Agent skill

Skill Iter Tune

by catlog22 in catlog22/Claude-Code-Workflow

Iterative skill tuning via execute-evaluate-improve feedback loop.

MITAuto-check: notes

Install Skill Iter Tune

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

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

GitHub CLI
$ gh skill install catlog22/Claude-Code-Workflow skill-iter-tune --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-iter-tune .claude/skills/skill-iter-tune && 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-iter-tune
GitHub stars
2.1k
Token cost
~3.4k tokens
SKILL.md length
608 words
Files
9
Skills in repo
82
Repo updated
First seen
Licence
MIT

At a glance

Iterative skill tuning via execute-evaluate-improve feedback loop.

  • Works in 5 steps: Setup (one-time) → Execute Skill (per iteration) → Evaluate Quality (per iteration) → …
  • Skill iter tune
  • SKILL.md covers Architecture Overview, Key Design Principles, Interactive Preference… and Input Processing, plus 7 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Skill Iter Tune is an agent skill from catlog22/Claude-Code-Workflow. Iterative skill tuning via execute-evaluate-improve feedback loop. Uses ccw cli Claude to execute skill, Gemini to evaluate quality, and Agent to apply improvements. Iterates until quality threshold or max iterations. Triggers on "skill iter tune", "iterative skill tuning", "tune skill".

Its SKILL.md is about 3.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 11 other files (for example `phases/01-setup.md`, `phases/02-execute.md` and `phases/03-evaluate.md`).

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 iter tune
  • Iterative skill tuning

Example prompts

  • “skill iter tune”
  • “iterative skill tuning”
  • “tune skill”
  • “/skill-iter-tune”

Requirements

  • Pre-approved tools (allowed-tools): Skill, Agent, AskUserQuestion, TaskCreate, TaskUpdate, TaskList, Read, Write, Edit, Bash, Glob, Grep

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Setup (one-time)
  2. Execute Skill (per iteration)
  3. Evaluate Quality (per iteration)
  4. Apply Improvements (per iteration, skipped on termination)
  5. Final Report (one-time)

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:

    • Skill
    • Agent
    • AskUserQuestion
    • TaskCreate
    • TaskUpdate
    • TaskList
    • Read
    • Write
    • Edit
    • Bash

    …and 2 more on the same allowed-tools line.

    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 javascript).

    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 Iter Tune loads about 3.4k tokens when it runs. Until then it costs about 76 tokens; SKILL.md has 608 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~76
When it runs · the whole SKILL.md, loaded when a task matches
~3.4k

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: Skill, Agent, AskUserQuestion, TaskCreate, TaskUpdate, TaskList, Read, Write, Edit, Bash, Glob, Grep

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). 608 words, ~3,359 tokens.

Download SKILL.mdSave it as .claude/skills/skill-iter-tune/SKILL.md (or your agent's skills folder). This skill also uses 8 other files; get the full folder from GitHub.
name
skill-iter-tune
description
Iterative skill tuning via execute-evaluate-improve feedback loop. Uses ccw cli Claude to execute skill, Gemini to evaluate quality, and Agent to apply improvements. Iterates until quality threshold or max iterations. Triggers on "skill iter tune", "iterative skill tuning", "tune skill".
allowed-tools
Skill, Agent, AskUserQuestion, TaskCreate, TaskUpdate, TaskList, Read, Write, Edit, Bash, Glob, Grep

Skill Iter Tune

Iterative skill refinement through execute-evaluate-improve feedback loops. Each iteration runs the skill via Claude, evaluates output via Gemini, and applies improvements via Agent.

Architecture Overview

┌──────────────────────────────────────────────────────────────────────────┐
│  Skill Iter Tune Orchestrator (SKILL.md)                                 │
│  → Parse input → Setup workspace → Iteration Loop → Final Report         │
└────────────────────────────┬─────────────────────────────────────────────┘
                             │
         ┌───────────────────┼───────────────────────────────────┐
         ↓                   ↓                                   ↓
    ┌──────────┐      ┌─────────────────────────────┐     ┌──────────┐
    │ Phase 1  │      │  Iteration Loop (2→3→4)     │     │ Phase 5  │
    │ Setup    │      │  ┌─────┐  ┌─────┐  ┌─────┐ │     │ Report   │
    │          │─────→│  │ P2  │→ │ P3  │→ │ P4  │ │────→│          │
    │ Backup + │      │  │Exec │  │Eval │  │Impr │ │     │ History  │
    │ Init     │      │  └─────┘  └─────┘  └─────┘ │     │ Summary  │
    └──────────┘      │       ↑               │     │     └──────────┘
                      │       └───────────────┘     │
                      │    (if score < threshold    │
                      │     AND iter < max)         │
                      └─────────────────────────────┘
Chain Mode Extension
Chain Mode (execution_mode === "chain"):

Phase 2 runs per-skill in chain_order:
  Skill A → ccw cli → artifacts/skill-A/
       ↓ (artifacts as input)
  Skill B → ccw cli → artifacts/skill-B/
       ↓ (artifacts as input)
  Skill C → ccw cli → artifacts/skill-C/

Phase 3 evaluates entire chain output + per-skill scores
Phase 4 improves weakest skill(s) in chain

Key Design Principles

  1. Iteration Loop: Phases 2-3-4 repeat until quality threshold, max iterations, or convergence
  2. Two-Tool Pipeline: Claude (write/execute) + Gemini (analyze/evaluate) = complementary perspectives
  3. Pure Orchestrator: SKILL.md coordinates only — execution detail lives in phase files
  4. Progressive Phase Loading: Phase docs read only when that phase executes
  5. Skill Versioning: Each iteration snapshots skill state before execution
  6. Convergence Detection: Stop early if score stalls (no improvement in 2 consecutive iterations)

Interactive Preference Collection

javascript
// ★ Auto mode detection
const autoYes = /\b(-y|--yes)\b/.test($ARGUMENTS)

if (autoYes) {
  workflowPreferences = {
    autoYes: true,
    maxIterations: 5,
    qualityThreshold: 80,
    executionMode: 'single'
  }
} else {
  const prefResponse = AskUserQuestion({
    questions: [
      {
        question: "选择迭代调优配置:",
        header: "Tune Config",
        multiSelect: false,
        options: [
          { label: "Quick (3 iter, 70)", description: "快速迭代,适合小幅改进" },
          { label: "Standard (5 iter, 80) (Recommended)", description: "平衡方案,适合多数场景" },
          { label: "Thorough (8 iter, 90)", description: "深度优化,适合生产级 skill" }
        ]
      }
    ]
  })

  const configMap = {
    "Quick": { maxIterations: 3, qualityThreshold: 70 },
    "Standard": { maxIterations: 5, qualityThreshold: 80 },
    "Thorough": { maxIterations: 8, qualityThreshold: 90 }
  }
  const selected = Object.keys(configMap).find(k =>
    prefResponse["Tune Config"].startsWith(k)
  ) || "Standard"
  workflowPreferences = { autoYes: false, ...configMap[selected] }

  // ★ Mode selection: chain vs single
  const modeResponse = AskUserQuestion({
    questions: [{
      question: "选择调优模式:",
      header: "Tune Mode",
      multiSelect: false,
      options: [
        { label: "Single Skill (Recommended)", description: "独立调优每个 skill,适合单一 skill 优化" },
        { label: "Skill Chain", description: "按链序执行,前一个 skill 的产出作为后一个的输入" }
      ]
    }]
  });
  workflowPreferences.executionMode = modeResponse["Tune Mode"].startsWith("Skill Chain")
    ? "chain" : "single";
}

Input Processing

$ARGUMENTS → Parse:
  ├─ Skill path(s): first arg, comma-separated for multiple
  │   e.g., ".claude/skills/my-skill" or "my-skill" (auto-prefixed)
  │   Chain mode: order preserved as chain_order
  ├─ Test scenario: --scenario "description" or remaining text
  └─ Flags: --max-iterations=N, --threshold=N, -y/--yes

Execution Flow

⚠️ COMPACT DIRECTIVE: Context compression MUST check TodoWrite phase status. The phase currently marked in_progress is the active execution phase — preserve its FULL content. Only compress phases marked completed or pending.

Phase 1: Setup (one-time)

Read and execute: Ref: phases/01-setup.md

  • Parse skill paths, validate existence
  • Create workspace at .workflow/.scratchpad/skill-iter-tune-{ts}/
  • Backup original skill files
  • Initialize iteration-state.json

Output: workDir, targetSkills[], testScenario, initialized state

Iteration Loop
javascript
// Orchestrator iteration loop
while (true) {
  // Increment iteration
  state.current_iteration++;
  state.iterations.push({
    round: state.current_iteration,
    status: 'pending',
    execution: null,
    evaluation: null,
    improvement: null
  });

  // Update TodoWrite
  TaskUpdate(iterationTask, {
    subject: `Iteration ${state.current_iteration}/${state.max_iterations}`,
    status: 'in_progress',
    activeForm: `Running iteration ${state.current_iteration}`
  });

  // === Phase 2: Execute ===
  // Read: phases/02-execute.md
  // Single mode: one ccw cli call for all skills
  // Chain mode: sequential ccw cli per skill in chain_order, passing artifacts
  // Snapshot skill → construct prompt → ccw cli --tool claude --mode write
  // Collect artifacts

  // === Phase 3: Evaluate ===
  // Read: phases/03-evaluate.md
  // Construct eval prompt → ccw cli --tool gemini --mode analysis
  // Parse score → write iteration-N-eval.md → check termination

  // Check termination
  if (shouldTerminate(state)) {
    break;  // → Phase 5
  }

  // === Phase 4: Improve ===
  // Read: phases/04-improve.md
  // Agent applies suggestions → write iteration-N-changes.md

  // Update TodoWrite with score
  // Continue loop
}
Phase 2: Execute Skill (per iteration)

Read and execute: Ref: phases/02-execute.md

  • Snapshot skill → iteration-{N}/skill-snapshot/
  • Build execution prompt from skill content + test scenario
  • Execute: ccw cli -p "..." --tool claude --mode write --cd "${iterDir}/artifacts"
  • Collect artifacts
Phase 3: Evaluate Quality (per iteration)

Read and execute: Ref: phases/03-evaluate.md

  • Build evaluation prompt with skill + artifacts + criteria + history
  • Execute: ccw cli -p "..." --tool gemini --mode analysis
  • Parse 5-dimension score (Clarity, Completeness, Correctness, Effectiveness, Efficiency)
  • Write iteration-{N}-eval.md
  • Check termination: score >= threshold | iter >= max | convergence | error limit
Phase 4: Apply Improvements (per iteration, skipped on termination)

Read and execute: Ref: phases/04-improve.md

  • Read evaluation suggestions
  • Launch general-purpose Agent to apply changes
  • Write iteration-{N}-changes.md
  • Update state
Phase 5: Final Report (one-time)

Read and execute: Ref: phases/05-report.md

  • Generate comprehensive report with score progression table
  • Write final-report.md
  • Display summary to user

Phase Reference Documents (read on-demand when phase executes):

PhaseDocumentPurposeCompact
1phases/01-setup.mdInitialize workspace and stateTodoWrite 驱动
2phases/02-execute.mdExecute skill via ccw cli ClaudeTodoWrite 驱动 + 🔄 sentinel
3phases/03-evaluate.mdEvaluate via ccw cli GeminiTodoWrite 驱动 + 🔄 sentinel
4phases/04-improve.mdApply improvements via AgentTodoWrite 驱动 + 🔄 sentinel
5phases/05-report.mdGenerate final reportTodoWrite 驱动

Compact Rules:

  1. TodoWrite in_progress → 保留完整内容,禁止压缩
  2. TodoWrite completed → 可压缩为摘要
  3. 🔄 sentinel fallback → 若 compact 后仅存 sentinel 而无完整 Step 协议,立即 Read() 恢复
Show full SKILL.md (226 more words)Show less

Core Rules

  1. Start Immediately: First action is preference collection → Phase 1 setup
  2. Progressive Loading: Read phase doc ONLY when that phase is about to execute
  3. Snapshot Before Execute: Always snapshot skill state before each iteration
  4. Background CLI: ccw cli runs in background, wait for hook callback before proceeding
  5. Parse Every Output: Extract structured JSON from CLI outputs for state updates
  6. DO NOT STOP: Continuous iteration until termination condition met
  7. Single State Source: iteration-state.json is the only source of truth

Data Flow

User Input (skill paths + test scenario)
    ↓ (+ execution_mode + chain_order if chain mode)
    ↓
Phase 1: Setup
    ↓ workDir, targetSkills[], testScenario, iteration-state.json
    ↓
┌─→ Phase 2: Execute (ccw cli claude)
│   ↓ artifacts/ (skill execution output)
│   ↓
│   Phase 3: Evaluate (ccw cli gemini)
│   ↓ score, dimensions[], suggestions[], iteration-N-eval.md
│   ↓
│   [Terminate?]─── YES ──→ Phase 5: Report → final-report.md
│   ↓ NO
│   ↓
│   Phase 4: Improve (Agent)
│   ↓ modified skill files, iteration-N-changes.md
│   ↓
└───┘ next iteration

TodoWrite Pattern

javascript
// Initial state
TaskCreate({ subject: "Phase 1: Setup workspace", activeForm: "Setting up workspace" })
TaskCreate({ subject: "Iteration Loop", activeForm: "Running iterations" })
TaskCreate({ subject: "Phase 5: Final Report", activeForm: "Generating report" })

// Chain mode: create per-skill tracking tasks
if (state.execution_mode === 'chain') {
  for (const skillName of state.chain_order) {
    TaskCreate({
      subject: `Chain: ${skillName}`,
      activeForm: `Tracking ${skillName}`,
      description: `Skill chain member position ${state.chain_order.indexOf(skillName) + 1}`
    })
  }
}

// During iteration N
// Single mode: one score per iteration (existing behavior)
// Chain mode: per-skill status updates
if (state.execution_mode === 'chain') {
  // After each skill executes in Phase 2:
  TaskUpdate(chainSkillTask, {
    subject: `Chain: ${skillName} — Iter ${N} executed`,
    activeForm: `${skillName} iteration ${N}`
  })
  // After Phase 3 evaluates:
  TaskUpdate(chainSkillTask, {
    subject: `Chain: ${skillName} — Score ${chainScores[skillName]}/100`,
    activeForm: `${skillName} scored`
  })
} else {
  // Single mode (existing)
  TaskCreate({
    subject: `Iteration ${N}: Score ${score}/100`,
    activeForm: `Iteration ${N} complete`,
    description: `Strengths: ... | Weaknesses: ... | Suggestions: ${count}`
  })
}

// Completed — collapse
TaskUpdate(iterLoop, {
  subject: `Iteration Loop (${totalIters} iters, final: ${finalScore})`,
  status: 'completed'
})

Termination Logic

javascript
function shouldTerminate(state) {
  // 1. Quality threshold met
  if (state.latest_score >= state.quality_threshold) {
    return { terminate: true, reason: 'quality_threshold_met' };
  }
  // 2. Max iterations reached
  if (state.current_iteration >= state.max_iterations) {
    return { terminate: true, reason: 'max_iterations_reached' };
  }
  // 3. Convergence: ≤2 points improvement over last 2 iterations
  if (state.score_trend.length >= 3) {
    const last3 = state.score_trend.slice(-3);
    if (last3[2] - last3[0] <= 2) {
      state.converged = true;
      return { terminate: true, reason: 'convergence_detected' };
    }
  }
  // 4. Error limit
  if (state.error_count >= state.max_errors) {
    return { terminate: true, reason: 'error_limit_reached' };
  }
  return { terminate: false };
}

Error Handling

PhaseErrorRecovery
2: ExecuteCLI timeout/crashRetry once with simplified prompt, then skip
3: EvaluateCLI failsRetry once, then use score 50 with warning
3: EvaluateJSON parse failsExtract score heuristically, save raw output
4: ImproveAgent failsRollback from iteration-{N}/skill-snapshot/
Any3+ consecutive errorsTerminate with error report

Error Budget: Each phase gets 1 retry. 3 consecutive failed iterations triggers termination.

Coordinator Checklist

Pre-Phase Actions
  • Read iteration-state.json for current state
  • Verify workspace directory exists
  • Check error count hasn't exceeded limit
Per-Iteration Actions
  • Increment current_iteration in state
  • Create iteration-{N} subdirectory
  • Update TodoWrite with iteration status
  • After Phase 3: check termination before Phase 4
  • After Phase 4: write state, proceed to next iteration
Post-Workflow Actions
  • Execute Phase 5 (Report)
  • Display final summary to user
  • Update all TodoWrite tasks to completed

© 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 8 other files in .claude/skills/skill-iter-tune of catlog22/Claude-Code-Workflow.

  • SKILL.md
  • phases/01-setup.md
  • phases/02-execute.md
  • phases/03-evaluate.md
  • phases/04-improve.md
  • phases/05-report.md
  • specs/evaluation-criteria.md
  • templates/eval-prompt.md
  • templates/execute-prompt.md

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

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Debugging Executionsn8n-io/n8n207k—~2.6kAutomated safety check: PassCustom licence
LLM Evaluationdavila7/claude-code-templates32k13 repos~3.5kAutomated safety check: PassMIT

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Questions about Skill Iter Tune

What does Skill Iter Tune do?

Iterative skill tuning via execute-evaluate-improve feedback loop. Skill Iter Tune is an agent skill from catlog22/Claude-Code-Workflow. Iterative skill tuning via execute-evaluate-improve feedback loop.

When should I use Skill Iter Tune?

Skill Iter Tune fits situations like: skill iter tune; iterative skill tuning.

How do I install Skill Iter Tune in Claude Code?

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

How do I install Skill Iter Tune in Codex?

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

Can I use Skill Iter Tune 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-iter-tune -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-iter-tune, .gemini/skills/skill-iter-tune, .github/skills/skill-iter-tune and .opencode/skills/skill-iter-tune in your project.

What does Skill Iter Tune need to run?

SKILL.md names no scripts, command-line tools or credentials: Skill Iter Tune is instructions for the agent only. Its frontmatter pre-approves these tools: Skill, Agent, AskUserQuestion, TaskCreate, TaskUpdate, TaskList, Read, Write, Edit, Bash, Glob, Grep.

Does Skill Iter Tune 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 Iter Tune 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 Iter Tune use?

Skill Iter Tune 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 Iter Tune use?

About 3.4k tokens (SKILL.md is roughly 13k 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 Iter Tune?

Skills that share tags, products or a category with Skill Iter Tune: Execute (alirezarezvani/claude-skills, 28k stars), Arize Evaluator (github/awesome-copilot, 40k stars), Feedback (codewhale-hq/Codewhale, 41k stars) and Debugging Executions (n8n-io/n8n, 207k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Skill Iter Tune?

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.