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 ChianW/C31 --skill c31-stormgit clone --depth 1 https://github.com/ChianW/C31Wrote 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/chianw/c31/c31-storm)<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>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 | $0.00043 | $0.03747 |
| Opus 5 | $0.00022 | $0.01873 |
| Sonnet 5 | $0.00009 | $0.00749 |
| Haiku 4.5 | $0.00004 | $0.00375 |
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.
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
- Perspectives are discovered, not preset — extract from input, not "always 5 fixed angles"
- Dialogue is simulated, not monologue — each perspective can ask follow-up questions
- Conflicts are mapped, not resolved — identify where perspectives disagree and why
- Synthesis is provisional, not absolute — confidence levels on all judgments
- 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:
- 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
- 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")
- For each perspective, note its core concern and potential bias
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.
- 7d ago First seen · 428 lines · 43 tokens per session scan A 7036bd3dc749
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.
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.
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…
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.
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.
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.
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…