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 AnastasiyaW/codex-claude-code-config --skill plan-swarm-reviewgit clone --depth 1 https://github.com/AnastasiyaW/codex-claude-code-configWrote 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/anastasiyaw/codex-claude-code-config/plan-swarm-review)<a href="https://agentmods.dev/skills/anastasiyaw/codex-claude-code-config/plan-swarm-review"><img src="https://agentmods.dev/badge/skills/anastasiyaw/codex-claude-code-config/plan-swarm-review/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/anastasiyaw/codex-claude-code-config/plan-swarm-review"><img src="https://agentmods.dev/badge/skills/anastasiyaw/codex-claude-code-config/plan-swarm-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 16 Skill selects an external model or provider that may use a different account or billing plan than the operator expects. Undisclosed model switches can cause unexpected cost or quota consumption.Fix: Remove the model/provider override or disclose it prominently and require explicit operator approval before invoking an external coding CLI or billed model.
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 | $0.00166 | $0.04017 |
| Opus 5 | $0.00083 | $0.02008 |
| Sonnet 5 | $0.00033 | $0.00803 |
| Haiku 4.5 | $0.00017 | $0.00402 |
Grade A, and why
plan-swarm-review scanned grade A with 0 findings 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 3d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 418 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Plan Swarm Review
Iterative plan hardening through multisampling and focused decomposition.
Scope and task ownership
The user's current task and selected model govern this review. Audit-only is read-only unless changes are requested. In an already authorized implementation or repair task, apply confirmed in-scope reversible fixes and run their focused causal checks; do not ask again merely because a review round found the problem. An optional deeper review does not block those fixes or the original milestone. Stop expanding review when the current acceptance criteria are sufficiently proved, then return to the owning task. Preserve actual external, deletion, and irreversible boundaries. The four rounds are a bounded menu, not a required loop.
Core insight: a single agent misses issues due to attention budget limits. Multiple independent agents reading the same document find different problems (stochastic diversity). Focused decomposition further improves depth per aspect. Iterative fix-then-re-review uncovers issues previously masked by other bugs.
Source: deksden (@deksden_notes) — "Plan Swarming" technique, April 2026. Related: Anthropic Harness Design (Generator-Evaluator), deep-review (parallel competency code review).
Research backing:
- [2502.11027] Sampling diversity in LLM inference — diverse prompts beat identical: +10.8% reasoning, +9.5% code
- [2602.09341] AgentAuditor — reasoning tree audit beats majority voting, recovers 65-82% of minority-correct findings
- [2602.17875] MultiVer — 4 parallel agents hit 82.7% recall on vulnerability detection (beats fine-tuned models)
- [2510.00317] MAVUL — multi-agent vuln detection: +600% vs single-agent
- Anthropic Code Review (Mar 2026) — parallel agents raise substantive findings from 16% to 54%
Modes
This skill works in two modes:
Plan mode (default): review design docs, specs, ADRs, RFCs before implementation.
Code mode: review code files for bugs and vulnerabilities when the user
explicitly requests a swarm code review. Merely supplying a diff or asking for
ordinary code review routes to deep-review. In code mode, aspects shift from plan-oriented (contracts,
completeness) to code-oriented (injection, auth bypass, race conditions, memory).
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 3d ago Changed · +28 lines · -6 tokens per session a742dee6fd65
- 10d ago First seen · 390 lines · 172 tokens per session scan A ce8d1a36455c
plan-swarm-review is a skill published in the GitHub repository AnastasiyaW/codex-claude-code-config (149 stars, last pushed 2d ago), licensed MIT. It adds 166 tokens to every session and 4,017 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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