Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add mnzralee/claude-multi-agent-architecture --skill plan-featuregit clone --depth 1 https://github.com/mnzralee/claude-multi-agent-architectureWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/mnzralee/claude-multi-agent-architecture/plan-feature)<a href="https://agentmods.dev/skills/mnzralee/claude-multi-agent-architecture/plan-feature"><img src="https://agentmods.dev/badge/skills/mnzralee/claude-multi-agent-architecture/plan-feature/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/mnzralee/claude-multi-agent-architecture/plan-feature"><img src="https://agentmods.dev/badge/skills/mnzralee/claude-multi-agent-architecture/plan-feature.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00064 | $0.01091 |
| Opus 5 | $0.00032 | $0.00545 |
| Sonnet 5 | $0.00013 | $0.00218 |
| Haiku 4.5 | $0.00006 | $0.00109 |
Grade A, and why
plan-feature scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- **Acceptance criteria as commands**: the exact `npm test`, `npx tsc --noEmit`, or curl that proves each step (see `.claude/rules/ai-agent-engineering.md`). How it starts
The opening of the file, as written. The whole thing — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Plan a Feature
The canonical agentic-coding loop, in four phases: Explore, Plan, Code, Commit. Anthropic's own guidance is that jumping straight to code is the most common way agents go wrong. This skill makes the loop explicit.
When to use this
Invoke /plan-feature (or just follow this loop) for any change where the steps are not already obvious: a new feature, a multi-file refactor, a behavior change, a bug whose cause is unknown.
The one-sentence skip heuristic: if you can describe the entire diff in one sentence ("rename getUser to fetchUser across the repo", "bump the timeout from 5s to 30s"), skip planning and just make the change, then verify. Planning a trivial edit is waste. Everything else gets the loop.
Phase 1: Explore (understand before touching)
Do not write code yet. Build an accurate model of the current state.
- Read the relevant files, tests, and types. Follow the real call paths, do not assume them.
- Find the existing patterns: how does this codebase already solve similar problems? Match them.
- Identify the blast radius: what else depends on what you are about to change? (See
.claude/rules/depth-first-impact-analysis.md.) - For verbose or wide exploration, dispatch a read-only
researchersub-agent so the main context stays clean (see.claude/rules/ai-orchestration-decision-gate.md).
Exit criterion: you can state, in plain language, what exists today and why the change is needed.
Phase 2: Plan (make the approach explicit and reviewable)
Write the plan down before editing. Use plan mode (Shift+Tab to enter it, so the model proposes without making changes) for anything non-trivial.
A good plan states:
- Goal: the one-sentence outcome.
- Approach: the chosen design, and why, over the alternative you rejected.
- Steps: the ordered changes, each small enough to verify on its own.
- Files: the specific files each step touches (this is also the parallel-conflict map if you delegate).
- Acceptance criteria as commands: the exact
npm test,npx tsc --noEmit, or curl that proves each step (see.claude/rules/ai-agent-engineering.md). - Risks and rollback: what could break, and how you back out.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 69 lines · 64 tokens per session scan A a7a458cf6a4d
plan-feature is a skill published in the GitHub repository mnzralee/claude-multi-agent-architecture (6 stars, last pushed 1mo ago), licensed MIT. It adds 64 tokens to every session and 1,091 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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