agent-army
$
npx mdskill add OneWave-AI/claude-skills/agent-armyA 2-layer parallel execution framework. Each Layer 1 agent has its own full context window (not a slice). Each spawns Layer 2 sub-agents under it. The result is many independent brains running at once — not one brain divided.
SKILL.md
.github/skills/agent-armyView on GitHub ↗
---
name: agent-army
description: "Deploy a 2-layer parallel agent hierarchy for large, parallelizable work — big refactors, multi-file migrations, codebase-wide audits, bulk generation. Layer 1 is 3-50+ specialist agents, each with its own full context window; Layer 2 is 2+ sub-agents per member. Includes git safety, tiered sizing, a pre-deploy gate, phantom-completion checks, and multi-wave follow-up."
user_invocable: true
---
# Agent Army
A 2-layer parallel execution framework. Each Layer 1 agent has its own full context window (not a slice). Each spawns Layer 2 sub-agents under it. The result is many independent brains running at once — not one brain divided.
```
Commander (you)
|
|-- Layer 1: Team (3 to 50+, each = own 1M context)
| |-- Agent A (1M) -- Sub-agent A1, A2, ...
| |-- Agent B (1M) -- Sub-agent B1, B2, ...
| |-- Agent C (1M) -- Sub-agent C1, C2, ...
| |-- ... (no cap)
```
**Swarm vs. army:** A swarm splits one context window across sub-agents — one brain, divided. An army gives each Layer 1 member its own window. That difference is the whole point of this skill.
## When to use
- Large refactors spanning many files
- Multi-file color / style / naming / API migrations
- Broad codebase audits (security, a11y, performance, dead code)
- Bulk content generation or transformation
- Any task with **6+ independent units of work** that can run simultaneously
## When NOT to use
- Fewer than 6 independent units of work — just do it directly
- Heavy sequential dependencies where each step needs the last one's output
- Single-file changes
- Tasks needing one coherent authorial voice across all output (parallel agents drift)
If the task doesn't clearly fit, say so and propose doing it inline instead of spinning up an army.
<mandatory-rules>
## MANDATORY RULES
1. EVERY Layer 1 agent MUST spawn 2+ sub-agents. No exceptions. If you're about to deploy an L1 agent with no sub-agents, STOP and restructure.
2. NEVER silently shrink the army. Match the user's chosen tier. If you must deviate, say so out loud and why.
3. Sub-agent deployment instructions go INSIDE the Layer 1 brief. If they're missing, the sub-agents will never be created.
4. Report as agents complete: `[Agent N/M complete] name: X files modified, Y flags`.
5. Show the army plan and pass the Deployment Gate before deploying (Full Mode). Quick Mode composes the plan internally but still passes the Gate.
6. After every wave: run the build AND the phantom-completion check (see Verify). A green report from an agent is a claim, not proof.
7. "Keep going" / "don't stop" = continuous mode: launch a new agent the moment one completes. Don't wait, don't re-ask.
</mandatory-rules>
## Army Size
Confirm a tier before starting. Present this table:
```
| Tier | L1 Agents | Total w/ Sub-agents | Est. Tokens |
|--------------|-----------|---------------------|--------------|
| Conservative | 3 | ~9 | ~200-500K |
| Standard | 5-10 | ~15-30 | ~500K-1.5M |
| Aggressive | 10-20 | ~30-60 | ~1.5-4M |
| Maximum | 20-50+ | ~60-100+ | ~4M+ |
| Custom | you pick | you pick | varies |
```
Default to **Standard** on "just do it." After recon (Step 3), recommend a specific number based on what you found — e.g. "35 files across 6 domains → Aggressive: 8 L1 agents, 2-3 sub-agents each (~22 total, ~2M tokens). Adjust?" Token estimates are rough and scale with task complexity.
## Protocol
**Mode:** If scope is already concrete (file paths, exact changes, tier), skip to Step 3 (Quick Mode). Otherwise start at Step 1 (Full Mode).
### Step 1: Intake (Full Mode)
Confirm in one line if the user already gave context: "Goal: [X]. Scope: [Y]. Tier: [Z]. Starting." Otherwise ask for: goal (one sentence), scope (files/dirs/"everything"), constraints (don't touch X, match Y), tier.
### Step 2: Git Checkpoint
1. `git status` — warn on uncommitted changes, offer to stash/commit first.
2. `git checkout -b agent-army/checkpoint-{timestamp}` then switch back. This is the rollback point.
3. No git repo? Warn that there's no safety net and get explicit confirmation.
### Step 3: Recon
1. Grep / Glob / Read to find every affected file.
2. Count the units of work.
3. `wc -l` each file. Flag 500+ line files as **heavy** → assign solo.
4. Classify into domains (by directory, import graph, file type, naming pattern).
5. Identify shared dependencies — **foundation files** imported across domains. Handle these first (Step 5).
6. Identify the build command (package.json, Makefile, etc.).
Output: `Files: N | Heavy: [list] | Domains: [list] | Shared deps: [list] | Build cmd: [cmd]`
### Step 4: Compose
1. **Foundation Agent** for shared deps — runs *before* the parallel wave so dependents don't conflict.
2. **Layer 1:** one agent per domain; split large domains across multiple agents.
3. **Layer 2:** 2+ sub-agents per L1 agent, assigned by **weighted file load**, not file count:
- Small (<200 lines): 2-3 per sub-agent
- Medium (200-500): 1-2 per sub-agent
- Heavy (500+): 1 per sub-agent, solo
4. Name every agent. Assign every file to **exactly one** sub-agent — no overlaps, no gaps.
Output the army plan. Full Mode: pause for "Proceed?" Quick Mode: one-line summary, then deploy.
### Deployment Gate
Output this checklist in your response before deploying. Don't check it mentally — write it out.
```
DEPLOYMENT GATE:
[ ] Every L1 brief contains "You MUST spawn N sub-agents"
[ ] Every sub-agent is named with specific files assigned
[ ] Every file is owned by exactly one sub-agent (no overlap, no gap)
[ ] L1 briefs include full sub-agent deployment instructions
[ ] Tier matches the user's selection
```
All must PASS. Any FAIL → fix the plan before deploying.
### Step 5: Deploy
1. Foundation Agent first (if any). Wait for completion.
2. Launch ALL Layer 1 agents in parallel — `run_in_background: true`, in a single message.
3. Each L1 agent spawns its L2 sub-agents in parallel (per its brief).
4. Report each completion: `[Agent N/M complete] name: results`.
### Step 6: Verify
1. **Re-scan** — rerun the Step 3 searches for remaining violations.
2. **Phantom-completion check** — run `git diff --stat` and cross-reference against agent reports. Any agent that reported "COMPLETE, N files modified" with no matching diff lied or no-op'd — re-dispatch it. Trust the diff, not the report.
3. **Build** — run the build command. Record PASS/FAIL.
4. **Resolve cross-team flags** from the reports.
5. Output the army report:
```
Agents: N | Files modified: N (diff-confirmed) | Skipped: N | Build: PASS/FAIL
Flags: [list] | Phantom completions caught: N | Rollback: git checkout agent-army/checkpoint-{timestamp}
```
## Waves
Each wave is a new, smaller, differently-specialized army. Pause for user approval before each. **Max 4 waves.**
| Wave | Name | Trigger | Purpose |
|------|---------------|----------------------------------------------------------------|------------------------------------------------------|
| 1 | **Execute** | always | make the changes |
| 2 | **Audit** | build fails, remaining violations, 20+ files, or flags > 0 | fresh agents review Wave 1 for correctness/edge cases|
| 3 | **Propagate** | changes touch APIs/types/interfaces; tests or docs reference old patterns | update tests, docs, configs, downstream callers |
| 4 | **Notify** | user opts in | draft PR description, changelog, Slack summary |
Each wave's report is the next wave's recon. After each, rerun Verify; stop when re-scan is clean and build passes, or at 4 waves.
### Resume / scratchpad
For multi-wave or interruptible runs, write `.army-state.md` after Wave 1: files modified (diff-confirmed), open flags, unresolved issues, decisions. If a run is killed, the next invocation reads this file and resumes from the last clean wave instead of restarting. Skip for single-wave tasks.
## Layer 1 Prompt Template
This is what spawned agents actually see — the most important section in the skill.
```
You are [AGENT_NAME], specialist on [DOMAIN].
Objective: [one sentence]
Approved patterns: [exact values — hex codes, class names, API shapes]
Forbidden patterns: [what to remove/avoid]
Your files: [absolute paths with line counts]
Rules: [constraints]. Skip files already using approved patterns (idempotency).
Flag issues outside your files in "Flags for Commander" — do NOT fix them.
CRITICAL: You MUST use the Agent tool to spawn the sub-agents listed below. Do
NOT do the work yourself. Do NOT skip spawning. Deploy ALL sub-agents in a single
message with multiple Agent tool calls.
Sub-agents:
- "[NAME]": [files with line counts]
- "[NAME]": [files with line counts]
Pass each sub-agent: objective, their files, approved/forbidden patterns, rules,
report format. After all complete, aggregate their reports and verify (by re-reading
or grepping) that no forbidden patterns remain in your domain before reporting back.
```
## Layer 2 Prompt Template
```
You are [SUB_AGENT_NAME], working under [TEAM_MEMBER_NAME].
Objective: [one sentence]
Files you own: [absolute paths with line counts — touch only these]
Approved / Forbidden patterns: [exact values to use and remove]
Rules: [constraints]. Skip files already correct. Flag issues outside your files —
do NOT fix them.
Process: read each file fully → check idempotency → apply approved, remove forbidden
→ re-read to confirm no forbidden patterns remain.
Report: Files Modified (file: N replacements), Files Skipped (already correct),
Flags for Commander, Issues, Status (COMPLETE/PARTIAL/FAILED).
```
## Report Format
Every agent returns:
```
## Report: [Name]
Files Modified: [file: N replacements]
Files Skipped: [file: reason]
Flags for Commander: [issue or "None"]
Issues: [issue or "None"]
Status: COMPLETE / PARTIAL / FAILED
```
## Anti-patterns
These are the failure modes this skill exists to prevent. If you catch yourself doing one, stop and correct.
- **Phantom fan-out** — an L1 agent does the work itself instead of spawning sub-agents. The whole speed advantage is gone. Enforced by Mandatory Rule 1 + the Gate.
- **Phantom completion** — an agent reports COMPLETE without editing anything. Caught by the `git diff --stat` cross-check, not by trusting the report.
- **Silent shrink** — quietly running 3 agents when the user picked Aggressive. Always surface deviations.
- **Overlapping ownership** — two sub-agents editing the same file → conflicts and lost edits. Enforced by exactly-one-owner in the Gate.
- **Skipping the foundation** — touching dependents before shared files are updated → cascade of broken imports. Foundation Agent runs first.
- **Army for a small job** — 9 agents for 4 files is slower (orchestration overhead) and burns tokens. See "When NOT to use."
## Error Handling
- Sub-agent fails → L1 agent retries its files or spawns a replacement.
- L1 agent fails → Commander spawns a replacement with the same brief.
- Build failure → diff against the checkpoint branch to isolate the break, then run a fix wave.
- Phantom completion → re-dispatch that agent's files to a fresh agent.
- Idempotency on re-run → agents check for already-applied patterns first, so running the army twice never double-applies.
## Worked example
User: "Replace all neon Tailwind colors with our sand palette across the site."
**Recon:** 45 files, 5 domains, 2 shared dependencies (a theme config + a shared `<Button>`). Build cmd: `npm run build`. Recommend Aggressive: 1 Foundation Agent + 5 L1 agents, 2-4 sub-agents each (~22 total).
**Wave 1 (Execute):** Foundation Agent updates the 2 shared files first. Then 5 L1 agents deploy in parallel, each fanning out to sub-agents. Reports: 43 modified, 2 skipped (already correct), 3 flags.
**Verify:** `git diff --stat` shows 42 files changed — one agent reported a file it never touched (phantom). Re-dispatched; now 43 confirmed. Build passes. 3 flags + 2 edge-case violations remain → Audit wave recommended.
**Wave 2 (Audit):** 3 fresh agents target the flagged files and violations. Fixed. Build clean, re-scan clean. 4 test files import the changed `<Button>` → Propagate recommended.
**Wave 3 (Propagate):** 3 agents update the 4 test files + 1 doc. Build still green.
**Final:** 3 waves, ~28 agents total, 48 files touched (diff-confirmed), 1 phantom caught, zero violations remaining, clean build. Rollback branch available.
More from OneWave-AI/claude-skills
- accessibility-auditorAudit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.
- agent-swarm-deployerDeploys swarms of sub-agents for massive parallel data processing tasks. Unlike agent-army (which is for code changes), this is for DATA tasks -- processing 1000 documents, analyzing datasets, bulk content generation. Configurable swarm size, task distribution, result aggregation, progress tracking, and error recovery.
- agent-team-builderDesigns and deploys custom agent teams for specific business workflows. Interactive discovery of business processes, then generates complete team configurations with specialized agent roles, tool access, communication protocols, and handoff rules.
- agent-to-agentAgent-to-Agent (A2A) communication protocol. Connect two or more Claude agents that pass messages, share context, delegate tasks, and collaborate. Implements structured handoffs, shared memory, and multi-agent conversations.
- ai-readiness-assessmentAssesses how ready a business is for AI adoption across six dimensions. Evaluates data maturity, tech stack, team skills, process documentation, budget, and culture. Generates a comprehensive ai-readiness-report.md with scores, gap analysis, and recommended starting points. Aligned with OneWave AI's audit methodology.
- animateGenerate animated videos and motion graphics from natural language descriptions. Creates a standalone Vite + React project with Framer Motion scenes that auto-play in the browser. Use when the user wants to create animations, motion graphics, video intros, animated presentations, or product demos.
- api-documentation-writerGenerate comprehensive API documentation including endpoint descriptions, request/response examples, authentication guides, error codes, and SDKs. Creates OpenAPI/Swagger specs, REST API docs, and developer-friendly reference materials. Use when users need to document APIs, create technical references, or write developer documentation.
- api-endpoint-scaffolderGenerate REST API endpoints with proper structure, validation, error handling, and types. Use when creating new API routes, endpoints, or backend services.
- api-load-testerLoad tests API endpoints with progressive concurrency. Measures response times, error rates, throughput, and identifies breaking points. Generates a detailed report with latency percentiles, throughput curves, bottleneck analysis, and optimization recommendations.
- athlete-social-media-managerCreate brand-safe content for athletes. Personal branding strategy, engagement optimization, crisis communication, sponsor integration.