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 when a coding session is burning context fast, output is drowning in noise, the model keeps forgetting earlier decisions, or the user asks to reduce tokens, stop the verbose narration, work in a huge repo, or make a long session survive. Covers what to read, what to summarize, what to write to disk, and when to start fresh.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Context is the scarcest resource in a long session. It gets spent on three things: files you read, output you generate, and narration nobody asked for. Only the first one is usually worth it.
Treat the context window like a budget with a balance. Before any expensive action — reading a big file, running a chatty command, dumping a directory tree — ask what the cheapest way to get the same answer is.
Read narrowly. sed -n '120,190p' file.ts beats reading a 2,000-line file to see one function. Grep for the symbol, then read the 40 lines around it. Read a whole file only when you are about to restructure it.
Search before reading. grep -rn "symbolName" --include="*.ts" costs a few hundred tokens and tells you which of forty files matters.
Silence the noisy commands. Pipe installs, builds, and test runs through a filter instead of dumping them whole:
npm test 2>&1 | tail -30
npm run build 2>&1 | grep -E "error|Error|warning" | head -20
npm install --silent 2>&1 | tail -5
git diff --stat # not git diff, unless you need the hunks
A passing test suite needs one line of proof, not 400.
Write instead of remembering. Anything that must survive — a plan, a decision, a list of files to touch — goes in a file on disk. Files are re-readable at a cost you choose; context is not.
Do not re-read what you just wrote. If an edit succeeded, it succeeded. Re-reading to "verify" is pure spend.
Cut the narration. No preamble, no recap of what was just shown, no bulleted summary of a diff the user can see. Say what changed and what is next, in a line or two.
When a session bloats, it is almost always one of these:
| Leak | Fix |
|---|---|
| Whole-file reads for one function | grep, then a line-ranged read |
Full npm install / build logs | | tail -n or grep for errors |
Directory listings of node_modules, dist, .next | prune them in the find/ls |
| Re-reading files after editing | trust the edit result |
| Long explanations of finished work | one line |
| Pasting a file back to show a small change | show the diff hunk only |
| Repeating the plan every turn | plan lives in a file |
When roughly two-thirds of the window is gone, stop adding and start consolidating:
A fresh session that reads a good plan file outperforms a stuffed session every time. Do not treat starting over as failure — it is the intended move.
Economy has a floor. Never skip:
Being cheap about the wrong thing produces confident, wrong work. The goal is fewer tokens, not less evidence.
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。
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.
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。