Agent skill

Workflow Multi CLI Plan

by catlog22 in catlog22/Claude-Code-Workflow

Multi-CLI collaborative planning with codebase context gathering, iterative cross-verification, and execution handoff.

MITAuto-check: notesAgent Workflows

Install Workflow Multi CLI Plan

skills CLI
$ npx skills add catlog22/Claude-Code-Workflow --skill workflow-multi-cli-plan -a claude-code

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

GitHub CLI
$ gh skill install catlog22/Claude-Code-Workflow workflow-multi-cli-plan --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/workflow-multi-cli-plan .claude/skills/workflow-multi-cli-plan && 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
workflow-multi-cli-plan
GitHub stars
2.1k
Token cost
~3.9k tokens
SKILL.md length
352 words
Files
1
Skills in repo
82
Repo updated
First seen
Licence
MIT

At a glance

Multi-CLI collaborative planning with codebase context gathering, iterative cross-verification, and execution handoff.

  • Works in 5 steps: Context Gathering → Agent Delegation → Present Options → …
  • Tasks that involve Codebase knowledge for agents
  • SKILL.md covers Auto Mode, Core Responsibilities, synthesis.json Schema and TodoWrite Pattern, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Workflow Multi CLI Plan is an agent skill from catlog22/Claude-Code-Workflow. Multi-CLI collaborative planning with codebase context gathering, iterative cross-verification, and execution handoff.

Its SKILL.md is about 3.9k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Agent Workflows, covering Codebase knowledge for agents. 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

  • Tasks that involve Codebase knowledge for agents

Example prompts

  • “/workflow-multi-cli-plan”

Requirements

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

Workflow steps

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

  1. Context Gathering
  2. Agent Delegation
  3. Present Options
  4. User Decision
  5. Plan Generation & Execution Handoff

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
    • TodoWrite
    • Read
    • Write
    • Edit
    • Bash
    • Glob
    • Grep

    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 and json).

    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

Workflow Multi CLI Plan loads about 3.9k tokens when it runs. Until then it costs about 36 tokens; SKILL.md has 352 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~36
When it runs · the whole SKILL.md, loaded when a task matches
~3.9k

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, TodoWrite, 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). 352 words, ~3,865 tokens.

Download SKILL.mdSave it as .claude/skills/workflow-multi-cli-plan/SKILL.md (or your agent's skills folder).
name
workflow-multi-cli-plan
description
Multi-CLI collaborative planning with codebase context gathering, iterative cross-verification, and execution handoff.
allowed-tools
Skill, Agent, AskUserQuestion, TodoWrite, Read, Write, Edit, Bash, Glob, Grep

Multi-CLI Collaborative Planning

Auto Mode

When workflowPreferences.autoYes is true: Auto-approve plan, use recommended solution and execution method (Agent, Skip review).

Context Source: ACE semantic search + Multi-CLI analysis Output Directory: .workflow/.multi-cli-plan/{session-id}/ Default Max Rounds: 3 (convergence may complete earlier) CLI Tools: @cli-discuss-agent (analysis), @cli-lite-planning-agent (plan generation) Execution: Auto-hands off to workflow-lite-execute after plan approval

Orchestrator Boundary (CRITICAL)
  • ONLY command for multi-CLI collaborative planning
  • Manages: Session state, user decisions, agent delegation, phase transitions
  • Delegates: CLI execution to @cli-discuss-agent, plan generation to @cli-lite-planning-agent
Execution Flow
Phase 1: Context Gathering
   └─ ACE semantic search, extract keywords, build context package

Phase 2: Multi-CLI Discussion (Iterative, via @cli-discuss-agent)
   ├─ Round N: Agent executes Gemini + Codex + Claude
   ├─ Cross-verify findings, synthesize solutions
   ├─ Write synthesis.json to rounds/{N}/
   └─ Loop until convergence or max rounds

Phase 3: Present Options
   └─ Display solutions with trade-offs from agent output

Phase 4: User Decision
   ├─ Select solution approach
   ├─ Select execution method (Agent/Codex/Auto)
   ├─ Select code review tool (Skip/Gemini/Codex/Agent)
   └─ Route:
      ├─ Approve → Phase 5
      ├─ Need More Analysis → Return to Phase 2
      └─ Cancel → Save session

Phase 5: Plan Generation & Execution Handoff
   ├─ Generate plan.json + .task/*.json (via @cli-lite-planning-agent, two-layer output)
   ├─ Build executionContext with user selections and taskFiles
   └─ Execute via workflow-lite-execute
Agent Roles
AgentResponsibility
OrchestratorSession management, ACE context, user decisions, phase transitions, executionContext assembly
@cli-discuss-agentMulti-CLI execution (Gemini/Codex/Claude), cross-verification, solution synthesis, synthesis.json output
@cli-lite-planning-agentTask decomposition, two-layer output: plan.json (overview with task_ids[]) + .task/*.json (task files)

Core Responsibilities

Phase 1: Context Gathering

Session Initialization:

javascript
const sessionId = `MCP-${taskSlug}-${date}`
const sessionFolder = `.workflow/.multi-cli-plan/${sessionId}`
Bash(`mkdir -p ${sessionFolder}/rounds`)

ACE Context Queries:

javascript
const aceQueries = [
  `Project architecture related to ${keywords}`,
  `Existing implementations of ${keywords[0]}`,
  `Code patterns for ${keywords} features`,
  `Integration points for ${keywords[0]}`
]
// Execute via mcp__ace-tool__search_context

Context Package (passed to agent):

  • relevant_files[] - Files identified by ACE
  • detected_patterns[] - Code patterns found
  • architecture_insights - Structure understanding
Phase 2: Agent Delegation

Core Principle: Orchestrator only delegates and reads output — NO direct CLI execution. CLI calls MUST use Bash with run_in_background: true, wait for hook callback, do NOT use TaskOutput polling.

Agent Invocation:

javascript
Agent({
  subagent_type: "cli-discuss-agent",
  run_in_background: false,
  description: `Discussion round ${currentRound}`,
  prompt: `
## Input Context
- task_description: ${taskDescription}
- round_number: ${currentRound}
- session: { id: "${sessionId}", folder: "${sessionFolder}" }
- ace_context: ${JSON.stringify(contextPackage)}
- previous_rounds: ${JSON.stringify(analysisResults)}
- user_feedback: ${userFeedback || 'None'}
- cli_config: { tools: ["gemini", "codex"], mode: "parallel", fallback_chain: ["gemini", "codex", "claude"] }

## Execution Process
1. Parse input context (handle JSON strings)
2. Check if ACE supplementary search needed
3. Build CLI prompts with context
4. Execute CLIs (parallel or serial per cli_config.mode)
5. Parse CLI outputs, handle failures with fallback
6. Perform cross-verification between CLI results
7. Synthesize solutions, calculate scores
8. Calculate convergence, generate clarification questions
9. Write synthesis.json

## Output
Write: ${sessionFolder}/rounds/${currentRound}/synthesis.json

## Completion Checklist
- [ ] All configured CLI tools executed (or fallback triggered)
- [ ] Cross-verification completed with agreements/disagreements
- [ ] 2-3 solutions generated with file:line references
- [ ] Convergence score calculated (0.0-1.0)
- [ ] synthesis.json written with all Primary Fields
`
})

Read Agent Output:

javascript
const synthesis = JSON.parse(Read(`${sessionFolder}/rounds/${round}/synthesis.json`))
// Access top-level fields: solutions, convergence, cross_verification, clarification_questions

Convergence Decision:

javascript
if (synthesis.convergence.recommendation === 'converged') {
  // Proceed to Phase 3
} else if (synthesis.convergence.recommendation === 'user_input_needed') {
  // Collect user feedback, return to Phase 2
} else {
  // Continue to next round if new_insights && round < maxRounds
}
Phase 3: Present Options

Display solutions from synthesis.solutions[] showing: name, source CLIs, effort/risk, pros/cons, affected files (file:line). Also show cross-verification agreements/disagreements count.

Show full SKILL.md (144 more words)Show less
Phase 4: User Decision
javascript
AskUserQuestion({
  questions: [
    {
      question: "Which solution approach?",
      header: "Solution",
      multiSelect: false,
      options: solutions.map((s, i) => ({
        label: `Option ${i+1}: ${s.name}`,
        description: `${s.effort} effort, ${s.risk} risk`
      })).concat([
        { label: "Need More Analysis", description: "Return to Phase 2" }
      ])
    },
    {
      question: "Execution method:",
      header: "Execution",
      multiSelect: false,
      options: [
        { label: "Agent", description: "@code-developer agent" },
        { label: "Codex", description: "codex CLI tool" },
        { label: "Auto", description: "Auto-select based on complexity" }
      ]
    },
    {
      question: "Code review after execution?",
      header: "Review",
      multiSelect: false,
      options: [
        { label: "Skip", description: "No review" },
        { label: "Gemini Review", description: "Gemini CLI tool" },
        { label: "Codex Review", description: "Codex CLI: prompt-based code quality review (--mode analysis)" },
        { label: "Agent Review", description: "Current agent review" }
      ]
    }
  ]
})

Routing:

  • Approve + execution method → Phase 5
  • Need More Analysis → Phase 2 with feedback
  • Cancel → Save session for resumption

TodoWrite Update (Phase 4 Decision):

javascript
const executionLabel = userSelection.execution_method  // "Agent" / "Codex" / "Auto"

TodoWrite({ todos: [
  { content: "Phase 1: Context Gathering", status: "completed", activeForm: "Gathering context" },
  { content: "Phase 2: Multi-CLI Discussion", status: "completed", activeForm: "Running discussion" },
  { content: "Phase 3: Present Options", status: "completed", activeForm: "Presenting options" },
  { content: `Phase 4: User Decision [${executionLabel}]`, status: "completed", activeForm: "Decision recorded" },
  { content: `Phase 5: Plan Generation [${executionLabel}]`, status: "in_progress", activeForm: `Generating plan [${executionLabel}]` }
]})
Phase 5: Plan Generation & Execution Handoff

Step 1: Build Context-Package (Orchestrator responsibility):

javascript
const contextPackage = {
  solution: {
    name: selectedSolution.name,
    source_cli: selectedSolution.source_cli,
    feasibility: selectedSolution.feasibility,
    effort: selectedSolution.effort,
    risk: selectedSolution.risk,
    summary: selectedSolution.summary
  },
  implementation_plan: selectedSolution.implementation_plan,
  dependencies: selectedSolution.dependencies || { internal: [], external: [] },
  technical_concerns: selectedSolution.technical_concerns || [],
  consensus: {
    agreements: synthesis.cross_verification.agreements,
    resolved_conflicts: synthesis.cross_verification.resolution
  },
  constraints: userConstraints || [],
  task_description: taskDescription,
  session_id: sessionId
}
Write(`${sessionFolder}/context-package.json`, JSON.stringify(contextPackage, null, 2))

Step 2: Invoke Planning Agent:

javascript
Agent({
  subagent_type: "cli-lite-planning-agent",
  run_in_background: false,
  description: "Generate implementation plan",
  prompt: `
## Schema Reference
Execute: cat ~/.ccw/workflows/cli-templates/schemas/plan-overview-base-schema.json
Execute: cat ~/.ccw/workflows/cli-templates/schemas/task-schema.json

## Output Format: Two-Layer Structure
- plan.json: Overview with task_ids[] referencing .task/ files (NO tasks[] array)
- .task/TASK-*.json: Independent task files following task-schema.json

plan.json required: summary, approach, task_ids, task_count, _metadata (with plan_type)
Task files required: id, title, description, depends_on, convergence (with criteria[])
Task fields: files[].change (not modification_points), convergence.criteria (not acceptance), test (not verification)

## Context-Package (from orchestrator)
${JSON.stringify(contextPackage, null, 2)}

## Execution Process
1. Read plan-overview-base-schema.json + task-schema.json for output structure
2. Read project-tech.json and specs/*.md
3. Parse context-package fields:
   - solution: name, feasibility, summary
   - implementation_plan: tasks[], execution_flow, milestones
   - dependencies: internal[], external[]
   - technical_concerns: risks/blockers
   - consensus: agreements, resolved_conflicts
   - constraints: user requirements
4. Use implementation_plan.tasks[] as task foundation
5. Preserve task dependencies (depends_on) and execution_flow
6. Expand tasks with convergence.criteria (testable completion conditions)
7. Create .task/ directory and write individual TASK-*.json files
8. Generate plan.json with task_ids[] referencing .task/ files

## Output
- ${sessionFolder}/plan.json (overview with task_ids[])
- ${sessionFolder}/.task/TASK-*.json (independent task files)

## Completion Checklist
- [ ] plan.json has task_ids[] and task_count (NO embedded tasks[])
- [ ] .task/*.json files preserve task dependencies from implementation_plan
- [ ] Task execution order follows execution_flow
- [ ] Key_points reflected in task descriptions
- [ ] User constraints applied to implementation
- [ ] convergence.criteria are testable
- [ ] plan.json follows plan-overview-base-schema.json
- [ ] Task files follow task-schema.json
`
})

Step 3: Build executionContext:

javascript
const plan = JSON.parse(Read(`${sessionFolder}/plan.json`))
const taskFiles = plan.task_ids.map(id => `${sessionFolder}/.task/${id}.json`)

// Build executionContext (same structure as lite-plan)
executionContext = {
  planObject: plan,
  taskFiles: taskFiles,                              // Paths to .task/*.json files (two-layer format)
  explorationsContext: null,                          // Multi-CLI doesn't use exploration files
  explorationAngles: [],
  explorationManifest: null,
  clarificationContext: null,                         // Store user feedback from Phase 2 if exists
  executionMethod: userSelection.execution_method,    // From Phase 4
  codeReviewTool: userSelection.code_review_tool,     // From Phase 4
  originalUserInput: taskDescription,
  executorAssignments: null,
  session: {
    id: sessionId,
    folder: sessionFolder,
    artifacts: {
      explorations: [],                              // No explorations in multi-CLI workflow
      explorations_manifest: null,
      plan: `${sessionFolder}/plan.json`,
      task_dir: plan.task_ids ? `${sessionFolder}/.task/` : null,
      synthesis_rounds: Array.from({length: currentRound}, (_, i) =>
        `${sessionFolder}/rounds/${i+1}/synthesis.json`
      ),
      context_package: `${sessionFolder}/context-package.json`
    }
  }
}

Step 4: Hand off to Execution:

javascript
Skill({
  skill: "workflow-lite-execute",
  args: "--in-memory"
})
// executionContext is passed via global variable to workflow-lite-execute (Mode 1: In-Memory Plan)

synthesis.json Schema

json
{
  "round": 1,
  "solutions": [{
    "name": "Solution Name",
    "source_cli": ["gemini", "codex"],
    "feasibility": 0.85,
    "effort": "low|medium|high",
    "risk": "low|medium|high",
    "summary": "Brief analysis summary",
    "implementation_plan": {
      "approach": "High-level technical approach",
      "tasks": [
        {"id": "T1", "name": "Task", "depends_on": [], "files": [], "key_point": "..."}
      ],
      "execution_flow": "T1 → T2 → T3",
      "milestones": ["Checkpoint 1", "Checkpoint 2"]
    },
    "dependencies": {"internal": [], "external": []},
    "technical_concerns": ["Risk 1", "Blocker 2"]
  }],
  "convergence": {
    "score": 0.85,
    "new_insights": false,
    "recommendation": "converged|continue|user_input_needed"
  },
  "cross_verification": {
    "agreements": [],
    "disagreements": [],
    "resolution": "..."
  },
  "clarification_questions": []
}

TodoWrite Pattern

Initialization (Phase 1 start):

javascript
TodoWrite({ todos: [
  { content: "Phase 1: Context Gathering", status: "in_progress", activeForm: "Gathering context" },
  { content: "Phase 2: Multi-CLI Discussion", status: "pending", activeForm: "Running discussion" },
  { content: "Phase 3: Present Options", status: "pending", activeForm: "Presenting options" },
  { content: "Phase 4: User Decision", status: "pending", activeForm: "Awaiting decision" },
  { content: "Phase 5: Plan Generation", status: "pending", activeForm: "Generating plan" }
]})

Output File Structure

.workflow/.multi-cli-plan/{MCP-task-slug-YYYY-MM-DD}/
├── session-state.json          # Session tracking (orchestrator)
├── rounds/
│   ├── 1/synthesis.json        # Round 1 analysis (cli-discuss-agent)
│   ├── 2/synthesis.json        # Round 2 analysis (cli-discuss-agent)
│   └── .../
├── context-package.json        # Extracted context for planning (orchestrator)
├── plan.json                   # Plan overview with task_ids[] (NO embedded tasks[])
└── .task/                      # Independent task files
    ├── TASK-001.json            # Task file following task-schema.json
    ├── TASK-002.json
    └── ...

Error Handling

ErrorResolution
ACE search failsFall back to Glob/Grep for file discovery
Agent failsRetry once, then present partial results
CLI timeout (in agent)Agent uses fallback: gemini → codex → claude
No convergencePresent best options, flag uncertainty
synthesis.json parse errorRequest agent retry
User cancelsSave session for later resumption

Configuration

FlagDefaultDescription
--max-rounds3Maximum discussion rounds
--toolsgemini,codexCLI tools for analysis
--modeparallelExecution mode: parallel or serial
--auto-executefalseAuto-execute after approval

© 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

Just SKILL.md in .claude/skills/workflow-multi-cli-plan of catlog22/Claude-Code-Workflow.

Open the folder on GitHubat commit 07491b0

Compare with similar skills

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Categories

Questions about Workflow Multi CLI Plan

What does Workflow Multi CLI Plan do?

Multi-CLI collaborative planning with codebase context gathering, iterative cross-verification, and execution handoff. Workflow Multi CLI Plan is an agent skill from catlog22/Claude-Code-Workflow. Multi-CLI collaborative planning with codebase context gathering, iterative cross-verification, and execution handoff.

When should I use Workflow Multi CLI Plan?

Workflow Multi CLI Plan fits situations like: tasks that involve Codebase knowledge for agents.

How do I install Workflow Multi CLI Plan in Claude Code?

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

How do I install Workflow Multi CLI Plan in Codex?

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

Can I use Workflow Multi CLI Plan 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 workflow-multi-cli-plan -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/workflow-multi-cli-plan, .gemini/skills/workflow-multi-cli-plan, .github/skills/workflow-multi-cli-plan and .opencode/skills/workflow-multi-cli-plan in your project.

What does Workflow Multi CLI Plan need to run?

SKILL.md names no scripts, command-line tools or credentials: Workflow Multi CLI Plan is instructions for the agent only. Its frontmatter pre-approves these tools: Skill, Agent, AskUserQuestion, TodoWrite, Read, Write, Edit, Bash, Glob, Grep.

Does Workflow Multi CLI Plan 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 Workflow Multi CLI Plan 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 Workflow Multi CLI Plan use?

Workflow Multi CLI Plan 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 Workflow Multi CLI Plan use?

About 3.9k tokens (SKILL.md is roughly 15k 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 Workflow Multi CLI Plan?

Skills that share tags, products or a category with Workflow Multi CLI Plan: ccc Semantic Code Search (cocoindex-io/cocoindex-code, 2.7k stars), Context Engineering (abashev/vfs-s3, 106 stars), Repomix Codebase Packer (yamadashy/repomix, 29k stars) and Codebase Handbook Builder (Ruhan-Wang/Harness_Handbook, 332 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Workflow Multi CLI Plan?

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.