Agentops swarm

Spawn isolated agents for parallel task execution with Codex session agents. Fresh context per agent (Ralph Wiggum pattern). Triggers: "swarm", "spawn agents", "parallel work", "run in parallel", "parallel execution".

install
source · Clone the upstream repo
git clone https://github.com/boshu2/agentops
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/boshu2/agentops "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills-codex/swarm" ~/.claude/skills/boshu2-agentops-swarm-facfe1 && rm -rf "$T"
manifest: skills-codex/swarm/SKILL.md
source content

$swarm

Spawn isolated agents to execute tasks in parallel with Codex session agents. Fresh context per agent.

Integration modes:

  • Via
    $crank
    - crank creates waves from beads and invokes
    $swarm
    for each wave
  • Standalone - direct invocation for ad-hoc parallel work

Requires a multi-agent runtime. Prefer runtime-native Codex session agents. If spawning is unavailable, fall back to sequential execution in the current session.

Architecture

Lead (this session)
  |
  +-> Identify the wave: tasks with no blockers
  +-> Build explicit file manifests
  +-> Pre-spawn conflict check (file ownership)
  +-> Spawn one worker per task
  +-> Wait for completion
  +-> Validate changes and close or retry tasks
  +-> Repeat if more work remains

Runtime preference:

  1. If
    spawn_agent
    is available, use Codex session agents.
  2. If your runtime exposes
    agent_type
    roles, use
    worker
    for execution and
    explorer
    for file discovery.
  3. If spawning is unavailable, execute sequentially and keep the same file-manifest contract.

Execution

Given

$swarm
:

Local Mode

In local mode, keep the same file-manifest contract and execute workers sequentially when the runtime cannot spawn agents.

Step 0: Detect Multi-Agent Capability

Check whether the runtime can spawn agents. If not:

WARN: Multi-agent not available. Executing tasks sequentially in this session.

Fall back to serial execution within the current session.

Step 1: Ensure Tasks Exist

Tasks come from one of:

  • bd ready
    output
  • An explicit task list from
    $crank
  • A user-provided description that you decompose first

Each task needs:

  • id
    - unique identifier
  • subject
    - what to do
  • description
    - detailed instructions
  • files
    - file manifest for worker ownership
  • validation
    - how to verify completion
  • metadata.issue_type
    - the canonical task type used by the lead when tracking work

Step 1.5: Populate File Manifests

If any task is missing a file manifest, spawn explorer agents to identify files. Use the explorer role if your runtime exposes roles:

spawn_agent(message="You are explorer-1.

Task: Given this task, identify all files that will need to be created or modified.
Return a JSON array of file paths only.
Task subject: <subject>
Task description: <description>")

Inject the discovered file list back into the task manifest before spawning workers.

Step 1.6: Advisory Bead Clustering

When tasks come from bd and

scripts/bd-cluster.sh
exists, run
scripts/bd-cluster.sh --json 2>/dev/null || true
before Step 2. Summarize any clusters as consolidation hints only; never run
--apply
here, and keep Step 2's file-manifest and dependency gates authoritative.

Step 2: Pre-Spawn Conflict Check

Pre-Spawn Friction Gates: Before spawning workers, execute all 5 friction gates (base sync, file manifest, dependency graph, misalignment breaker, wave cap). See

references/pre-spawn-friction-gates.md
.

wave_tasks = [tasks with status=pending and no blockers]
all_files = {}
for task in wave_tasks:
    for f in task.files:
        if f in all_files:
            CONFLICT: f claimed by both all_files[f] and task.id
        all_files[f] = task.id

On conflict:

  • Serialize conflicting workers into separate sub-waves
  • Do not spawn overlapping file manifests into the same shared-worktree wave

Display an ownership table before spawning:

File Ownership Map (Wave N):
┌─────────────────────────────┬──────────┬──────────┐
│ File                        │ Owner    │ Conflict │
├─────────────────────────────┼──────────┼──────────┤
│ src/auth/middleware.go      │ task-1   │          │
│ src/api/routes.go           │ task-2   │          │
└─────────────────────────────┴──────────┴──────────┘
Conflicts: 0

Step 3: Spawn Workers

Build one worker prompt per task. Each worker gets a single assignment and a single file manifest.

spawn_agent(message="You are worker-<task-id>.

Assignment: <subject>

<description>

FILE MANIFEST (files you are permitted to modify):
<list of files>

Rules:
1. Stay within your assigned files
2. Run validation: <validation_cmd>
3. Write your result to .agents/swarm/results/<task-id>.json
4. Keep any message back to the lead short

Result file format:
On success:
{\"type\":\"completion\",\"issue_id\":\"<task-id>\",\"status\":\"done\",\"detail\":\"<one-line summary>\",\"artifacts\":[\"path/to/file1\"],\"worktreePath\":\"<absolute-worktree-path-or-empty>\"}

If blocked:
{\"type\":\"blocked\",\"issue_id\":\"<task-id>\",\"status\":\"blocked\",\"detail\":\"<reason>\",\"worktreePath\":\"<absolute-worktree-path-or-empty>\"}

Knowledge artifacts are in .agents/. See .agents/AGENTS.md for navigation.")

If your runtime supports

agent_type
, mark these as
worker
agents and keep any file-discovery agents as
explorer
.

Step 4: Wait and Collect Results

wait_agent(ids=["agent-id-1", "agent-id-2"])

Collect worker result files from

.agents/swarm/results/
.

If a worker needs a short correction, use:

send_input(id="agent-id-1", message="Validation failed. Fix the test failure and retry.")

If a worker stalls, use:

close_agent(id="agent-id-1")

Step 5: Validate Wave

For each worker result:

  1. PASS
    - accept changes
  2. FAIL
    - log failure, mark for retry, max 2 retries per task
  3. BLOCKED
    - escalate to the lead

After collecting results, run project-level tests appropriate to the wave.

If tests fail, identify which worker's changes caused the break and requeue only that work.

Step 6: Report Results

Output a wave summary with task status, files changed, and any retries.

Test File Naming Validation

When workers create test files, validate naming:

  • Go:
    <source>_test.go
    or
    <source>_extra_test.go
  • Python:
    test_<module>.py
    or
    <module>_test.py

Output Schema Size Guard

When 5+ workers share the same output schema, cache it to

.agents/swarm/output-schema.json
and reference it by path instead of inlining it everywhere.

Serial Fallback

If spawning is unavailable, execute tasks sequentially:

for task in wave_tasks:
    1. Read task details
    2. Implement changes
    3. Run validation
    4. Record result

This is slower but functionally identical.

Reference Documents