error-diagnostics-error-analysis

You are an expert error analysis specialist with deep expertise in debugging distributed systems, analyzing production incidents, and implementing comprehensive observability solutions.

Quality

51%

Does it follow best practices?

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Securityby

Passed

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tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills-claude/skills/error-diagnostics-error-analysis/SKILL.md

Quality

Content

50%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 is concise and reasonably structured with a clear sequence, but guidance stays at a high level with no concrete commands or code, and the single referenced bundle file does not exist in the skill. Validation for risky production operations is only implicit.

Suggestions

Add concrete, executable guidance — example commands for log correlation or trace inspection, or a concrete reproduction/analysis template — to lift actionability above vague hints.

Create the referenced `resources/implementation-playbook.md` (or fix the path to match the actual file) so the signaled reference resolves, or remove the broken reference.

Add an explicit validation checkpoint in the workflow (e.g., confirm root cause with evidence before proposing fixes; require approval + rollback plan before any production change) to satisfy the feedback-loop requirement for destructive operations.

Dimension	Reasoning	Score
Conciseness	The body is short with compact sections and no padding of basic concepts, though the "Context" paragraph is somewhat verbose ("systematic error analysis... across the full application lifecycle—...using industry-standard observability tools, structured logging, distributed tracing..."), fitting anchor 4; not a 5 because the Context paragraph could still be trimmed.	4 / 5
Actionability	The instructions are high-level hints — "Gather error context", "Reproduce or narrow the issue with targeted experiments", "Identify root cause and validate with evidence" — with no concrete code, commands, or specific examples, matching anchor 2; not a 1 because there is some real guidance, and not a 3 because no executable specifics are given.	2 / 5
Workflow Clarity	A rough sequence exists (Gather → Reproduce → Identify root cause → Propose fixes) but validation is only abstract ("validate with evidence") and the skill touches risky/destructive operations (production changes) without explicit checkpoints, fitting anchor 3 and the feedback-loop cap; not a 4 because concrete validation steps are missing.	3 / 5
Progressive Disclosure	Sections are reasonably organized and a detailed reference is signaled ("open `resources/implementation-playbook.md"), but the referenced bundle file does not actually exist in the skill bundle, so navigation is broken; fits anchor 3; not a 4 because the one signaled reference points at a missing file.	3 / 5
	Total	12 / 20 Passed

Description

53%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.

The description states a clear capability niche with a few concrete actions but omits any "Use when" trigger guidance, which caps completeness. Trigger terms are somewhat technical and lack natural conversational phrasings users would say.

Suggestions

Add an explicit "Use when..." clause naming concrete triggers (e.g., "Use when investigating production incidents, recurring errors, or performing root-cause analysis across services") to raise completeness.

Add natural conversational keywords and synonyms users actually say ("investigating errors", "root cause", "debugging an outage") alongside the technical terms.

Tighten the action list to more specific concrete actions (e.g., "analyzes stack traces, correlates logs across services, proposes fixes") to improve specificity.

Dimension	Reasoning	Score
Specificity	Names the domain ("error analysis specialist") and a few concrete actions — "debugging distributed systems, analyzing production incidents, and implementing comprehensive observability solutions" — but coverage is not comprehensive, matching anchor 3; not a 4 because the actions are broad categories rather than several specific concrete actions.	3 / 5
Completeness	It states a clear "what" (expert error analysis specialist) but provides no "when"/"Use when" trigger guidance, which the rubric caps at 3; not a 4 because the "when" is entirely missing rather than merely weakly implied.	3 / 5
Trigger Term Quality	Relevant terms like "debugging," "production incidents," and "observability" appear but lean technical/jargony and miss common user phrasings like "investigating errors" or "root-cause analysis," fitting anchor 3; not a 4 because natural conversational triggers and synonyms are largely absent.	3 / 5
Distinctiveness Conflict Risk	The niche (production-incident error analysis / observability) is fairly distinct from most skills with only minor overlap risk against general debugging or troubleshooting skills, matching anchor 4; not a 5 because there is some residual overlap with generic debugging skills and no crisp trigger boundary.	4 / 5
	Total	13 / 20 Passed

Validation

93%

Warnings & errors only

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

Validation — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: administrakt0r/AI-Agents-Safe-Coding-Skills
Path: plugins/AI-Agents-Safe-Coding-Skills-claude/skills/error-diagnostics-error-analysis/SKILL.md
Commit: 57c135b

Reviewed: 3 days ago

Table of Contents

Discovery Implementation Validation

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