C31-storm

C31-storm is a skill for Claude Code, Codex from ChianW/C31. It costs 43 tokens per session (3,747 once invoked), scanned A, original, MIT.

A multi-perspective analysis method based on Stanford's STORM approach. It discovers relevant viewpoints, simulates discussion between them, maps disagreements, and produces a provisional judgment with confidence levels.

In plain words
What is it for?
Use it to evaluate complex choices, identify competing interests and contradictions, compare viewpoints, and synthesize a cautious recommendation after research.
Why use it?
It reduces one-sided conclusions when a decision, technology, person, or strategy has important trade-offs or conflicting evidence.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to evaluate complex choices, identify competing interests and contradictions, compare viewpoints, and synthesize a cautious recommendation after research.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/chianw/c31/c31-storm
Install

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.

Any agent
npx skills add ChianW/C31 --skill c31-storm
Clone the repo
git clone --depth 1 https://github.com/ChianW/C31

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for C31-storm

README.md
[![agentmods](https://agentmods.dev/badge/skills/chianw/c31/c31-storm.svg)](https://agentmods.dev/skills/chianw/c31/c31-storm)
Your own site
<a href="https://agentmods.dev/skills/chianw/c31/c31-storm"><img src="https://agentmods.dev/badge/skills/chianw/c31/c31-storm.svg" alt="Measured on agentmods" height="20"></a>
Per session 43 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,747 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00043 $0.03747
Opus 5 $0.00022 $0.01873
Sonnet 5 $0.00009 $0.00749
Haiku 4.5 $0.00004 $0.00375

Measured 7d ago against content hash 7036bd3dc749, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

C31-storm 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 7d 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.

skills/core/C31-storm/SKILL.md · 428 lines

How it starts

The opening of the file, as written. The whole thing — 428 lines — stays where its author put it; the contents beside it link to each section on GitHub.

C31 STORM

Multi-perspective analysis and synthesis skill. Based on Stanford STORM's core philosophy: discover perspectives → simulate dialogue → map conflicts → synthesize judgment.

Not a fixed "5 perspectives" template. Perspectives are dynamically discovered from the input context, not preset.

When to Use

  • User says "你怎么看" / "评估一下" / "值得吗" / "该不该"
  • C31-research detects multi-source conflicts (conflict_detected: true)
  • Need to evaluate a decision, a person, a technology, or a strategy from multiple angles
  • Single-perspective analysis feels insufficient or biased

When NOT to Use

  • Factual lookup only (use C31-research)
  • Simple yes/no questions with no nuance
  • User explicitly asks for a single perspective
  • No prior research context exists (run C31-research first)

Core Principles

  1. Perspectives are discovered, not preset — extract from input, not "always 5 fixed angles"
  2. Dialogue is simulated, not monologue — each perspective can ask follow-up questions
  3. Conflicts are mapped, not resolved — identify where perspectives disagree and why
  4. Synthesis is provisional, not absolute — confidence levels on all judgments
  5. Sources are mandatory, not optional — every claim must be traceable

Pipeline

Input: C31-research output (structured findings + sources)
  ↓
Phase 1: Perspective Discovery → extract 3-5 dynamic perspectives
  ↓
Phase 2: Simulated Dialogue → each perspective interrogates the topic
  ↓
Phase 3: Conflict Map → organize agreements, conflicts, blind spots
  ↓
Phase 4: Synthesis → judgment + recommendations with confidence
  ↓
Phase 5: Source Audit → verify all claims have traceable sources
  ↓
Output: Structured analysis with conflict map + synthesis + gaps

Phase 1: Perspective Discovery

Goal: Extract 3-5 distinct perspectives from the input context.

Input: C31-research output (or user-provided context).

Process:

  1. Scan the input for implicit立场分化:
    • Source A says X, Source B says not-X → 2 perspectives
    • Different disciplines approach same topic differently → disciplinary perspectives
    • Different stakeholders have different incentives → stakeholder perspectives
    • Historical vs current view → temporal perspectives
  2. Name each perspective with a one-line identity (e.g., "Effectuation理论研究者", "批判管理学者", "实践派创业者")
  3. For each perspective, note its core concern and potential bias

Read the full file on GitHub · 428 lines

Changes

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.

  1. 7d ago First seen · 428 lines · 43 tokens per session scan A 7036bd3dc749

Subscribe to this mod's changes

C31-storm is a skill published in the GitHub repository ChianW/C31 (1 stars, last pushed 12d ago), licensed MIT. It adds 43 tokens to every session and 3,747 once invoked, about $0.0002 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 2026-08-31.

Related

Other skills, from other repositories

compare-harnesses

Diff two scaffolded harnesses (ADR-031). Reports manifest meta drift + host list + per-file fingerprint changes (added/removed/changed). Exits 0 IDENTICAL, 1 DRIFT, 2 missing manifest. Use --bundle for the ADR-031 schema-1 JSON envelope.

ruvnet/metaharness · 66 tokens

create-harness

Scaffold your own focused AI agent harness — pick host (Claude Code, Codex, pi.dev, Hermes), template, agents, skills, and ship a npm-publishable harness with its own npx CLI. Use when a user asks to "create my own agent harness", "scaffold a harness", "make a custom Claude Code plugin like ruflo", or "build a…

ruvnet/metaharness · 89 tokens

diag-harness

Kernel-version skew check (ADR-027). Reports manifest surface + manifest kernel + installed kernel + verdict (match/patch-diff/minor-diff/major-diff). Exits 1 on minor/major skew with a copy-pasteable npm install @metaharness/[email protected] next step. Exits 2 if no .harness/manifest.json at path.

ruvnet/metaharness · 85 tokens

oia-manifest

Emit .harness/oia-manifest.json declaring layer alignment with the OIA v0.1 9-layer reference architecture. Self-describes the harness's MCP wiring, witness signing, audit log, identity posture (always 'none' at v0.1). --check verifies an existing manifest, --dry-run prints without writing, --json emits to stdout.

ruvnet/metaharness · 79 tokens

repo-genome

7-section readiness scorecard for a LOCAL repo. Reports repo type + agent topology + MCP risk + test confidence + release readiness + recommended harness plan + scorecard. Exit 0 ready, 1 needs-work, 2 blocked. --json for the 6-field scorecard, --bundle for the ADR-031 schema-1 envelope.

ruvnet/metaharness · 73 tokens

example-harness

Scaffold a ready-made AI agent harness in one command from the 19 published @metaharness/ example packages — 9 host integrations (Claude Code, Codex, Hermes, pi.dev, OpenClaw, RVM, Copilot, OpenCode, GitHub Actions) + 10 vertical pods (devops, research, trading, support, legal, coding, education, sales, gaming…

ruvnet/metaharness · 90 tokens