Fleet Plan

Fleet Plan

Analyze work, pick the right fleet type, generate fleet.json + worker prompts. You plan — the fleet skills execute.

Step 0: Pick a fleet type

Use the built-in decision tree below. No external lookup needed.

Available fleet types

Decision tree

Q1: Can one agent handle this in a single session?
    YES → "No fleet needed — this fits in one session." STOP.

Q2: Are the tasks independent (no shared files, no shared state)?
    YES → worktree-fleet
    NO  → continue

Q3: Does the work need iteration with a reviewer making accept/iterate decisions?
    YES → iterative-fleet
    NO  → continue

Q4: Is the work a one-shot DAG (each agent runs to completion, dependencies via depends_on)?
    YES → dag-fleet
    NO  → continue

Q5: Is it an optimization loop with a fast eval harness?
    YES → autoresearch-fleet
    NO  → "Open multiple sessions — you're the orchestrator." STOP.

After picking, read ONLY the chosen fleet's SKILL.md for the full schema. Never read all fleet SKILL.md files — that wastes context.

Step 0.5: Verify prerequisites

Before generating the fleet, warn the user if any prerequisite is missing:

• bash >= 4.0 — required for associative arrays in launch.sh. On macOS: brew install bash and invoke with /opt/homebrew/bin/bash
• flock — Linux has it built-in. On macOS: brew install flock or use launch.sh --no-lock
• tmux — brew install tmux or apt install tmux
• jq — brew install jq or apt install jq

If the user is on macOS, mention these upfront so they don't hit launch failures.

Step 1: Ask where to place the fleet

Before analyzing anything, ask the user:

Where should I place the fleet root? (Press enter for default)

• If the user provides a path → use it
• If the user ignores, says "default", or doesn't respond → use: fleet-{YYYYMMDD-HHMMSS}-{fleet-name}/ in the current working directory

Always use absolute paths for the fleet root.

Step 2: Pick a fleet type

Use the table and decision tree above to match the user's task. Quick heuristic:

Q1: Can one agent handle this in a single session?
    YES → "No fleet needed — this fits in one session." STOP.

Q2: Does the decision tree match a fleet type?
    YES → Pick it. Read its SKILL.md for the full schema.

Q3: No match?
    → "Open multiple Claude Code sessions — you're the orchestrator." STOP.

After picking, read the chosen fleet's SKILL.md to get the exact fleet.json schema, worker type rules, and output path conventions. Do not guess schemas from memory — read the SKILL.md. STOP HERE.

**Show the user your reasoning.** Don't just pick a type — explain why.

## Step 3: Generate fleet.json

### Guardrail: unsupported provider or model

If the user requests a provider or model not in the valid lists below, you MUST either:
1. **Map to closest valid equivalent** and explicitly disclose: *"Fleet mode only supports [valid providers]. Mapped your request to [X] with [Y]."*
2. **Error and refuse to generate** if no reasonable mapping exists.

**Never silently emit invalid config.** The agent's own thinking recognizing a value is unsupported is not sufficient — the output must be valid.

### Valid models — ONLY use these

**Claude models (provider: "claude", default):**

| Alias | Full ID | When to use |
|-------|---------|-------------|
| `sonnet` | `claude-sonnet-4-6` | Default for most workers |
| `opus` | `claude-opus-4-6` | Complex reasoning, architectural review, large-context synthesis |
| `haiku` | `claude-haiku-4-5` | Cheap/fast — validators, linters, simple checks |

**Codex models (provider: "codex"):**

| Model | When to use |
|-------|-------------|
| `gpt-5.5` | Latest flagship — strongest reasoning, use when available |
| `gpt-5.4` | Flagship — strong reasoning, reliable default |
| `gpt-5.4-mini` | Fast/cheap — validators, simple tasks |
| `gpt-5.3-codex` | Coding-focused (migrating to gpt-5.4) |

**Default:** `sonnet` for Claude workers, `haiku` for fallback_model.
**Only use `opus`** when the task clearly needs it. Cost difference is significant.
**Use codex** when the user requests it or the task benefits from OpenAI models (e.g. research with web search via codex).

### Model family guidance

When the user says "use the [family]" without per-worker specifics, assign models by worker role:

**Claude family (provider: "claude"):**
| Worker role | Model | Why |
|-------------|-------|-----|
| Synthesis, architecture, complex reasoning | `opus` | Highest intelligence, largest context |
| General workers, researchers, builders | `sonnet` | Default — best cost/performance |
| Validators, linters, simple checks | `haiku` | Cheap/fast |

**Pi / Kimi family (provider: "pi"):**
| Worker role | Model | reasoning_effort |
|-------------|-------|------------------|
| Synthesis, deep reasoning, complex analysis | `kimi-k2-thinking` | `high` (or `medium` if not deeply analytical) |
| General coding, research, builders | `kimi-for-coding` | `medium` |
| Simple checks, validators | `kimi-for-coding` | `low` |

**Codex / GPT family (provider: "codex"):**
| Worker role | Model | reasoning_effort |
|-------------|-------|------------------|
| Complex reasoning, flagship tasks | `gpt-5.5` | `high` |
| General workers, research | `gpt-5.4` | `medium` |
| Validators, simple tasks | `gpt-5.4-mini` | `medium` or `low` |

**Rule:** Match model capability to task complexity. Don't put `opus` on a linter or `haiku` on an architecture review.

### Budget guidelines

| Task complexity | max_budget_usd |
|----------------|---------------|
| One-line change, simple edit | 0.25 - 0.50 |
| Moderate task (new function, refactor one file) | 1.00 - 2.00 |
| Complex task (new feature, multi-file changes) | 3.00 - 5.00 |
| Large task (architectural change, full module) | 5.00 - 10.00 |

**Do NOT set max_turns.** It defaults to unlimited. Budget is the only limiter.

### Validation checklist (MUST complete before output)

Before writing fleet.json to disk, verify EVERY item:
- [ ] `type` field present at top level (`"worktree"`, `"dag"`, or `"iterative"`)
- [ ] `max_turns` is NOT set on any worker (omit entirely for unlimited)
- [ ] `reasoning_effort` only paired with `codex` or `pi` provider
- [ ] All provider/model values are from the valid lists above
- [ ] Status command template uses `<fleet-root>` path, not fleet name

### Worker type selection — CRITICAL

Pick the worker type based on what the worker **needs to output**, not what it reads:

| Worker role | Correct type | WRONG type | Why wrong |
|-------------|-------------|------------|-----------|
| Researcher (web + findings file) | `research` | `read-only` | read-only has no WebFetch/WebSearch and cannot Write |
| Synthesizer (reads inputs, writes synthesis) | `write` | `read-only` | read-only cannot Write — burns entire budget trying |
| Code builder (shell + files) | `code-run` | `write` | write has no Bash |
| Reviewer (reads code, runs tests, writes verdict) | `reviewer` | `write` | reviewer has full access (Bash, Edit, etc.) for verification |

**`read-only` CANNOT write files.** Only use it for workers whose output is captured from assistant messages in session.jsonl, not from output files. If a worker needs to save ANY file, use `write`, `research`, `code-run`, or `reviewer`.

### fleet.json rules by type

**All types:**
```json
{
  "fleet_name": "<descriptive-kebab-case-name>",
  "type": "<worktree|dag|iterative>",
  "config": {
    "max_concurrent": 3,
    "model": "sonnet",
    "fallback_model": "haiku",
    "provider": "claude",
    "reasoning_effort": ""
  },
  "workers": [...]
}

"type" is a required top-level field.

Provider fields (optional):

• config.provider — "claude" (default) or "codex". Per-worker override: worker.provider.
• config.reasoning_effort — "low", "medium", or "high" (codex only). Per-worker override: worker.reasoning_effort.
• When provider: "codex", the fallback_model field is ignored (codex has no fallback).
• When provider: "codex", max_budget_usd is NOT enforced per-worker (codex has no budget flag). Fleet-level cost tracking still works via token estimation.

worktree-fleet additions:

• Every worker MUST have target_files (array of file globs) and branch (unique branch name)
• No two workers can have overlapping target_files
• Default worker type: code-run (worktree workers need Bash for git commit)

iterative-fleet additions:

• Exactly one worker with type: "reviewer" (use depends_on to ensure it runs after builders)
• Must include stop_when block with at least max_iterations and reviewer_lgtm_count
• Workers have max_budget_per_iter instead of max_budget_usd

dag-fleet additions:

• Use depends_on: ["worker-id"] for dependency ordering
• Workers without dependencies run in parallel

Config → prompt sync rule

After ANY change to fleet.json (models, budgets, methodology, provider), you MUST regenerate ALL prompt.md files to match. Prompts are not automatically synced — stale prompts cause partial update propagation bugs.

Specifically verify in each prompt.md:

• Model name matches fleet.json
• Budget value matches fleet.json
• Methodology (MECE, citations, web search) matches user requirements
• Output path rules match fleet type

Step 4: Generate prompt.md for each worker

Create $FLEET_ROOT/workers/{id}/prompt.md for each worker. Every prompt MUST include:

1. Clear task description — what to do, in specific terms
2. Scope boundaries — what files to touch, what NOT to touch

Output path rules (CRITICAL — different per fleet type)

worktree-fleet:

• Do NOT include a "Save ALL output to output/" line. The worker edits files in its worktree at the real repo paths.
• MUST include at the end:

  When you are done, commit your changes on the current branch with a descriptive message.
  

dag-fleet:

• Workers that produce artifacts (research, summaries, matrices) → include:

  Save ALL output files to $FLEET_ROOT/workers/{id}/output/ — use absolute paths.
  

• Workers that edit repo files (code changes, creating docs at specific paths) → tell them the exact target path. Do NOT also mention output/. One destination only.

iterative-fleet:

• Same as dag-fleet: if the deliverable is a specific file, give the exact path. Do not also mention output/.

• Reviewer prompt MUST include verdict-writing instructions. Without these, the reviewer won't write a verdict file and the orchestrator defaults to iterate, wasting an iteration. Every reviewer prompt.md must end with:

  ## Writing your verdict
  
  1. Determine the current iteration number: list the `iterations/` directory and find the
     highest-numbered subdirectory that does NOT yet contain a `review.md`.
  2. Write your verdict to `iterations/<N>/review.md` (relative to your working directory).
     **Never use absolute paths.**
  3. The file MUST contain a line exactly like one of:
     - `verdict: lgtm`
     - `verdict: iterate`
     - `verdict: escalate`
  4. Below the verdict line, list **actionable fix instructions** per worker — not just
     what's wrong, but exactly where and how to fix it (file path, function name, what
     to change). 2-3 precise points per worker. The builder sees this on next iteration,
     so vague feedback wastes a cycle.
  

  This is non-negotiable. Fleet 02 burned an iteration because the reviewer prompt didn't specify where/how to write the verdict.

The rule: Never give a worker two destinations. One task = one output location. Conflicting instructions cause workers to write to the wrong place.

Step 5: Output to user

After generating everything, tell the user:

1. Plan summary — what each worker does (a table)
2. Fleet type chosen and why
3. Launch command:

   bash <path-to-fleet-skill>/scripts/launch.sh <fleet-root>
   

   For worktree-fleet, suggest --dry-run first.
4. Status command:

   bash <path-to-fleet-skill>/scripts/status.sh <fleet-root>
   

   <fleet-root> is the absolute path to the fleet directory. While status.sh can resolve fleet names, always give the user the absolute path to avoid confusion.

ALWAYS give the user the status command. This is mandatory.

Preferred defaults

• Implementation workers: codex gpt-5.3-codex, reasoning_effort: "medium"
• Reviewer workers: claude opus (claude-opus-4-6), reasoning_effort: "medium"
• max_turns: unset (unlimited)
• max_iterations (iterative-fleet stop_when): 10

Override only when user specifies different models/caps in the request.

Rationalizations to reject

$ARGUMENTS