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Subagent

Skill Effectiveness Analyzer

Analyzes skill effectiveness data to identify failure patterns and recommend improvements. Use after /skill-monitor flags underperforming skills.

Type
Subagent
GitHub stars
556
License
MIT
Repo last updated
Sep 25, 2026
Model
sonnet

What Skill Effectiveness Analyzer is

Skill Effectiveness Analyzer is a subagent published in the oliver-kriska/claude-elixir-phoenix repository on GitHub, which has about 556 stars. The repository describes itself as: “Claude Code plugin for Elixir/Phoenix/LiveView — 26 specialist agents, Iron Laws enforcement, and Tidewave MCP integration. Plan features with parallel research agents, execute with automatic verification, review with 4-agent parallel audits, and capture learnings as reusable knowledge.”

A subagent is a specialist assistant that Claude can hand part of a task to. It is a markdown file whose frontmatter sets a name, a description that tells Claude when to delegate, and optionally the tools and model it may use; the body becomes the subagent's own system prompt.

Because a subagent works in its own context, it keeps the main conversation focused: Claude can send a narrow job, such as a review or a specialised analysis, to Skill Effectiveness Analyzer and get back a compact result.

It is set up to use these tools: Read, Grep, Glob, Write. Limiting tools is a good sign: the subagent can only do what those tools allow.

How to install Skill Effectiveness Analyzer

Claude Code

  1. Download skill-effectiveness-analyzer.md from the repository.
  2. Save it to ~/.claude/agents/ to use it in every project, or to .claude/agents/ inside one project to share it through version control.
  3. Claude Code watches these folders, so the subagent is usually available right away. Ask Claude to use it by name, or @-mention it to make sure it runs.

Claude Cowork

  1. Cowork loads subagents through plugins. If the repository is packaged as a plugin marketplace, add it under Customize → Plugins → Add marketplace and install the plugin that contains this subagent.
  2. Otherwise, bundle the file into your own plugin's agents/ folder and upload it from Customize → Plugins.

New to extending Cowork? Our plugins guide and Customize guide explain how skills, plugins, and connectors fit together.

Inside the source file

An excerpt from .claude/agents/skill-effectiveness-analyzer.md, shared under the repository's MIT license. Read the full file on GitHub.

You analyze plugin skill effectiveness metrics and produce actionable improvement recommendations. You are part of the closed-loop feedback cycle: deploy - monitor - evaluate - improve.

Your Role

You receive aggregated skill metrics from /skill-monitor and produce structured recommendations following the improvement template. You do NOT modify skills or agents — you write a recommendations file that the developer reviews.

Inputs (via prompt)

  1. metrics_data — JSON with per-skill aggregates
  2. flagged_skills — Skills below effectiveness thresholds
  3. session_ids — Sessions where flagged skills had friction
  4. window — Time window analyzed

Workflow

Step 1: Load Context

  1. Read metrics data from prompt
  2. Read improvement template — Glob: **/skill-monitor/references/improvement-template.md
  3. Check for session analysis reports — Glob: .claude/session-analysis/*-report.md
  4. Check for previous recommendations — Glob: .claude/skill-metrics/recommendations-*.md

Step 2: Analyze Flagged Skills

For each flagged skill:

  1. Read the skill's source file — Glob: **/skills/{skill-name}/SKILL.md
  2. Read related agent files — Grep: {skill-name} in plugins/elixir-phoenix/agents/*.md
  3. Check session reports — Grep: {skill-name} in .claude/session-analysis/*-report.md
  4. Check compound solutions — Grep: {skill-name} in .claude/solutions/**/*.md

Step 3: Identify Failure Patterns

For each flagged skill, classify the failure mode:

Cross-reference with session reports if available. Prefer STRONG evidence (3+ sessions) over inference.

Step 4: Generate Recommendations

Follow the improvement template structure exactly. For each recommendation:

  1. Identify the specific file to change
  2. Describe the change concretely (not vaguely)
  3. Cite session evidence
  4. Estimate impact

Step 5: Check Previous Recommendations

If previous recommendation files exist, check:

  • Were prior recommendations implemented? (read the skill files)
  • Did effectiveness improve after implementation?
  • Are any prior recommendations still relevant?

Add a "Prior Recommendations Status" section:

Step 6: Write Output

Write to .claude/skill-metrics/recommendations-{date}.md following the improvement template format.

Include tracking plan at the end with:

  • Current baseline metrics for flagged skills
  • Target metrics after improvements
  • Re-evaluation timeline

Constraints

  • Read-only analysis — never modify skill or agent files
  • Evidence-backed only — every recommendation needs session citations
  • Concrete changes — "improve the prompt" is not actionable; "add step 2b: check compound solutions before debugging" is
  • Keep recommendations under 200 lines
  • Max 5 priority recommendations — focus beats breadth
  • Don't recommend new skills when existing ones need fixing
  • Attribution: if a pattern was found by session-deep-dive, cite the session report

Output Format

# Skill Improvement Recommendations — {date}

## Executive Summary
{1 paragraph}

## Flagged Skills
{per-skill analysis following template}

## Cross-Skill Patterns
{patterns affecting multiple skills}

## Positive Patterns (Preserve)
{what's working}

## Priority Ranking
{ordered recommendations}

## Prior Recommendations Status
…

Before you install

  • Read the whole file first. Skills, commands, and subagents are instructions Claude will follow, so make sure they match what you want.
  • Check which tools, scripts, or MCP servers it uses. Local servers and scripts run with your permissions.
  • Try it in a test project or a copy of your files before pointing it at real work.
  • Pin the version you tested, and review changes before updating.
  • Watch for instructions that fetch web content or run shell commands; those are where prompt injection risks start. See our prompt injection guide.

FAQ

What is Skill Effectiveness Analyzer?

Skill Effectiveness Analyzer is a subagent for Claude Code and Claude Cowork from the oliver-kriska/claude-elixir-phoenix repository on GitHub. Analyzes skill effectiveness data to identify failure patterns and recommend improvements. Use after /skill-monitor flags underperforming skills.

How do I install Skill Effectiveness Analyzer in Claude Code?

Download skill-effectiveness-analyzer.md from the repository. Save it to ~/.claude/agents/ to use it in every project, or to .claude/agents/ inside one project to share it through version control. Claude Code watches these folders, so the subagent is usually available right away. Ask Claude to use it by name, or @-mention it to make sure it runs.

Can I use Skill Effectiveness Analyzer in Claude Cowork?

Cowork loads subagents through plugins. If the repository is packaged as a plugin marketplace, add it under Customize → Plugins → Add marketplace and install the plugin that contains this subagent. Otherwise, bundle the file into your own plugin's agents/ folder and upload it from Customize → Plugins.

Is Skill Effectiveness Analyzer safe to install?

It is a third-party community resource, not reviewed by Anthropic or this site. Read the source file first, check which tools and connectors it uses, and install only from sources you trust.

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Listing data comes from the public GitHub repository and was last checked in September 2026. Excerpts are © their authors and shared under MIT. This directory is independent and not affiliated with Anthropic or the resource's authors.