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 HirogaKatageri/hirokata --skill storm-researchgit clone --depth 1 https://github.com/HirogaKatageri/hirokataWrote 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/hirogakatageri/hirokata/storm-research)<a href="https://agentmods.dev/skills/hirogakatageri/hirokata/storm-research"><img src="https://agentmods.dev/badge/skills/hirogakatageri/hirokata/storm-research/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/hirogakatageri/hirokata/storm-research"><img src="https://agentmods.dev/badge/skills/hirogakatageri/hirokata/storm-research.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 | $0.00141 | $0.01968 |
| Opus 5 | $0.00071 | $0.00984 |
| Sonnet 5 | $0.00028 | $0.00394 |
| Haiku 4.5 | $0.00014 | $0.00197 |
Grade A, and why
storm-research 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 11d 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 — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.
STORM Research — Multi-Perspective Deep Research Orchestrator
Run the full STORM pipeline on a topic. One prompt asks one question and returns the majority view — the surface. STORM asks the same topic from five independent expert lenses, maps where they fight, synthesizes a briefing no single expert could write, then red-teams its own output. In Stanford's peer-reviewed testing, multi-perspective articles were ~25% more organized and ~10% broader than single-pass research. This skill operationalizes that with dedicated sub-agents so the heavy reading happens off the main context window.
The Pipeline
Topic
└─ Phase 1 FAN-OUT (parallel): practitioner · skeptic · economist · historian · academic
└─ Phase 2 CONTRADICTION MAP: contradiction-mapper reads all 5 → finds clashes/agreement/gaps
└─ Phase 3 SYNTHESIS: synthesizer reads 5 + map → cited research briefing
└─ Phase 4 PEER REVIEW: peer-reviewer audits the briefing → reliability grade + fixes
└─ Present consolidated result to the user
Workflow
Step 0 — Scope the Topic
- Get the topic. If the user supplied one (
storm research <topic>), use it. If it's vague or sprawling (e.g. "AI", "the economy"), ask one sharpening question to narrow it — a tight topic produces a far better briefing than a broad one. - Capture the audience and angle if offered (deciding / investing / writing / presenting / learning). It tunes the synthesizer's recommendations. Don't interrogate — one optional ask.
- Derive a slug (lowercase, hyphenated, canonical: e.g.
lab-grown-meat-viability). - Set the workspace:
.storm/{slug}/. Create it:mkdir -p .storm/{slug}.
Step 0.5 — Tune the Panel (optional, powerful)
The five default personas fit most topics. For some topics, a swap sharpens the analysis — e.g. add a Clinician for a medical topic, a Regulator for a policy topic, an End User for a product topic. If a swap clearly helps, mention it to the user and spawn the extra persona with the Task tool using the same output contract as the built-in agents (worldview + owned bias + evidence-gathering + structured file with sources). Keep the panel at 5–6; more dilutes. Default to the standard five if unsure.
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.
- 11d ago First seen · 165 lines · 141 tokens per session scan A 5b99db72e9de
storm-research is a skill published in the GitHub repository HirogaKatageri/hirokata (5 stars, last pushed 2d ago), licensed MIT. It adds 141 tokens to every session and 1,968 once invoked, about $0.0007 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.
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