Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.
日本語の概要は準備中です。原文の説明を表示しています。
Deploy a 2-layer parallel agent hierarchy for large, parallelizable work — big refactors, multi-file migrations, codebase-wide audits, bulk generation. A top-tier commander (Fable or Opus) orchestrates the swarms; the user picks a power level (Max Power / Heavy / Balanced / Economy) that sets the Opus/Sonnet/Haiku model mix per layer. 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.
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A 2-layer parallel execution framework. The Commander is the top-tier model — Fable by default, or Opus if that's the session model — and does the thinking: recon, composition, briefing, verification. It orchestrates swarms of subordinate models whose tiers the user picks at deploy time (see Power Level). 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 — Fable or Opus, the session model)
|
|-- Layer 1: Team (3 to 50+, each = own 1M context, L1 tier from power level)
| |-- Agent A (1M) -- Sub-agent A1, A2, ... (L2 tier)
| |-- Agent B (1M) -- Sub-agent B1, B2, ... (L2 tier)
| |-- Agent C (1M) -- Sub-agent C1, C2, ... (L2 tier)
| |-- ... (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.
If the task doesn't clearly fit, say so and propose doing it inline instead of spinning up an army.
<mandatory-rules> ## MANDATORY RULESmodel: explicitly on every Agent call at the user's chosen power level. Omitting it inherits the session model — that's a silent Max Power bill.[Agent N/M complete] name: X files modified, Y flags.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.
Size sets how many agents; power sets which models. Ask for both in one question (AskUserQuestion works well — size and power as two questions in one call). Skip asking if the user already named a power level or explicit models.
| Power Level | Commander | Layer 1 | Layer 2 | Best For |
|---------------------|--------------------|---------|---------|-------------------------------------------------|
| Max Power | Fable/Opus (session)| Opus | Opus | gnarly refactors, correctness-critical migrations|
| Heavy (recommended) | Fable/Opus (session)| Opus | Sonnet | most large tasks — smart orchestration, fast exec|
| Balanced | Fable/Opus (session)| Sonnet | Sonnet | mechanical migrations with clear patterns |
| Economy | Fable/Opus (session)| Sonnet | Haiku | high-volume, dead-simple find/replace |
Rules:
/model or claude --model fable) before deploying: commander quality is the ceiling on army quality.model: "<L1 tier>" for team members; L1 briefs instruct model: "<L2 tier>" for sub-agents.Mode: If scope is already concrete (file paths, exact changes, tier), skip to Step 3 (Quick Mode). Otherwise start at Step 1 (Full Mode).
Confirm in one line if the user already gave context: "Goal: [X]. Scope: [Y]. Tier: [Z]. Power: [P]. Starting." Otherwise ask for: goal (one sentence), scope (files/dirs/"everything"), constraints (don't touch X, match Y), tier, and power level.
git status — warn on uncommitted changes, offer to stash/commit first.git checkout -b agent-army/checkpoint-{timestamp} then switch back. This is the rollback point.wc -l each file. Flag 500+ line files as heavy → assign solo.Output: Files: N | Heavy: [list] | Domains: [list] | Shared deps: [list] | Build cmd: [cmd]
Output the army plan. Full Mode: pause for "Proceed?" Quick Mode: one-line summary, then deploy.
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
[ ] Power level set: model: "<L1 tier>" on every L1 call; L2 tier + escalations in every brief
All must PASS. Any FAIL → fix the plan before deploying.
run_in_background: true and model: "<L1 tier>" on every call, in a single message.[Agent N/M complete] name: results.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.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}
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.
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.
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, passing model: "[L2_TIER]" on every call
[note escalations: except "NAME" which gets model: "[higher tier]"].
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.
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).
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
These are the failure modes this skill exists to prevent. If you catch yourself doing one, stop and correct.
git diff --stat cross-check, not by trusting the report.model: on Agent calls, so every sub-agent silently inherits the commander's model. The user picked Economy and got billed Max Power. Enforced by Mandatory Rule 2b + the Gate.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). User picks Heavy power: Fable commander, Opus L1, Sonnet L2.
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.
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.
日本語の概要は準備中です。原文の説明を表示しています。
Use to test whether an AI agent, prompt, or skill actually works before or after it ships — build a small eval set, score runs, and catch regressions when the prompt or model changes. Trigger when the user asks whether a prompt is good, wants to compare models or versions, sees inconsistent agent output, or is about to put an agent in front of customers.
日本語の概要は準備中です。原文の説明を表示しています。
Deploys 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.
日本語の概要は準備中です。原文の説明を表示しています。
Designs 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.
日本語の概要は準備中です。原文の説明を表示しています。
Agent-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.
日本語の概要は準備中です。原文の説明を表示しています。
Use when an AI coding agent is writing or refactoring production code and its known failure modes need to be held in check — over-engineering, silent scope creep, invented APIs, claiming work is done without checking, deleting tests to make them pass. Trigger when the user asks for careful, production-grade, or reviewed code, or when a previous agent run produced confident output that turned out wrong.
日本語の概要は準備中です。原文の説明を表示しています。