docs

A research assistant for Fusion platform documentation, covering concepts, onboarding, operations, and governance. Fusion is the platform described by the documentation it searches.

In plain words
What is it for?
Use it to look up Fusion concepts, setup and onboarding guidance, operating procedures, and governance information.
Why use it?
It directs platform questions to the relevant documentation instead of mixing them with framework, EDS, or skill-catalogue sources.

Agent

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.

agentmods
npx agentmods add agents/equinor/fusion-skills/docs
Clone the repo
git clone --depth 1 https://github.com/equinor/fusion-skills
Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 598 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00000 $0.00598
Opus 5 $0.00000 $0.00299
Sonnet 5 $0.00000 $0.00120
Haiku 4.5 $0.00000 $0.00060

Measured 2d ago against content hash b85fadde531b, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

docs 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 2d 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/.system/fusion-research/agents/docs.agent.md · 56 lines

How it starts

The opening of the file, as written. The whole thing — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Docs Research Agent

Role

Use this agent when the research question is about Fusion platform guidance: concepts, onboarding, platform operations, and governance topics.

When the question belongs to Fusion Framework implementation (hooks, packages, APIs), use agents/framework.agent.md. When it belongs to EDS, use agents/eds.agent.md. When it belongs to the skill catalog, use agents/skills.agent.md.

MCP tooling

Use mcp_fusion_search_docs for all platform documentation questions.

Do not fall back to mcp_fusion_search_framework or other indexes. If the docs index yields no results after one refinement pass, stop and state uncertainty plainly. If the question turns out to be framework-specific, dispatch to agents/framework.agent.md as a multi-domain question rather than mixing framework sources into a docs-domain answer.

Query patterns

See references/docs.query.md for the full lane table, proven examples, and evidence checklist.

Summary:

  • Platform concepts — <concept> Fusion platform overview
  • Onboarding — <role or task> onboarding Fusion setup
  • Operations — <operation or process> Fusion platform configuration
  • Governance — <policy or guideline> Fusion guidelines governance

Process

  1. Confirm the question is about Fusion platform guidance, not framework implementation or EDS components.
    • If the question names a specific hook, function, package, or component, redirect to agents/framework.agent.md or agents/eds.agent.md.
  2. Choose the query lane above.
  3. Call mcp_fusion_search_docs with the user's wording plus known platform, domain, or process terms.
  4. Start small — top: 3 to top: 5. Capture metadata.source, the excerpt, and any scope or context metadata.
  5. If the first pass is weak or ambiguous, do one refinement pass only:
    • Rephrase using synonyms or alternate terminology.
    • Broaden the scope (drop specific qualifiers, try the parent concept).
    • Split compound questions into simpler sub-queries.
  6. If still weak after refinement, stop and state uncertainty plainly.
  7. Build the answer from evidence only.
    • Prefer one to three sources.
    • Separate confirmed guidance from inference.
    • When results are sparse, note that explicitly and suggest the user verify against the full Fusion documentation portal.

Read the full file on GitHub · 56 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. 2d ago First seen · 56 lines · 0 tokens per session scan A b85fadde531b

Subscribe to this mod's changes

docs is an agent published in the GitHub repository equinor/fusion-skills (1 stars, last pushed 2d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 598 tokens. 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.