Run Wave
jpicklyk/task-orchestrator
Resolves ready MCP work items into a run plan, shows it to you, then executes it through the Workflow tool or direct subagent dispatch, with post-run verification.
Multi-agent swarm orchestration via RuFlo + Claude Code. An agent skill from LeoYeAI/openclaw-master-skills.
$ npx skills add LeoYeAI/openclaw-master-skills --skill flow-swarm -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills flow-swarm --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/flow-swarm .claude/skills/flow-swarm && rm -rf skills-srcUse ~/.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/
Install the "flow-swarm" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/flow-swarm into .claude/skills/flow-swarm/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "flow-swarm", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/flow-swarmType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add LeoYeAI/openclaw-master-skills --skill flow-swarm -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills flow-swarm --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/flow-swarm .agents/skills/flow-swarm && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "flow-swarm" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/flow-swarm into .agents/skills/flow-swarm/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "flow-swarm", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add LeoYeAI/openclaw-master-skills --skill flow-swarm -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills flow-swarm --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/flow-swarm .cursor/skills/flow-swarm && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "flow-swarm" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/flow-swarm into .cursor/skills/flow-swarm/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "flow-swarm", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/LeoYeAI/openclaw-master-skills.git --path skills/flow-swarm--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add LeoYeAI/openclaw-master-skills --skill flow-swarm -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills flow-swarm --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/flow-swarm .gemini/skills/flow-swarm && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "flow-swarm" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/flow-swarm into .gemini/skills/flow-swarm/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "flow-swarm", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install LeoYeAI/openclaw-master-skills flow-swarmInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add LeoYeAI/openclaw-master-skills --skill flow-swarm -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/flow-swarm .github/skills/flow-swarm && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "flow-swarm" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/flow-swarm into .github/skills/flow-swarm/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "flow-swarm", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add LeoYeAI/openclaw-master-skills --skill flow-swarm -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills flow-swarm --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/flow-swarm .opencode/skills/flow-swarm && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "flow-swarm" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/flow-swarm into .opencode/skills/flow-swarm/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "flow-swarm", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
flow-swarmMulti-agent swarm orchestration via RuFlo + Claude Code. An agent skill from LeoYeAI/openclaw-master-skills.
Flow Swarm is an agent skill from LeoYeAI/openclaw-master-skills. Multi-agent swarm orchestration via RuFlo + Claude Code. Turns single coding sessions into coordinated agent teams (architect/coder/tester/reviewer). Proven on 7 consecutive production runs generating 430+ tests across a 50K+ line Elixir codebase with 83% zero-iteration success rate. Features 150+ MCP tools for inter-agent coordination, persistent cross-run memory (sql.js + HNSW vectors), task tracking, file claim locks, and session persistence. Includes battle-tested prompt templates for test generation, feature…
Its SKILL.md is about 5.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts and reference files (for example `_meta.json`, `references/template-examples.md` and `scripts/setup-flow-swarm.sh`).
It sits in Agent Workflows, covering Multi-agent orchestration, Prompt engineering and MCP servers. It works with Elixir, SQL and Model Context Protocol. The repository describes itself as: 🧠 Curated collection of 1209+ best OpenClaw skills — weekly updated by MyClaw.ai. The licence is MIT.
3 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit e5199b5. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Ships 1 file in scripts/ (Shell), which the agent can run.
Shell commands in SKILL.md call:
claudepython3npmFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md. Its commands use npm, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Flow Swarm loads about 5.3k tokens when it runs, and up to ~7.9k if it reads all its reference files. Until then it costs about 194 tokens; SKILL.md has 1,495 words of instructions outside code blocks.
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.
The automated check found no risky patterns in SKILL.md.
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); the scripts in this folder are not scanned.
The full file from LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 1,495 words, ~5,281 tokens.
.claude/skills/flow-swarm/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.Multi-agent swarm orchestration for Claude Code via RuFlo. One prompt, coordinated agents, production results.
Critical fix: MCP tools were disabled (autoStart: false). This prevented Claude Code from calling mcp__claude-flow__swarm_init, memory_store, agent_spawn, etc. during every swarm run we'd done so far. Fixed.
Also fixed: --print mode does not auto-discover .mcp.json. You must pass --mcp-config .mcp.json explicitly.
| Change | Why |
|---|---|
autoStart: true in .mcp.json | Was false — all 150+ MCP tools were disabled in every prior run |
--mcp-config .mcp.json flag added to exec pattern | --print doesn't auto-load project MCP config |
| MCP tool reference table added | 150+ tools now documented: swarm_init, agent_spawn, memory_store, etc. |
| Prompt templates updated to call MCP tools | Without explicit instructions, Claude may not use them |
| Setup script auto-fixes autoStart | ruflo init defaults to false; setup script corrects it |
v1.0 was theory. v2.0 is battle-tested across 5 production runs (355 tests, 5/5 green, 4/5 zero-iteration).
| Change | Why |
|---|---|
| Tiered task routing replaces one-size-fits-all | Pure-data modules don't need GenServer test isolation advice |
| Target selection protocol added | Picking the RIGHT module matters more than swarm config |
| Pre-flight context injection | Feeding the swarm grep output of public functions = dramatically better coverage |
| Daemon reality check | Workers timeout/fail often (20-50% success); swarm value comes from prompt orchestration, not daemon workers |
| Removed WASM Booster claims | Never observed in practice; hooks + prompt patterns drive all real value |
| Real performance data | Actual timing, test counts, iteration rates from production runs |
Two layers working together:
Layer 1: MCP Tools (150+ tools via @claude-flow/cli)
When autoStart: true in .mcp.json, Claude Code gets access to real coordination tools:
swarm_init — creates swarm with topology, persists to .claude-flow/swarm/swarm-state.jsonagent_spawn — registers agents with model routing (haiku/sonnet/opus/inherit)memory_store / memory_search — sql.js + HNSW vector embeddings for semantic recalltask_create / task_complete — tracks task state with assignmentsession_save / session_restore — persists session state between runsclaims_claim / claims_release — prevents agents from editing same filescoordination_consensus — multi-agent agreement on decisionsLayer 2: Prompt Orchestration (our FlowSwarm patterns) The SWARM MODE prefix causes Claude Code to think in roles (architect/coder/reviewer). Combined with pre-flight context injection (grepping public APIs), this produces 80% zero-iteration success.
Both layers matter. v1.0 had Layer 2 only (autoStart was false, MCP tools never loaded). v2.0 enables both.
OpenClaw → exec (background) → Claude Code
↓
MCP Server starts (autoStart: true)
150+ tools available via @claude-flow/cli
↓
SWARM MODE prompt → swarm_init tool called
agent_spawn × N → task_create → execute
↓
memory_store (findings) → task_complete
↓
Output + persisted stateruflo --version # 3.5.x+
claude --version # Claude Code CLI# Full setup: install RuFlo + register MCP + init project
./scripts/setup-flow-swarm.sh /path/to/project
# Verify
./scripts/setup-flow-swarm.sh --verify /path/to/projectOr manually:
npm install -g ruflo@latest
claude mcp add ruflo -- npx -y ruflo@latest mcp start
cd /path/to/project && ruflo init && ruflo memory init && ruflo daemon startAfter ruflo init, the .mcp.json file defaults to autoStart: false. This disables ALL 150+ MCP tools during Claude Code sessions. Fix it:
# Check current state
python3 -c "import json; d=json.load(open('.mcp.json')); print('autoStart:', d['mcpServers']['claude-flow'].get('autoStart'))"
# Enable (REQUIRED for full swarm functionality)
python3 -c "
import json
with open('.mcp.json') as f: d = json.load(f)
d['mcpServers']['claude-flow']['autoStart'] = True
with open('.mcp.json', 'w') as f: json.dump(d, f, indent=2)
print('MCP autoStart enabled')
"Without this, Claude Code runs without swarm tools. The prompt patterns still work (v1.0 proved this), but you lose: persistent swarm state, agent memory, task tracking, session persistence, and inter-agent coordination.
This is the highest-leverage decision. Pick wrong and you waste a swarm run.
Best targets (in order):
Find targets fast:
# List untested modules by size (biggest = best target)
for f in lib/**/*.ex; do
base=$(basename "$f" .ex)
count=$(find test/ -name "${base}_test.exs" 2>/dev/null | wc -l | tr -d ' ')
[ "$count" = "0" ] && echo "$(wc -l < "$f")L $f"
done | sort -rn | head -10The secret sauce: feed the swarm a pre-flight scan of the module. Don't just say "test this file" — tell it exactly what functions exist, what patterns the project uses, what edge cases matter.
# Pre-flight: scan public API
grep -n "^ def " lib/your_module.ex
# Pre-flight: check existing test patterns
head -30 test/some_existing_test.exsThen build the prompt with that intel baked in.
# Launch (ALWAYS background, NEVER nohup)
exec(
command='cd /project && claude --permission-mode bypassPermissions --mcp-config .mcp.json --print "SWARM MODE: ... TASK: ..."',
background=True,
timeout=300
)
# Poll for completion
process(action="poll", sessionId="xxx", timeout=120000)
# Verify the output actually compiles/passes
mix test test/path/to/new_test.exsCritical exec rules:
--print buffers ALL output until exit. Use background: true + poll.nohup — Node.js stdout capture breaks silently (empty files).mix test (or equivalent) on swarm output before committing.Proven: 147/147, 66/66, 41/41 zero-iteration
Best for: static catalogs, type definitions, translation modules, config builders.
SWARM MODE: Initialize hierarchical swarm with MCP tools.
COORDINATION:
1. Call swarm_init with topology "hierarchical", maxAgents 4, strategy "specialized"
2. Call agent_spawn for: architect (analyze module), coder (write tests), reviewer (verify)
3. Call task_create for the test generation task
4. After completion: call memory_store with key findings and task_complete
TASK: Write comprehensive ExUnit tests for [MODULE_PATH] ([LINE_COUNT] lines, [DESCRIPTION]).
Public API:
[PASTE grep -n "^ def " output here]
Key data to validate:
- [List specific assertions: required struct keys, value ranges, URL formats, etc.]
- [List known edge cases: unknown inputs, nil, empty string, integer where string expected]
Requirements:
- File: test/[matching_path]_test.exs
- Use async: true (pure functions, no state)
- Group tests by function (describe blocks)
- Test ALL variants, not just a sample (e.g., all 8 hotels, not just 2)
- Include edge cases: nil input, empty string, unknown keys
- Do NOT modify any source files
When done: call memory_store with test count + key findings, then output results.Proven: 43/43, required 3 iterations (test isolation)
SWARM MODE: Initialize hierarchical swarm with MCP tools.
COORDINATION:
1. Call swarm_init with topology "hierarchical", maxAgents 4, strategy "specialized"
2. Call agent_spawn for: architect (analyze GenServer behavior), coder (write tests), reviewer (verify)
3. Call task_create for the test generation task
4. After each iteration: call memory_store with what failed and why
5. After completion: call task_complete with final results
TASK: Write comprehensive ExUnit tests for [MODULE_PATH] ([LINE_COUNT] lines, GenServer).
Public API:
[PASTE grep output]
CRITICAL — Test Isolation for GenServers:
- The module registers as a named process (__MODULE__). It's already supervised globally.
- Do NOT use start_supervised! — it conflicts with the app-supervised instance.
- Pattern: stop the global instance, restart with test config, re-stop at end.
- OR: if start_link accepts a name: option, use unique names per test.
- async: false for GenServer tests that touch global state.
Requirements:
- Test GenServer lifecycle (init, handle_call, handle_cast, handle_info)
- Test crash recovery: missing catch-all handlers are REAL BUGS worth flagging
- Test state transitions and side effects
- Do NOT modify any source filesSWARM MODE: Initialize hierarchical swarm (maxAgents 4, strategy specialized).
Spawn: architect (analyze deps + plan mocks), coder (write tests), reviewer (verify coverage).
TASK: Write comprehensive ExUnit tests for [MODULE_PATH].
This module depends on: [LIST DEPENDENCIES]
Mock strategy: [Mox / manual mock / test config override]
Reference existing mocks in test/support/ if any.
Requirements:
- Mock all external calls (HTTP, DB, external services)
- Test happy path AND error paths (timeouts, 4xx, 5xx, malformed responses)
- async: true if using Mox with allowances
- Do NOT modify source filesSWARM MODE: Initialize hierarchical swarm (maxAgents 6, strategy specialized).
Spawn: architect (plan structure), coder (implement), tester (tests), reviewer (quality).
Architect plans FIRST. Coder implements. Tester validates. Reviewer catches issues.
TASK: [Feature description with clear acceptance criteria]
Architecture constraints:
- [List patterns to follow from existing codebase]
- [List modules/files to reference for conventions]
HARD LIMIT: Maximum 5 iterations if quality loop needed.SWARM MODE: Initialize anti-drift hierarchical swarm (maxAgents 4).
Spawn: architect (plan + checkpoint), coder (execute), reviewer (validate each step).
ANTI-DRIFT RULES:
- Architect creates numbered plan before ANY code is written
- Coder implements ONE step at a time
- Reviewer validates EACH step before proceeding
- If reviewer rejects twice: STOP and report
- Checkpoint state after each successful step
TASK: [Refactor description]
HARD LIMIT: Maximum 8 iterations.SWARM MODE: Security-focused hierarchical swarm (maxAgents 5, strategy specialized).
Spawn: security-architect (threat model), auditor (scan), coder (fix), tester (verify).
TASK: Security audit of [scope].
Checklist:
- [ ] Dependency vulnerabilities (mix audit / npm audit)
- [ ] Hardcoded secrets in source
- [ ] Injection vectors (SQL, XSS, command)
- [ ] Auth/authz bypass paths
- [ ] GenServer catch-all handlers (handle_info, handle_cast) — these are REAL BUGS
- [ ] Error messages leaking internal state
- [ ] Rate limiting gaps
- [ ] CORS/CSP headers
Output: findings table with severity, file, line, fix.
HARD LIMIT: Maximum 5 iterations.SWARM MODE: Initialize ring swarm (maxAgents 4, strategy adaptive).
Spawn: coder, tester, reviewer, coordinator.
HARD LIMIT: Maximum 10 iterations.
TASK: Iterate on [target] until [score threshold].
Per iteration:
1. Coder fixes based on reviewer feedback
2. Tester runs full suite, reports pass/fail count
3. Reviewer scores against rubric
4. Score >= threshold → STOP, report final score
5. Iteration == 10 → STOP regardless, report score and remaining gapsThese tools become available to Claude Code during swarm sessions. Include instructions to USE them in your prompts.
| Tool | Purpose |
|---|---|
swarm_init | Create swarm with topology + strategy. Persists to .claude-flow/swarm/ |
swarm_status | Check swarm health mid-run |
swarm_shutdown | Clean shutdown with state persistence |
agent_spawn | Register agents with model routing (haiku/sonnet/opus) |
agent_status | Check individual agent state |
memory_store | Persist findings to sql.js + HNSW (semantic search) |
memory_search | Retrieve relevant context from prior runs |
task_create | Track task with assignment + status |
task_complete | Mark task done with summary |
| Tool | Purpose |
|---|---|
session_save | Save session state between runs |
session_restore | Resume from prior session |
claims_claim | Lock a file/resource (prevents agent conflicts) |
claims_release | Release lock |
coordination_consensus | Multi-agent agreement |
coordination_sync | Synchronize agent state |
| Tool | Purpose |
|---|---|
analyze_diff | Review code changes |
analyze_diff_risk | Assess risk of changes |
performance_report | Bottleneck detection |
Without autoStart: true, Claude Code has ZERO access to these tools. It runs on prompt intelligence alone (which works, as v1.0 proved). With them enabled, the swarm can:
| Topology | When | Track Record |
|---|---|---|
hierarchical | Test gen, features, refactors | 5/5 green tonight |
ring | Quality loops, pipelines | Proven in Ralph loops |
mesh | Research, exploration | Untested in production |
star | Simple delegation | Untested in production |
Default: hierarchical. It has the strongest anti-drift properties and all production wins used it.
| Module | Lines | Tests | Time | Iterations | Result |
|---|---|---|---|---|---|
| AssetHealthCheck (GenServer) | 288 | 43 | ~120s | 3 | 43/43 ✅ + found 2 real bugs |
| WidgetHotelAssets (data) | 2,017 | 147 | ~90s | 0 | 147/147 ✅ |
| SquadBuilder (config builder) | 889 | 58 | ~90s | 0 | 58/58 ✅ |
| I18n (translations) | 272 | 41 | ~60s | 0 | 41/41 ✅ |
| TravelClick Types (structs) | 637 | 66 | ~90s | 0 | 66/66 ✅ |
Totals: 355 new tests, 5/5 modules green, 4/5 zero-iteration (80%)
--print buffering, not swarm complexity).| Worker | Success Rate | Notes |
|---|---|---|
| map | 100% | Fast (1ms), just indexes project structure |
| consolidate | 100% | Fast (9ms), memory compaction |
| audit | 20% | Timeouts at 300s, falls back to local mode |
| optimize | 33% | Timeouts, deferred on high CPU load |
| testgaps | 50% | Deferred on high CPU load |
| predict | 0% | Disabled by default |
| document | 0% | Disabled by default |
Takeaway: Don't rely on daemon workers for task quality. The prompt pattern does the heavy lifting. Daemon adds marginal background value (map + consolidate work; audit/optimize are unreliable).
You can run multiple swarms simultaneously on different modules. Each gets its own exec session:
# Launch 2 parallel swarms
exec(command='cd /project && claude --mcp-config .mcp.json --print "SWARM: ... TASK: test module_a"', background=True)
exec(command='cd /project && claude --mcp-config .mcp.json --print "SWARM: ... TASK: test module_b"', background=True)
# Poll both
process(action="poll", sessionId="session-a", timeout=120000)
process(action="poll", sessionId="session-b", timeout=120000)Observed: 2 parallel swarms work cleanly. 3+ may cause CPU load deferrals on daemon workers (irrelevant for prompt-driven value).
FlowSwarm can analyze and improve itself:
SWARM MODE: Initialize meta-analysis hierarchical swarm (maxAgents 4).
Spawn: architect (analyze skill files), analyst (review production data), coder (rewrite), reviewer (validate).
TASK: Analyze the FlowSwarm skill at [path] against production run data.
Review: what worked, what failed, what's missing. Generate v[N+1].No output from Claude Code:
--print buffers until completion. Use background: true on exec, poll with generous timeout. Never use nohup.
Swarm didn't fire:
.claude/settings.json has hooksruflo doctorclaude mcp list | grep rufloruflo daemon stop && ruflo daemon startGenServer test isolation failures: The global supervised instance conflicts with test instances. Solutions:
name: :"test_#{System.unique_integer()}"async: false for stateful testsDaemon workers timing out:
Normal. Workers like audit and optimize timeout at 300s regularly (20-33% success rate). The swarm's value comes from prompt orchestration, not daemon workers. Ignore worker failures unless you specifically need their output.
Memory shows 0 entries: In v1.0 (autoStart: false), the MCP server never started so memory_store was never called. With v2.0 (autoStart: true), Claude Code can call memory_store directly. Check after a run:
ruflo memory stats
ruflo memory search -q "test results"CPU load deferrals: Workers defer when system CPU > 8. This is protective. During active swarm runs, expect deferrals. Workers catch up when CPU drops.
skills/flow-swarm/
├── SKILL.md # This file (v2.0)
├── scripts/
│ └── setup-flow-swarm.sh # Install + init + verify
└── references/
└── template-examples.md # Extended templates with context--print mode doesn't persist swarm memories© LeoYeAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 3 other files (scripts, references) in skills/flow-swarm of LeoYeAI/openclaw-master-skills.
Open the folder on GitHubat commit e5199b5
Flow Swarm next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Flow Swarm this skillLeoYeAI/openclaw-master-skills | 2.2k | — | ~5.3k | Automated safety check: Pass | MIT | |
| Run Wavejpicklyk/task-orchestrator | 207 | — | ~5k | Automated safety check: Pass | MIT | |
| NaturalNPC-Worldwide/npcpy | 1.5k | — | ~161 | Automated safety check: Pass | MIT | |
| Opik Comparecomet-ml/opik-mcp | 220 | — | ~2.6k | Automated safety check: Notes | Apache-2.0 | |
| Codex with ChatGPT Planning LoopXiaoDuoYa/codex-with-chatgpt | 7.2k | — | ~11k | Automated safety check: Notes | MIT | |
| Workflow Schema Tuningbreaking-brake/cc-wf-studio | 5.4k | — | ~1.3k | Automated safety check: Pass | Custom licence |
jpicklyk/task-orchestrator
Resolves ready MCP work items into a run plan, shows it to you, then executes it through the Workflow tool or direct subagent dispatch, with post-run verification.
NPC-Worldwide/npcpy
Render the provided prompt template with Jinja context and send it to the active NPC's LLM.
comet-ml/opik-mcp
Run a candidate against the baseline over an Opik test suite and read the numbers back — which cases broke, which got fixed, the per-metric deltas, worst rows, and whether the two runs are…
XiaoDuoYa/codex-with-chatgpt
Uses ChatGPT in the browser as the planning and review brain for a Codex session, with Codex keeping all execution and ChatGPT reading the workspace through a bridge.
breaking-brake/cc-wf-studio
Guides edits to cc-wf-studio's workflow schema so AI agents generate better workflows, treating schema text as prompt engineering rather than validation.
professorpalmer/Puppetmaster
Operates and supervises Puppetmaster, a multi-agent orchestrator, through its MCP tools or CLI, picking the right verb for edits, reviews, audits and long-running jobs.
LeoYeAI/openclaw-master-skills
Manages pipelines on a DevOps quality and efficiency platform through its OpenAPI: list workspaces and templates, create, update, run and cancel pipelines, and read run records.
LeoYeAI/openclaw-master-skills
Patches OpenClaw's Feishu extension so an edited document triggers an isolated agent session that reads the doc and replies inline, turning it into a live chat space.
LeoYeAI/openclaw-master-skills
Multi-context memory management system for OpenClaw agents with group-isolated storage, global shared memory, workspace organization, and group-specific skills isolation.
LeoYeAI/openclaw-master-skills
Runs a brand's AI-search visibility work end to end: diagnosing how AI platforms represent it, repositioning it, producing AI-optimized content and monitoring ongoing mentions.
LeoYeAI/openclaw-master-skills
Installs and authenticates the gws CLI, then automates Gmail, Drive, Sheets, Calendar, Docs, Chat and Tasks with ready-made recipes, persona bundles and security audits.
LeoYeAI/openclaw-master-skills
Runs four advisor roles, a fitness coach, nutritionist, data analyst and TCM practitioner, to build a health profile and track workouts, diet and wellness over time.
Works with
Categories
Multi-agent swarm orchestration via RuFlo + Claude Code. An agent skill from LeoYeAI/openclaw-master-skills. Flow Swarm is an agent skill from LeoYeAI/openclaw-master-skills. Multi-agent swarm orchestration via RuFlo + Claude Code.
Flow Swarm fits situations like: tasks that involve Multi-agent orchestration; tasks that involve Prompt engineering; tasks that involve MCP servers.
Run `npx skills add LeoYeAI/openclaw-master-skills --skill flow-swarm -a claude-code`. Or copy the skill folder (skills/flow-swarm in LeoYeAI/openclaw-master-skills) into .claude/skills/flow-swarm in your project. Claude Code loads it when a task matches its description.
Run `npx skills add LeoYeAI/openclaw-master-skills --skill flow-swarm -a codex`. Or copy the skill folder (skills/flow-swarm in LeoYeAI/openclaw-master-skills) into .agents/skills/flow-swarm in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add LeoYeAI/openclaw-master-skills --skill flow-swarm -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/flow-swarm, .gemini/skills/flow-swarm, .github/skills/flow-swarm and .opencode/skills/flow-swarm in your project.
Going by SKILL.md and its folder, Flow Swarm needs a shell for the scripts in its folder and the command-line tools its instructions call (claude, python3 and npm). Our summary lists: Python 3; Node.js; A Bash shell.
SKILL.md contains no URLs. Its commands use npm, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
Flow Swarm is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 5.3k tokens (SKILL.md is roughly 21k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.6k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Flow Swarm: Run Wave (jpicklyk/task-orchestrator, 207 stars), Natural (NPC-Worldwide/npcpy, 1.5k stars), Opik Compare (comet-ml/opik-mcp, 220 stars) and Codex with ChatGPT Planning Loop (XiaoDuoYa/codex-with-chatgpt, 7.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
LeoYeAI (a GitHub user) maintains it in LeoYeAI/openclaw-master-skills, which has 2,161 GitHub stars. The repository holds 1,235 skills in this directory. The repository was last updated on July 20, 2026.
Source: LeoYeAI/openclaw-master-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.