askill
code-quality-evaluation

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Multi-agent code quality evaluation — orchestrates specialized review agents for naming, complexity, comments, tests, error handling, security, observability, and documentation. Invoke after completing coding tasks or when asked to evaluate code quality.

1 stars
1.2k downloads
Updated 3/2/2026

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SKILL.md

Code Quality Evaluation

Orchestrate 8 specialized agents for comprehensive code quality evaluation. All agents report findings — you apply fixes.

Agents

All agents are recommendation-only — they read code and report, never modify.

Evaluation agents (run in parallel, background):

AgentFocus
namerNaming issues: vague, misleading, type-focused identifiers
code-simplifierComplexity: duplication, deep nesting, verbose patterns
comment-cleanerComment noise, redundancy, missing documentation comments
test-reviewerTest quality, strategy, leanness, locality
error-handling-reviewerError flow: creation, propagation, handling, silent swallowing
security-reviewerSecrets, injection, input validation, crypto, auth
observability-reviewerLogging, metrics, tracing, context propagation

Documentation agent (runs last, after fixes):

AgentFocus
documenterMissing, outdated, or insufficient API documentation

CRITICAL: Do Not Use TaskOutput

Never call the TaskOutput tool during this workflow.

How to wait for background agents:

  1. Launch agent with run_in_background=true
  2. Do nothing — literally output nothing and wait
  3. System notifies you: "Background task completed"
  4. Read the report FILE the agent wrote — not TaskOutput

TaskOutput destroys context efficiency for the entire workflow.

Workflow

Phase 0: Setup Reports Directory

Create reports directory inside the target:

mkdir -p {target}/.reviews/{timestamp}

Reports directory must be inside target — agents can only write within their review scope.

Phase 1: Evaluate Code (Parallel, Background)

Launch all 7 evaluation agents in parallel:

For each agent in [namer, code-simplifier, comment-cleaner,
                   test-reviewer, error-handling-reviewer,
                   security-reviewer, observability-reviewer]:
    Task(agent, prompt="""
    Evaluate {target} and report findings.

    Write findings to: {reports_dir}/{agent}.md
    Do NOT modify any code — only report what should change.
    Do NOT include file contents in the report — only findings.
    """, run_in_background=true)

Phase 2: Read Reports and Present Findings

After all evaluation agents complete:

  1. Read all report files from {reports_dir}/
  2. Aggregate into summary (Critical / Issues / Recommendations counts)
  3. Present findings to user organized by severity
  4. Ask user which issues to address

Phase 3: Apply Fixes

Address findings based on user direction:

  1. Fix critical issues first
  2. Then issues, then recommendations
  3. User may skip categories or individual findings
  4. Continue until user says to stop or all addressed

This phase is iterative — fix, verify, repeat.

Phase 4: Documentation Review (After Fixes)

Only after code fixes are complete, run documenter:

Task(documenter, prompt="""
Evaluate API documentation in {target}.

Write findings to: {reports_dir}/documenter.md
Do NOT modify any code — only report what documentation is missing.
Do NOT include file contents in the report — only findings.
""", run_in_background=true)

Documentation reviews the fixed code, not the original state. Running it earlier wastes effort on outdated code.

Phase 5: Cleanup

After all work complete:

rm -rf {reports_dir}

Remove the .reviews/ directory — reports are temporary artifacts.

Usage

Run code quality evaluation on [path]

Constraints

Install

Download ZIP
Requires askill CLI v1.0+

AI Quality Score

81/100Analyzed 2/19/2026

Well-structured multi-agent code quality evaluation skill with 8 specialized agents across 6 phases. Excellent actionability with clear commands, prompts, and constraints. Strong safety with read-only agents and proper cleanup. Minor gaps: missing explicit trigger section in body, some placeholder definitions could be clearer. High-quality technical reference suitable for orchestrating comprehensive code reviews."

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Metadata

Licenseunknown
Version-
Updated3/2/2026
Publisherxobotyi

Tags

apigithub-actionsobservabilitypromptingsecuritytesting