Agent skill

Flow Swarm

by LeoYeAI in LeoYeAI/openclaw-master-skills

Multi-agent swarm orchestration via RuFlo + Claude Code. An agent skill from LeoYeAI/openclaw-master-skills.

MITAuto-check passedAgent Workflows

Install Flow Swarm

skills CLI
$ npx skills add LeoYeAI/openclaw-master-skills --skill flow-swarm -a claude-code

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

GitHub CLI
$ gh skill install LeoYeAI/openclaw-master-skills flow-swarm --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/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-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
flow-swarm
GitHub stars
2.2k
Token cost
~5.3k tokens
SKILL.md length
1,495 words
Files
4 (incl. scripts, references)
Skills in repo
1,235
Repo updated
First seen
Licence
MIT

At a glance

Multi-agent swarm orchestration via RuFlo + Claude Code. An agent skill from LeoYeAI/openclaw-master-skills.

  • Works in 3 steps: Select Your Target → Build the Prompt (Context-Rich) → Launch and Verify
  • Tasks that involve Multi-agent orchestration
  • SKILL.md covers What Changed in v2.1, What Changed in v2.0, Architecture and Prerequisites, plus 8 more sections
  • Runs Shell scripts from its folder; calls claude, python3 and npm

What it does

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.

When your agent uses it

  • Tasks that involve Multi-agent orchestration
  • Tasks that involve Prompt engineering
  • Tasks that involve MCP servers

Example prompts

  • “swarm this”
  • “flow swarm”
  • “use the swarm”
  • “/flow-swarm”

Requirements

  • Python 3
  • Node.js
  • A Bash shell

Workflow steps

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

  1. Select Your Target
  2. Build the Prompt (Context-Rich)
  3. Launch and Verify

What it can do on your machine

Read from SKILL.md and the folder at commit e5199b5. It shows what the files ask for, not the result of running them.

  • Tool permissions

    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.

  • Runs code

    Ships 1 file in scripts/ (Shell), which the agent can run.

    Shell commands in SKILL.md call:

    • claude
    • python3
    • npm

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    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.

  • 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

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.

Always · name and description, kept in context so the agent knows when to use it
~194
When it runs · the whole SKILL.md, loaded when a task matches
~5.3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~7.9k

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 passed

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.

SKILL.md

The full file from LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 1,495 words, ~5,281 tokens.

Download SKILL.mdSave it as .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.
name
flow-swarm
description
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 builds, refactors, security audits, and quality loops. Setup script with 8-point verification. Works with any language, any codebase. NOT for one-liner edits or read-only tasks. Triggers: "swarm this", "flow swarm", "use the swarm", "flowswarm".
version
2.1.1

FlowSwarm v2.1

Multi-agent swarm orchestration for Claude Code via RuFlo. One prompt, coordinated agents, production results.

What Changed in v2.1

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.

ChangeWhy
autoStart: true in .mcp.jsonWas 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 added150+ tools now documented: swarm_init, agent_spawn, memory_store, etc.
Prompt templates updated to call MCP toolsWithout explicit instructions, Claude may not use them
Setup script auto-fixes autoStartruflo init defaults to false; setup script corrects it

What Changed in v2.0

v1.0 was theory. v2.0 is battle-tested across 5 production runs (355 tests, 5/5 green, 4/5 zero-iteration).

ChangeWhy
Tiered task routing replaces one-size-fits-allPure-data modules don't need GenServer test isolation advice
Target selection protocol addedPicking the RIGHT module matters more than swarm config
Pre-flight context injectionFeeding the swarm grep output of public functions = dramatically better coverage
Daemon reality checkWorkers timeout/fail often (20-50% success); swarm value comes from prompt orchestration, not daemon workers
Removed WASM Booster claimsNever observed in practice; hooks + prompt patterns drive all real value
Real performance dataActual timing, test counts, iteration rates from production runs

Architecture

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.json
  • agent_spawn — registers agents with model routing (haiku/sonnet/opus/inherit)
  • memory_store / memory_search — sql.js + HNSW vector embeddings for semantic recall
  • task_create / task_complete — tracks task state with assignment
  • session_save / session_restore — persists session state between runs
  • claims_claim / claims_release — prevents agents from editing same files
  • coordination_consensus — multi-agent agreement on decisions

Layer 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 state

Prerequisites

bash
ruflo --version    # 3.5.x+
claude --version   # Claude Code CLI

Setup (One-Time Per Machine)

bash
# 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/project

Or manually:

bash
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 start

CRITICAL: Enable MCP Server

After ruflo init, the .mcp.json file defaults to autoStart: false. This disables ALL 150+ MCP tools during Claude Code sessions. Fix it:

bash
# 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.

The FlowSwarm Protocol (3 Steps)

Step 1: Select Your Target

This is the highest-leverage decision. Pick wrong and you waste a swarm run.

Best targets (in order):

  1. Large modules with zero tests — highest ROI, swarm excels here
  2. Pure data/logic modules — no IO mocking needed, near-100% first-pass success
  3. Modules with thin test coverage — swarm fills gaps the original author skipped
  4. Feature builds with clear specs — architect/coder/reviewer shines on greenfield

Find targets fast:

bash
# 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 -10
Step 2: Build the Prompt (Context-Rich)

The 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.

bash
# Pre-flight: scan public API
grep -n "^  def " lib/your_module.ex
# Pre-flight: check existing test patterns
head -30 test/some_existing_test.exs

Then build the prompt with that intel baked in.

Step 3: Launch and Verify
bash
# 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.exs

Critical exec rules:

  • --print buffers ALL output until exit. Use background: true + poll.
  • NEVER use nohup — Node.js stdout capture breaks silently (empty files).
  • Timeout 300s minimum for complex tasks. Simple test gen: 60-120s.
  • Always run mix test (or equivalent) on swarm output before committing.

Prompt Templates (Battle-Tested)

Test Generation — Pure Data Module

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.
Test Generation — GenServer / Stateful Module

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 files
Test Generation — Module with External Dependencies
SWARM 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 files
Feature Build (Greenfield)
SWARM 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.
Refactor (Anti-Drift)
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.
Security Audit
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.
Quality Loop (Ralph-Style)
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 gaps

MCP Tools Reference (Available When autoStart: true)

These tools become available to Claude Code during swarm sessions. Include instructions to USE them in your prompts.

Core Swarm (must-use)
ToolPurpose
swarm_initCreate swarm with topology + strategy. Persists to .claude-flow/swarm/
swarm_statusCheck swarm health mid-run
swarm_shutdownClean shutdown with state persistence
agent_spawnRegister agents with model routing (haiku/sonnet/opus)
agent_statusCheck individual agent state
memory_storePersist findings to sql.js + HNSW (semantic search)
memory_searchRetrieve relevant context from prior runs
task_createTrack task with assignment + status
task_completeMark task done with summary
Coordination (high-value for complex tasks)
ToolPurpose
session_saveSave session state between runs
session_restoreResume from prior session
claims_claimLock a file/resource (prevents agent conflicts)
claims_releaseRelease lock
coordination_consensusMulti-agent agreement
coordination_syncSynchronize agent state
Analysis (useful for reviews)
ToolPurpose
analyze_diffReview code changes
analyze_diff_riskAssess risk of changes
performance_reportBottleneck detection
Why This Matters

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:

  • Persist memories between runs — learn from prior sessions
  • Track tasks formally — not just in-context reasoning
  • Coordinate agents — prevent file conflicts, reach consensus
  • Route by model — use haiku for simple subtasks, opus for architecture

Swarm Topologies

TopologyWhenTrack Record
hierarchicalTest gen, features, refactors5/5 green tonight
ringQuality loops, pipelinesProven in Ralph loops
meshResearch, explorationUntested in production
starSimple delegationUntested in production

Default: hierarchical. It has the strongest anti-drift properties and all production wins used it.

Show full SKILL.md (588 more words)Show less

Performance Data (Real, Not Theoretical)

Test Generation Runs (March 23, 2026)
ModuleLinesTestsTimeIterationsResult
AssetHealthCheck (GenServer)28843~120s343/43 ✅ + found 2 real bugs
WidgetHotelAssets (data)2,017147~90s0147/147 ✅
SquadBuilder (config builder)88958~90s058/58 ✅
I18n (translations)27241~60s041/41 ✅
TravelClick Types (structs)63766~90s066/66 ✅

Totals: 355 new tests, 5/5 modules green, 4/5 zero-iteration (80%)

Key Observations
  • Pure data modules: near-100% first-pass success. No iteration needed.
  • GenServer modules: expect 2-3 iterations for test isolation issues.
  • Real bugs found: 2 missing catch-all handlers (handle_cast, handle_info) — production-grade findings.
  • Data quality issues found: 1 (duplicate image URLs across hotel rooms).
  • Execution time: 60-120s per module regardless of size (bottleneck is Claude Code --print buffering, not swarm complexity).
Daemon Worker Reality Check
WorkerSuccess RateNotes
map100%Fast (1ms), just indexes project structure
consolidate100%Fast (9ms), memory compaction
audit20%Timeouts at 300s, falls back to local mode
optimize33%Timeouts, deferred on high CPU load
testgaps50%Deferred on high CPU load
predict0%Disabled by default
document0%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).

Parallel Swarm Runs

You can run multiple swarms simultaneously on different modules. Each gets its own exec session:

python
# 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).

Self-Improvement Protocol

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].

Troubleshooting

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:

  1. Check .claude/settings.json has hooks
  2. Run ruflo doctor
  3. Verify MCP: claude mcp list | grep ruflo
  4. Restart: ruflo daemon stop && ruflo daemon start

GenServer test isolation failures: The global supervised instance conflicts with test instances. Solutions:

  • Stop global, restart for test, cleanup after
  • Use unique names: name: :"test_#{System.unique_integer()}"
  • Set async: false for stateful tests

Daemon 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:

bash
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.

Files

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

Changelog

v2.1.0 (2026-03-23)
  • CRITICAL FIX: Enabled MCP autoStart (was false, disabling ALL 150+ swarm tools)
  • Added MCP tool reference table (swarm_init, agent_spawn, memory_store, etc.)
  • Updated prompt templates to instruct Claude Code to USE MCP tools
  • Setup script now auto-fixes autoStart: false → true
  • Verify mode checks autoStart status
  • Documented full MCP tool inventory (150+ tools across 18 categories)
  • Root cause of "memory shows 0 entries" identified: MCP server wasn't running
v2.0.0 (2026-03-23)
  • Complete rewrite based on 5 production swarm runs (355 tests generated)
  • Added tiered prompt templates (pure-data vs GenServer vs external deps)
  • Added target selection protocol + --targets script flag
  • Added pre-flight context injection pattern
  • Added real performance data table with actual timing and iteration counts
  • Added parallel swarm run documentation
  • Added daemon worker reality check (success rates, what to ignore)
  • Added self-improvement protocol
  • Removed unverified WASM Booster performance claims
  • Fixed: documented that --print mode doesn't persist swarm memories
v1.0.0 (2026-03-23)
  • Initial release based on first swarm run (AssetHealthCheck)

© LeoYeAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (scripts, references) in skills/flow-swarm of LeoYeAI/openclaw-master-skills.

  • SKILL.md
  • _meta.json
  • references/template-examples.md
  • scripts/setup-flow-swarm.sh

Open the folder on GitHubat commit e5199b5

Compare with similar skills

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.

Flow Swarm compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Flow Swarm this skillLeoYeAI/openclaw-master-skills2.2k—~5.3kAutomated safety check: PassMIT
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NaturalNPC-Worldwide/npcpy1.5k—~161Automated safety check: PassMIT
Opik Comparecomet-ml/opik-mcp220—~2.6kAutomated safety check: NotesApache-2.0
Codex with ChatGPT Planning LoopXiaoDuoYa/codex-with-chatgpt7.2k—~11kAutomated safety check: NotesMIT
Workflow Schema Tuningbreaking-brake/cc-wf-studio5.4k—~1.3kAutomated safety check: PassCustom licence

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Questions about Flow Swarm

What does Flow Swarm do?

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.

When should I use Flow Swarm?

Flow Swarm fits situations like: tasks that involve Multi-agent orchestration; tasks that involve Prompt engineering; tasks that involve MCP servers.

How do I install Flow Swarm in Claude Code?

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.

How do I install Flow Swarm in Codex?

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.

Can I use Flow Swarm 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 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.

What does Flow Swarm need to run?

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.

Does Flow Swarm access the network?

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.

Is Flow Swarm safe to install?

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.

What licence does Flow Swarm use?

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.

How many tokens does Flow Swarm use?

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.

What are the alternatives to Flow Swarm?

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.

Who maintains Flow Swarm?

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.