypotheses, and targeted instrumentation" names the domain plus several concrete methods in third person, matching the 'several specific actions; minor gaps' anchor; below anchor 5 because a single verb ('Diagnose') with methods is not a comprehensive action list.

4 / 5

Completeness

Both what ("Diagnose software failures using...") and when ("Use when the cause is unclear...") are explicitly present, but the when-clause is generic and abstract rather than concrete trigger phrases, so it fits 'both present; when could be more explicit' rather than the anchor 5 example.

4 / 5

Trigger Term Quality

Triggers like "software failures", "cause is unclear", and "systematic investigation" are relevant, but the most natural user phrases — "debug", "bug", "error", "root cause", "troubleshoot" — are absent, matching the 'relevant keywords but missing common variations or synonyms' anchor.

3 / 5

Distinctiveness Conflict Risk

The failure-diagnosis niche is mostly distinct with concrete tool signals (observability, instrumentation), but "cause is unclear or requires systematic investigation" could overlap with general debugging/troubleshooting skills, matching 'mostly distinct; minor overlap risk'.

4 / 5

Total

15

/

20

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 — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

rmyndharis/antigravity-skills

Path

skills/debugging-toolkit-smart-debug/SKILL.md

Reviewed

Table of Contents

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