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agent-mux

shalomb/agent-skills/skills/agent-mux/SKILL.md

Use when a TODO.md backlog must be burned down by multiple agents working concurrently in isolated git worktrees. Requires both an existing TODO.md and worktree isolation — without those, use `dispatching-parallel-agents` for in-session parallel tasks, or a `*-sub-agent` skill to delegate a single task. This is the top-level orchestrator: it composes those skills into waves, handling cross-task dependency analysis, model spread across Ralph/Bart, and feedback accumulation between waves. Triggers on: 'agent-mux', 'wave of agents', 'multi-agent orchestration', 'burn down the backlog in parallel'.

Skill3 starsChanged 42 days ago

What's in it

  1. agent-mux — Multi-Agent Orchestrator
  2. What agent-mux adds
  3. Orchestrator role and boundaries
  4. Step 1 — Dependency Analysis
  5. Step 2 — Create Worktrees
  6. Step 3 — Model Spread
  7. Step 4 — Write Agent Artifacts
  8. Step 5 — Launch Agents
  9. Claude sub-agent (preferred for focused features, quota-safe)
  10. Gemini sub-agent (large refactors, complex reasoning)
  11. Step 6 — Bart Review Loop
  12. Orchestrator actions on verdict
  13. Step 7 — Feedback Accumulation
  14. Supervisor Mode
  15. Adaptive polling — use supervisor-watch.py
  16. Supervisor state file
  17. Quota management
  18. Pitfalls (hard-won, not obvious)
  19. gemini /tmp/ is not writable
  20. tmux send-keys truncates long commands
  21. Monitor blocks if gemini runs in foreground
  22. gemini model names
  23. Bart gh pr merge fails with "main is already used by worktree"
  24. poll.py reads stale pane scrollback
  25. Wave Bart: pass --worktree explicitly
  26. Bart uses --auto and PR stays open
  27. gh pr review --comment vs gh pr comment
  28. Verdict filename case
  29. Pre-existing failures
  30. Bart going rogue (acting as Ralph)
---
name: agent-mux
description: >
  Use when a TODO.md backlog must be burned down by multiple agents working
  concurrently in isolated git worktrees. Requires both an existing TODO.md and
  worktree isolation — without those, use `dispatching-parallel-agents` for
  in-session parallel tasks, or a `*-sub-agent` skill to delegate a single task.
  This is the top-level orchestrator: it composes those skills into waves,
  handling cross-task dependency analysis, model spread across Ralph/Bart, and
  feedback accumulation between waves. Triggers on: 'agent-mux', 'wave of
  agents', 'multi-agent orchestration', 'burn down the backlog in parallel'.
---

# agent-mux — Multi-Agent Orchestrator

Compose `ralph`, `bart`, `gemini-sub-agent`, `pi-sub-agent`, and
`dispatching-parallel-agents` skills into a wave-based build+review pipeline
across git worktrees.

**Read these skills first — agent-mux builds on them, not instead of them:**
- `ralph` — TDD build loop
- `bart` — adversarial review loop
- `claude-sub-agent` — launching + monitoring Claude CLI agents
- `gemini-sub-agent` — launching + monitoring gemini agents
- `pi-sub-agent` — launching + monitoring pi agents
- `dispatching-parallel-agents` — independent task dispatch principles

## What agent-mux adds

The unique value not covered by the above skills:

1. **Dependency analysis** — which TODO items touch shared files and must be sequenced
2. **Wave ordering** — groups of independent items dispatched in parallel waves
3. **Model spread** — which model for which task character
4. **Ralph→Bart rejection loop** — feedback corral into planning branch `FEEDBACK.md`
5. **Hard-won pitfalls** — specific failure modes discovered in practice

---

## Orchestrator role and boundaries

The orchestrator's job is **routing, not implementing**.

| Orchestrator DOES | Orchestrator does NOT do |
|---|---|
| Run `dispatch.py` commands | Write code or tests |
| Read agent verdicts | Fix failing tests directly |
| Apply state transitions | Commit or push code |
| Triage Bart rejections into TODO entries | Rebase branches manually |
| Decide wave ordering | Investigate root causes inline |
| Update TODO.md status | Open PRs |

If you find yourself running `git`, `uv run pytest`, or editing source files — **stop**.
Write a TODO entry and dispatch an agent instead.

**Ralph owns:** implementing, testing, committing, pushing, opening PRs, rebasing onto main.
**Bart owns:** reviewing diff, running tests, posting comments, merging or rejecting.
**Orchestrator owns:** dispatch decisions, state tracking, triage of verdicts, wave sequencing.

## Step 1 — Dependency Analysis

Before creating worktrees, identify which TODO items touch the same source files.
Items touching shared hub files (e.g. `cli/utils.py`, `cli/main.py`, `api/client.py`)
**must go in the same branch or be sequenced**, not parallelised.

```bash
# Find which source files each TODO item would touch
# Then check fan-out: files imported by 3+ others are hubs
grep -rn "from terrapyne.cli.utils\|from .utils" src/
```

Group items into **waves**:
- Wave 0: independent S-effort items with no shared files (quick wins, unblock CI)
- Wave 1: parallel independent features (one worktree each)
- Wave 2: items that depend on Wave 1 landing to main first

See `references/dependency-analysis.md` for a worked example.

---

## Step 2 — Create Worktrees

```bash
git worktree add .worktrees/<name> -b <branch> origin/main
cd .worktrees/<name> && <setup-command>  # uv sync, npm install, etc.
```

Rules:
- Always base off `origin/main` — never off another feature branch
- Verify `.worktrees/` is gitignored: `git check-ignore -q .worktrees && echo ok`
- One worktree per independent feature — never share worktrees between agents

---

## Step 3 — Model Spread

| Task character | Model | Via |
|---|---|---|
| Focused feature, new API/CLI commands | `haiku` | claude-sub-agent |
| Bug fixes, correctness-sensitive changes | `sonnet` | claude-sub-agent |
| Large refactor, structural surgery, complex reasoning | `gemini-3-flash-preview` | gemini-sub-agent |
| Bart review (all) | `gemini-3-flash-preview` | gemini-sub-agent |
| Fallback when Gemini quota exhausted | `sonnet` | claude-sub-agent |
| Re-run after rejection | same model as original | consistency |

Claude CLI invocation for headless use:
```bash
claude --print --output-format stream-json \
  --dangerously-skip-permissions \
  --no-session-persistence \
  --model haiku \
  -p @/tmp/ralph-<feature>.md \
  > /tmp/claude-<feature>.jsonl 2>&1 &
```
Monitor: `python3 {SKILLS_DIR}/claude-sub-agent/scripts/monitor.py /tmp/claude-<feature>.jsonl`
Poll:    `python3 {SKILLS_DIR}/claude-sub-agent/scripts/poll.py "$TARGET" --interval 30`

See `references/model-spread.md` for the rationale.

---

## Step 4 — Write Agent Artifacts

**Use `dispatch.py` — do not write prompts or launch scripts by hand.**

```bash
DISPATCH={SKILLS_DIR}/agent-mux/scripts/dispatch.py

# Ralph: implement a task
python3 $DISPATCH --repo /path/to/repo ralph <TASK_ID> --pane <session:window.pane>

# Bart: review a PR
python3 $DISPATCH --repo /path/to/repo bart <TASK_ID> --pr <N> --pane <session:window.pane>

# Show state
python3 $DISPATCH --repo /path/to/repo status

# List TODO items
python3 $DISPATCH --repo /path/to/repo list
```

`dispatch.py` automatically:
- Reads the task detail section from TODO.md and uses it verbatim as the prompt
- Creates the worktree off `origin/main` (`fix/<task-id>` branch)
- Selects model from `AGENT_CONFIG` in the script (edit once, applies everywhere)
- Writes `/tmp/ralph-<id>.md` (or `bart-<id>.md`) and `/tmp/launch-<agent>-<id>.sh`
- Sends the launch script to the tmux pane
- Updates dispatch state (`.worktrees/planning/.dispatch-state.json`)

For persona injection and model overrides, edit `AGENT_CONFIG` at the top of `dispatch.py`.
See `scripts/dispatch.py` for the full config and template details.

---

## Step 5 — Launch Agents

**Always write a shell script. Never inline long commands in tmux send-keys.**

### Claude sub-agent (preferred for focused features, quota-safe)

```bash
cat > /tmp/launch-<feature>.sh << 'SCRIPT'
#!/bin/bash
cd /path/to/worktree
> /tmp/claude-<feature>.jsonl
claude --print --output-format stream-json \
  --dangerously-skip-permissions \
  --no-session-persistence \
  --model haiku \
  -p @/tmp/ralph-<feature>.md \
  >> /tmp/claude-<feature>.jsonl 2>&1 &
echo "PID: $!"
python3 {SKILLS_DIR}/claude-sub-agent/scripts/monitor.py \
  /tmp/claude-<feature>.jsonl
SCRIPT
chmod +x /tmp/launch-<feature>.sh
tmux send-keys -t "$TARGET" "bash /tmp/launch-<feature>.sh" Enter
```

### Gemini sub-agent (large refactors, complex reasoning)

```bash
cat > /tmp/launch-<feature>.sh << 'SCRIPT'
#!/bin/bash
cd /path/to/worktree
> /tmp/gemini-<feature>.jsonl
gemini -y --output-format stream-json --model gemini-3-flash-preview \
  -p @/tmp/ralph-<feature>.md \
  >> /tmp/gemini-<feature>.jsonl 2>&1 &
echo "PID: $!"
python3 {SKILLS_DIR}/gemini-sub-agent/scripts/monitor.py \
  /tmp/gemini-<feature>.jsonl
SCRIPT
chmod +x /tmp/launch-<feature>.sh
tmux send-keys -t "$TARGET" "bash /tmp/launch-<feature>.sh" Enter
```

For pi agents, same background-then-monitor pattern with `pi-monitor.py`.

See `claude-sub-agent`, `gemini-sub-agent`, and `pi-sub-agent` skills for full invocation flags.

---

## Step 6 — Bart Review Loop

After Ralph opens a PR, launch Bart as a Claude sub-agent using the `bart` skill.
Bart handles both evidence gathering and the merge/reject decision.

```bash
cat > /tmp/bart-<feature>.md << 'EOF'
# Bart review — PR #<N>

## Working directory
You are in /path/to/worktree. Do NOT cd elsewhere for any command.

## Context
- PR: #<N> on <owner>/<repo>
- Branch: <branch>
- Task: <copy one-line description from TODO.md>
- Pre-existing failures to ignore: <list from baseline run, or "none">

## Your job
Use the `bart` skill. Review PR #<N> in PR review mode:
1. Read the diff: `gh pr diff <N>`
2. Run the tests
3. Apply the adversarial checklist
4. Post inline comments for BLOCKERs
5. Write verdict to /tmp/bart-verdict-<feature>.md
6. If APPROVED: merge with `gh pr merge <N> --squash --delete-branch`
   If REJECTED: leave open, write issues, do NOT merge
EOF
```

Launch via claude-sub-agent with bart persona appended:
```bash
cat > /tmp/launch-bart-<feature>.sh << 'SCRIPT'
#!/bin/bash
cd /path/to/worktree
> /tmp/claude-bart-<feature>.jsonl
claude --print --output-format stream-json \
  --dangerously-skip-permissions \
  --no-session-persistence \
  --model sonnet \
  --append-system-prompt @{SKILLS_DIR}/bart-adversarial-reviewer/references/bart.md \
  -p @/tmp/bart-<feature>.md \
  >> /tmp/claude-bart-<feature>.jsonl 2>&1 &
echo "PID: $!"
python3 {SKILLS_DIR}/claude-sub-agent/scripts/monitor.py \
  /tmp/claude-bart-<feature>.jsonl
SCRIPT
chmod +x /tmp/launch-bart-<feature>.sh
tmux send-keys -t "$TARGET" "bash /tmp/launch-bart-<feature>.sh" Enter
```

### Orchestrator actions on verdict

| Outcome | Bart action | Orchestrator action |
|---|---|---|
| APPROVED | Posts inline comments (if any MINOR), merges PR | Accumulate MINOR notes to FEEDBACK.md |
| REJECTED | Posts BLOCKER comments, writes verdict, does NOT merge | Triage; write targeted ralph-v2 prompt; relaunch Ralph |

---

## Step 7 — Feedback Accumulation

After all Barts in a wave finish, accumulate into the **planning branch** `FEEDBACK.md`:

```bash
bash /tmp/accumulate-feedback.sh
```

Triage before writing next wave prompts:
- **Critical rejections** → write targeted `ralph-<feature>-v<N>.md`, re-run Ralph
- **Systemic patterns** (same observation across 3+ PRs) → add to next wave guardrails
- **Non-critical one-offs** → acknowledge in FEEDBACK.md, defer

**Never commit feedback files to feature branches or main.**
`planning/FEEDBACK.md` is the only accumulator — committed to the planning branch only.

---

## Supervisor Mode

For large backlogs (6+ branches), run a continuous supervisor loop:

1. Write `/tmp/supervisor-poll.sh` — checks all JSONL files for completion
2. Poll every ~5 min: `watch -n 300 bash /tmp/supervisor-poll.sh`
3. On Ralph completion → launch Bart immediately on the same pane
4. On Bart APPROVED → run `accumulate-feedback.sh`, update `supervisor-state.md`
5. On Bart REJECTED → triage issues, write targeted `ralph-<feature>-v<N>.md`, relaunch Ralph
6. Wave 2 branches launch as Wave 1 panes free up
7. Update `agent-mux` skill with new learnings after each wave

### Adaptive polling — use supervisor-watch.py

Never use `sleep N && check` loops. Use `references/supervisor-watch.py` instead:

```bash
# Edit AGENTS list at top of script to match current wave
python3 {SKILLS_DIR}/agent-mux/references/supervisor-watch.py
```

Adaptive sleep principle:
- **Young agent** (just started, file growing fast) → poll every 5s
- **Mature agent** (file growing slowly) → back off toward 60s
- **Stalled agent** (file not growing for >120s) → flag immediately, don't wait
- **Terminal event** (`agent_end` or `result` line) → wake immediately, report
- **Sleep interval** = `min(poll_interval for all active agents)` — the most urgent agent drives the cadence

To add/remove agents from the watch list, edit the `AGENTS = [...]` block at the top of the script. Copy the script to `/tmp/` and edit there — don't modify the skill copy.

### Supervisor state file

Keep `/tmp/supervisor-state.md` updated:
```markdown
| Branch | PR | Ralph | Bart | Action |
|---|---|---|---|---|
| fix/quick-wins | #32 | v2 running | REJECTED v1 | waiting |
```

### Quota management

Gemini has a per-session quota. If you get `exhausted capacity` errors:
- Switch pending gemini agents to `claude-sub-agent` with `--model haiku`
- Update launch scripts: replace `gemini -y --output-format stream-json` with
  `claude --print --output-format stream-json --dangerously-skip-permissions --no-session-persistence --model haiku`
- Monitor with `claude-sub-agent/scripts/monitor.py` instead of `gemini-sub-agent/scripts/monitor.py`
- Gemini quota resets — switch back before Wave 2 if preferred

## Pitfalls (hard-won, not obvious)

### gemini /tmp/ is not writable
Gemini runs in a sandboxed context and can only write to `~/.gemini/tmp/<worktree-name>/`.
Tell Bart to write issues there, not `/tmp/`.
Accumulate from `~/.gemini/tmp/<wt>/bart-issues-*.md`.

### tmux send-keys truncates long commands
Any command > ~200 chars sent via `tmux send-keys` gets silently cut.
Always write to a script file first, then `tmux send-keys "bash /tmp/script.sh" Enter`.

### Monitor blocks if gemini runs in foreground
If you send `gemini ... > file &` and then the monitor command in the same pane,
but the script runs gemini in foreground, the pane is blocked.
Always background gemini (`&`) before launching the monitor in the script.

### gemini model names
Only `gemini-2.5-flash` and `gemini-2.5-pro` work via API key.
`gemini-3-flash-preview` works via the `gemini` CLI with user auth (OAuth).
Test first: `echo "hi" | gemini -y -p "say hello" --model <name>` before wiring into agents.

### Bart `gh pr merge` fails with "main is already used by worktree"
When Bart runs from a worktree, `gh pr merge --delete-branch` tries to checkout `main`
locally to fast-forward — but `main` is locked by the primary worktree.
Fix in Bart prompt: tell Bart to use `gh pr merge --squash` without `--delete-branch`,
or pass `--repo owner/repo` explicitly. Remote branch deletion still works.
Add to Bart prompt: `gh pr merge <N> --squash --repo <owner>/<repo>` (omit `--delete-branch`).

### poll.py reads stale pane scrollback
`poll.py` reads tmux pane text for `✅ DONE`. After multiple monitor sessions in the
same pane, old DONE boxes trigger false positives. Monitor the JSONL directly instead:
```bash
python3 {SKILLS_DIR}/claude-sub-agent/scripts/monitor.py /tmp/claude-bart-<id>.jsonl --no-clear
```
Or check the verdict file: `ls -lt /tmp/bart-verdict-*.md | head -1`

### Wave Bart: pass --worktree explicitly
When dispatching Bart after a `wave` command, the default worktree slug (`fix/<id>`)
won't match the wave worktree (`fix/<id1>-<id2>-...`). Always pass `--worktree`:
```bash
python3 dispatch.py bart D2 --pr <N> --worktree /path/to/repo/.worktrees/d2-d3-d4 --pane <pane>
```
To triage all wave items after Bart approves, use `signal` in a loop:
```bash
for id in D2 D3 D4; do python3 dispatch.py signal $id bart_approved; done
```

### Bart uses --auto and PR stays open
When branch protection requires CI, Bart correctly sets `gh pr merge --auto`.
The PR merges automatically once CI clears — wait, don't intervene.
Check status: `gh pr view <N> --json state,mergedAt`.
If open after 5 min, check CI: `gh pr checks <N>`. Do NOT merge manually.

### `gh pr review --comment` vs `gh pr comment`
`gh pr review --comment "text"` posts an inline diff comment and requires `--file` + `--line`.
For a standalone PR summary comment, use `gh pr comment <N> --body-file /tmp/verdict.md`.
Bart often confuses these. The bart skill documents the correct syntax; ensure the prompt
tells Bart to write verdict to a file first, then use `--body-file`.

### Verdict filename case
Bart sometimes writes `/tmp/bart-verdict-B2.md` (uppercase) instead of the lowercase convention.
`dispatch.py triage` now checks both cases automatically.

### Pre-existing failures
Always run `uv run pytest tests/ -x -q` on `origin/main` before any wave.
Note which tests fail. Bart must not flag pre-existing failures as new regressions.
Include the list explicitly in every Bart prompt.

### Bart going rogue (acting as Ralph)
Gemini's Bart sometimes starts implementing fixes instead of just reviewing.
Add to Bart persona: `ABSOLUTE RULE: DO NOT write code. DO NOT edit source files.
You are a reviewer only. If you find yourself editing .py files — STOP immediately.`

### Module-level Console() captures at import time
Rich `Console()` captures `sys.stdout`/`sys.stderr` at instantiation.
In tests with `CliRunner`, the real stdout is replaced — module-level consoles miss it.
Fix: instantiate `Console(stderr=True)` inside functions, or use `force_terminal=False`.
This affects `rich_tables.py` and any module with a top-level `console = Console()`.

### paginate_with_meta mock gap
A common systemic failure: `TFCClient.paginate_with_meta` is not mocked in fixtures.
When a refactor changes code paths to call it, previously-passing tests break.
Fix in conftest: `mock_client.paginate_with_meta.return_value = (iter([]), 0)`
Include this in every project-specific guardrails template.

---

## References

- `references/dependency-analysis.md` — worked example of hub-file dependency mapping
- `references/model-spread.md` — model selection rationale
- `references/guardrails-template.md` — standard guardrails to prepend to all prompts
- `references/accumulate-feedback.sh` — session accumulator script

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