CtrlK
BlogDocsLog inGet started
Tessl Logo
SkillPublic

multi-ai-debugging

Systematic debugging using Claude, Gemini, and Codex as specialized agents. Multi-agent root cause analysis, log analysis, error classification, and auto-fix generation. Use when debugging production issues, analyzing error logs, performing root cause analysis, troubleshooting complex systems, or implementing self-healing patterns.

Quality

77%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide
SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./.claude/skills/multi-ai-debugging/SKILL.md

The canonical home for this skill is multi-ai-debugging in fernandezbaptiste/Skrillz

SKILL.md
Quality
Evals
Security

Quality

Content

63%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 delivers genuinely actionable multi-agent debugging workflows with ready-to-use prompts, concrete commands, and built-in verification loops. Its weaknesses are a ~640-line monolithic structure that keeps log-analysis and self-healing material inline instead of pointing to the existing bundle files, plus padded sections (decision trees, severity matrix, quality targets) that restate what Claude already knows.

Suggestions

Link the bundle files at their point of use (e.g. in Operation 3: 'For parsing strategies and pattern regexes, see references/log-analysis-patterns.md') and trim the overlapping inline content to move progressive disclosure toward the well-signaled anchor.

Delete or move the error-type decision tree, P1–P4 severity matrix, and 'Quality Targets' sections to a reference file — they restate standard knowledge and add token cost without new information.

Close the critic-loop gap in Operation 5 by adding an explicit retry step: 'If NEEDS_REVISION: revise the fix per the critique and re-run the critic before generating the regression test.'

DimensionReasoningScore

Conciseness

The six operation prompt templates are the efficient core, but several sections restate what Claude already knows — the error-type decision tree ('NullPointer/TypeError → Missing null check'), the P1–P4 severity matrix — plus unverifiable 'Quality Targets' ('Root Cause Identification: >80% accuracy') and the dated 'Based on 2024-2025 best practices' claim, fitting 'mostly efficient but includes some unnecessary explanation'.

3 / 5

Actionability

Most guidance is concrete and ready to use: copy-paste prompt templates with placeholders, executable shell commands (git log, grep, gemini -p, codex), and a full worked example with before/after code; the gap is the non-executable Python scaffolding (AICircuitBreaker calling an undefined analyze(), correlate_logs_for_debugging calling undefined query_logs()), so it fits 'mostly executable guidance with minor gaps'.

4 / 5

Workflow Clarity

Operations 2 and 5 present clear step sequences with verification built in (hypothesis cross-validation with reproduction test design, a critic agent issuing APPROVE/NEEDS_REVISION/REJECT verdicts, regression test generation), matching 'clear sequence with most checkpoints present'; it falls short of anchor 5 because the critic loop never says what to do on NEEDS_REVISION — there is no explicit revise-and-re-validate retry loop.

4 / 5

Progressive Disclosure

Sections are well organized and the two bundle files (references/log-analysis-patterns.md, references/self-healing-playbooks.md) are real and one level deep, but they are only listed in a terminal References section — Operations 3 and 6 never link to their matching files and instead carry that material inline in a ~640-line body, fitting 'some structure but could be better organized; content that should be separate is inline'.

3 / 5

Total

14

/

20

Passed

Description

83%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 strong description that clearly states what the skill does and when to use it, with concrete capabilities and natural trigger phrases. Its main weaknesses are minor coverage gaps (council debugging, post-mortems) and a few missing common trigger synonyms like 'stack traces' or 'incident'.

Suggestions

Add missing capability nouns from the body's operations, e.g. '...and post-mortem reporting' or 'multi-agent council debates', to close the specificity gap toward comprehensive coverage.

Include high-frequency natural trigger variations such as 'stack traces', 'incident response', or 'fixing recurring errors' to improve trigger term coverage.

Narrow the broad trigger 'troubleshooting complex systems' (e.g. 'troubleshooting complex multi-service failures') to reduce overlap with the related ecs-troubleshooting/railway-troubleshooting skills.

DimensionReasoningScore

Specificity

The description lists several concrete actions — 'Multi-agent root cause analysis, log analysis, error classification, and auto-fix generation' — but omits capabilities present in the body (multi-agent council workflow, post-mortem reports), so it fits 'several specific actions; minor gaps in coverage' rather than the comprehensive anchor 5.

4 / 5

Completeness

It explicitly answers both what ('Systematic debugging using Claude, Gemini, and Codex as specialized agents. Multi-agent root cause analysis, log analysis, error classification, and auto-fix generation') and when ('Use when debugging production issues, analyzing error logs, performing root cause analysis, troubleshooting complex systems, or implementing self-healing patterns') with concrete trigger phrases, matching the anchor 5 example pattern exactly.

5 / 5

Trigger Term Quality

Triggers like 'debugging production issues', 'analyzing error logs', 'performing root cause analysis', and 'troubleshooting complex systems' are natural user phrases, but common variations such as 'stack traces', 'incident', or '500 errors' are missing, matching 'good keyword coverage; a few natural terms missing' rather than the comprehensive anchor 5.

4 / 5

Distinctiveness Conflict Risk

Naming the Claude/Gemini/Codex ensemble and multi-agent RCA carves a distinct niche, but 'troubleshooting complex systems' broadly overlaps with closely related skills the body itself lists (ecs-troubleshooting, railway-troubleshooting, multi-ai-verification), fitting 'mostly distinct; minor overlap risk'.

4 / 5

Total

17

/

20

Passed

Validation

87%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (644 lines); consider splitting into references/ and linking

Warning

allowed_tools_field

'allowed-tools' contains unusual tool name(s)

Warning

Total

14

/

16

Passed

Repository

fernandezbaptiste/Skrillz

Path

.claude/skills/multi-ai-debugging/SKILL.md

Reviewed

Table of Contents

Is this your skill?

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.