Agent skill

Skill Tuning

by catlog22 in catlog22/Claude-Code-Workflow

Universal skill diagnosis and optimization tool. An agent skill from catlog22/Claude-Code-Workflow.

MITAuto-check: notesAgent Workflows

Install Skill Tuning

skills CLI
$ npx skills add catlog22/Claude-Code-Workflow --skill skill-tuning -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install catlog22/Claude-Code-Workflow skill-tuning --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/catlog22/Claude-Code-Workflow.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/skill-tuning .claude/skills/skill-tuning && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
skill-tuning
GitHub stars
2.1k
Token cost
~1.7k tokens
SKILL.md length
341 words
Files
28
Skills in repo
82
Repo updated
First seen
Licence
MIT

At a glance

Universal skill diagnosis and optimization tool. An agent skill from catlog22/Claude-Code-Workflow.

  • Works in 5 steps: Problem-First: Diagnosis before any fix → Data-Driven: Record traces, token… → Iterative: Multiple rounds until quality… → …
  • Skill diagnosis
  • SKILL.md covers Architecture, Core Issues Detected, Problem Categories (Detailed… and Tuning Strategies (Detailed…, plus 8 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Skill Tuning is an agent skill from catlog22/Claude-Code-Workflow. Universal skill diagnosis and optimization tool. Detect and fix skill execution issues including context explosion, long-tail forgetting, data flow disruption, and agent coordination failures. Supports Gemini CLI for deep analysis. Triggers on "skill tuning", "tune skill", "skill diagnosis", "optimize skill", "skill debug".

Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 29 other files (for example `phases/actions/action-abort.md`, `phases/actions/action-analyze-requirements.md` and `phases/actions/action-apply-fix.md`).

It sits in Agent Workflows, covering Multi-agent orchestration. The repository describes itself as: JSON-driven multi-agent cadence-team development framework with intelligent CLI orchestration (Gemini/Qwen/Codex), context-first architecture, and automated workflow execution. The licence is MIT.

When your agent uses it

  • Skill diagnosis
  • Tasks that involve Multi-agent orchestration

Example prompts

  • “skill tuning”
  • “tune skill”
  • “skill diagnosis”
  • “/skill-tuning”

Requirements

  • Pre-approved tools (allowed-tools): Agent, AskUserQuestion, Read, Write, Bash, Glob, Grep, mcp__ace-tool__search_context

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Problem-First: Diagnosis before any fix
  2. Data-Driven: Record traces, token counts, snapshots
  3. Iterative: Multiple rounds until quality gates pass
  4. Reversible: All changes with backup checkpoints
  5. Non-Invasive: Minimal changes, maximum clarity

What it can do on your machine

Read from SKILL.md and the folder at commit 07491b0. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Agent
    • AskUserQuestion
    • Read
    • Write
    • Bash
    • Glob
    • Grep
    • mcp__ace-tool__search_context

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are json and bash).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Skill Tuning loads about 1.7k tokens when it runs. Until then it costs about 85 tokens; SKILL.md has 341 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~85
When it runs · the whole SKILL.md, loaded when a task matches
~1.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Agent, AskUserQuestion, Read, Write, Bash, Glob, Grep, mcp__ace-tool__search_context

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from catlog22/Claude-Code-Workflow at commit 07491b0, republished under its MIT licence (© catlog22). 341 words, ~1,671 tokens.

Download SKILL.mdSave it as .claude/skills/skill-tuning/SKILL.md (or your agent's skills folder). This skill also uses 27 other files; get the full folder from GitHub.
name
skill-tuning
description
Universal skill diagnosis and optimization tool. Detect and fix skill execution issues including context explosion, long-tail forgetting, data flow disruption, and agent coordination failures. Supports Gemini CLI for deep analysis. Triggers on "skill tuning", "tune skill", "skill diagnosis", "optimize skill", "skill debug".
allowed-tools
Agent, AskUserQuestion, Read, Write, Bash, Glob, Grep, mcp__ace-tool__search_context

Skill Tuning

Autonomous diagnosis and optimization for skill execution issues.

Architecture

┌─────────────────────────────────────────────────────┐
│  Phase 0: Read Specs (mandatory)                    │
│  → problem-taxonomy.md, tuning-strategies.md         │
└─────────────────────────────────────────────────────┘
                        ↓
┌─────────────────────────────────────────────────────┐
│  Orchestrator (state-driven)                         │
│  Read state → Select action → Execute → Update → ✓ │
└─────────────────────────────────────────────────────┘
        ↓                           ↓
┌──────────────────────┐   ┌──────────────────┐
│  Diagnosis Phase     │   │ Gemini CLI       │
│  • Context          │   │ Deep analysis    │
│  • Memory           │   │ (on-demand)      │
│  • DataFlow         │   │                  │
│  • Agent            │   │ Complex issues   │
│  • Docs             │   │ Architecture     │
│  • Token Usage      │   │ Performance      │
└──────────────────────┘   └──────────────────┘
                ↓
        ┌───────────────────┐
        │  Fix & Verify     │
        │  Apply → Re-test  │
        └───────────────────┘

Core Issues Detected

PriorityProblemRoot CauseFix Strategy
P0Authoring ViolationIntermediate files, state bloat, file relayeliminate_intermediate, minimize_state
P1Data Flow DisruptionScattered state, inconsistent formatsstate_centralization, schema_enforcement
P2Agent CoordinationFragile chains, no error handlingerror_wrapping, result_validation
P3Context ExplosionUnbounded history, full content passingsliding_window, path_reference
P4Long-tail ForgettingEarly constraint lossconstraint_injection, checkpoint_restore
P5Token ConsumptionVerbose prompts, state bloatprompt_compression, lazy_loading

Problem Categories (Detailed Specs)

See specs/problem-taxonomy.md for:

  • Detection patterns (regex/checks)
  • Severity calculations
  • Impact assessments

Tuning Strategies (Detailed Specs)

See specs/tuning-strategies.md for:

  • 10+ strategies per category
  • Implementation patterns
  • Verification methods

Workflow

StepActionOrchestrator DecisionOutput
1action-initstatus='pending'Backup, session created
2action-analyze-requirementsAfter initRequired dimensions + coverage
3Diagnosis (6 types)Focus areasstate.diagnosis.{type}
4action-gemini-analysisCritical issues OR user requestDeep findings
5action-generate-reportAll diagnosis completestate.final_report
6action-propose-fixesIssues foundstate.proposed_fixes[]
7action-apply-fixPending fixesApplied + verified
8action-completeQuality gates passsession.status='completed'

Action Reference

CategoryActionsPurpose
Setupaction-initInitialize backup, session state
Analysisaction-analyze-requirementsDecompose user request via Gemini CLI
Diagnosisaction-diagnose-{context,memory,dataflow,agent,docs,token_consumption}Detect category-specific issues
Deep Analysisaction-gemini-analysisGemini CLI: complex/critical issues
Reportingaction-generate-reportConsolidate findings → final_report
Fixingaction-propose-fixes, action-apply-fixGenerate + apply fixes
Verifyaction-verifyRe-run diagnosis, check gates
Exitaction-complete, action-abortFinalize or rollback

Full action details: phases/actions/

State Management

Single source of truth: .workflow/.scratchpad/skill-tuning-{ts}/state.json

json
{
  "status": "pending|running|completed|failed",
  "target_skill": { "name": "...", "path": "..." },
  "diagnosis": {
    "context": {...},
    "memory": {...},
    "dataflow": {...},
    "agent": {...},
    "docs": {...},
    "token_consumption": {...}
  },
  "issues": [{"id":"...", "severity":"...", "category":"...", "strategy":"..."}],
  "proposed_fixes": [...],
  "applied_fixes": [...],
  "quality_gate": "pass|fail",
  "final_report": "..."
}

See phases/state-schema.md for complete schema.

Orchestrator Logic

See phases/orchestrator.md for:

  • Decision logic (termination checks → action selection)
  • State transitions
  • Error recovery

Key Principles

  1. Problem-First: Diagnosis before any fix
  2. Data-Driven: Record traces, token counts, snapshots
  3. Iterative: Multiple rounds until quality gates pass
  4. Reversible: All changes with backup checkpoints
  5. Non-Invasive: Minimal changes, maximum clarity

Usage Examples

bash
# Basic skill diagnosis
/skill-tuning "Fix memory leaks in my skill"

# Deep analysis with Gemini
/skill-tuning "Architecture issues in async workflow"

# Focus on specific areas
/skill-tuning "Optimize token consumption and fix agent coordination"

# Custom issue
/skill-tuning "My skill produces inconsistent outputs"

Output

After completion, review:

  • .workflow/.scratchpad/skill-tuning-{ts}/state.json - Full state with final_report
  • state.final_report - Markdown summary (in state.json)
  • state.applied_fixes - List of applied fixes with verification results

Reference Documents

DocumentPurpose
specs/problem-taxonomy.mdClassification + detection patterns
specs/tuning-strategies.mdFix implementation guide
specs/dimension-mapping.mdDimension ↔ Spec mapping
specs/quality-gates.mdQuality verification criteria
phases/orchestrator.mdWorkflow orchestration
phases/state-schema.mdState structure definition
phases/actions/Individual action implementations

© catlog22, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 27 other files in .claude/skills/skill-tuning of catlog22/Claude-Code-Workflow.

  • SKILL.md
  • phases/actions/action-abort.md
  • phases/actions/action-analyze-requirements.md
  • phases/actions/action-apply-fix.md
  • phases/actions/action-complete.md
  • phases/actions/action-diagnose-agent.md
  • phases/actions/action-diagnose-context.md
  • phases/actions/action-diagnose-dataflow.md
  • phases/actions/action-diagnose-docs.md
  • phases/actions/action-diagnose-memory.md
  • phases/actions/action-diagnose-token-consumption.md
  • phases/actions/action-gemini-analysis.md
  • phases/actions/action-generate-report.md
  • phases/actions/action-init.md
  • phases/actions/action-propose-fixes.md
  • phases/actions/action-verify.md
  • phases/orchestrator.md
  • phases/state-schema.md
  • specs
  • … and 9 more

Open the folder on GitHubat commit 07491b0

Used in 1 other repository

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders. This page covers the copy in catlog22/Claude-Code-Workflow, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Skill Tuning next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Paseo Advisor Second Opiniongetpaseo/paseo20k1 repos~756Automated safety check: PassCustom licence
O2 Review Loopopenobserve/openobserve22k—~3.7kAutomated safety check: PassAGPL-3.0
Paseo Committeegetpaseo/paseo20k1 repos~496Automated safety check: PassCustom licence
Mission Control Agent APIbuilderz-labs/mission-control6.3k—~2.1kAutomated safety check: PassMIT

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Categories

Questions about Skill Tuning

What does Skill Tuning do?

Universal skill diagnosis and optimization tool. An agent skill from catlog22/Claude-Code-Workflow. Skill Tuning is an agent skill from catlog22/Claude-Code-Workflow. Universal skill diagnosis and optimization tool.

When should I use Skill Tuning?

Skill Tuning fits situations like: skill diagnosis; tasks that involve Multi-agent orchestration.

How do I install Skill Tuning in Claude Code?

Run `npx skills add catlog22/Claude-Code-Workflow --skill skill-tuning -a claude-code`. Or copy the skill folder (.claude/skills/skill-tuning in catlog22/Claude-Code-Workflow) into .claude/skills/skill-tuning in your project. Claude Code loads it when a task matches its description.

How do I install Skill Tuning in Codex?

Run `npx skills add catlog22/Claude-Code-Workflow --skill skill-tuning -a codex`. Or copy the skill folder (.claude/skills/skill-tuning in catlog22/Claude-Code-Workflow) into .agents/skills/skill-tuning in your project. Codex loads it when a task matches its description.

Can I use Skill Tuning in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add catlog22/Claude-Code-Workflow --skill skill-tuning -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/skill-tuning, .gemini/skills/skill-tuning, .github/skills/skill-tuning and .opencode/skills/skill-tuning in your project.

What does Skill Tuning need to run?

SKILL.md names no scripts, command-line tools or credentials: Skill Tuning is instructions for the agent only. Its frontmatter pre-approves these tools: Agent, AskUserQuestion, Read, Write, Bash, Glob, Grep, mcp__ace-tool__search_context.

Does Skill Tuning access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Skill Tuning safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Skill Tuning use?

Skill Tuning is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Skill Tuning use?

About 1.7k tokens (SKILL.md is roughly 6.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Skill Tuning?

Skills that share tags, products or a category with Skill Tuning: Orca CLI (stablyai/orca, 87k stars), Paseo Advisor Second Opinion (getpaseo/paseo, 20k stars), O2 Review Loop (openobserve/openobserve, 22k stars) and Paseo Committee (getpaseo/paseo, 20k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Skill Tuning?

catlog22 (a GitHub user) maintains it in catlog22/Claude-Code-Workflow, which has 2,130 GitHub stars. The repository holds 82 skills in this directory. The repository was last updated on June 18, 2026.

Source: catlog22/Claude-Code-Workflow on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.