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 agentmods add skills/grimoire-rs/grimoire/docsnpx skills add grimoire-rs/grimoire --skill docsgit clone --depth 1 https://github.com/grimoire-rs/grimoireWhat 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 | $0.00031 | $0.00376 |
| Opus 5 | $0.00015 | $0.00188 |
| Sonnet 5 | $0.00006 | $0.00075 |
| Haiku 4.5 | $0.00003 | $0.00038 |
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.
What it actually says
Grimoire Documentation
Role: write user-facing docs for Grimoire (Markdown under docs/).
Workflow
- Read source code — no memory docs
- Read product context —
.claude/rules/product-context.mdbefore user-facing writing - Search real-world examples from other ecosystems before comparisons
- Identify problem feature solves before solution
- Draft narratively — idea → problem → solution → depth
- Verify internal links point to existing sections with content
Narrative Standards
- Idea → problem → solution, then depth — never start "Grimoire is a..."
- No marketing tone — examples make case
- Reference-style links only — never inline
[text](url); definitions at file bottom - Every external tool hyperlinked — every occurrence, not first
- Analogies in
:::infocallout boxes, not inline - Custom anchors on every heading —
{#parent-subsection}
Relevant Rules (load explicitly for planning)
.claude/rules/docs-style.md— narrative + linking + anchor conventions.claude/rules/product-context.md— positioning and product identity
Tool Preferences
- WebFetch / WebSearch — real-world examples from other tools before comparisons
Constraints
- NEVER inline links — reference-style only
- ALWAYS read source before documenting; never from memory
Handoff
- To /hex-architect — docs revealing design ambiguity
- To Builder — code changes uncovered while writing docs
$ARGUMENTS
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.
- 2d ago First seen · 47 lines · 31 tokens per session scan A 216fddfced6d
docs is a skill published in the GitHub repository grimoire-rs/grimoire (8 stars, last pushed 2d ago), licensed Apache-2.0. It adds 31 tokens to every session and 376 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
agent-code-analyzer
Agent skill for code-analyzer - invoke with $agent-code-analyzer.
foundry-config-setup
Resolve missing setup caused by a hardcoded Foundry project endpoint or model in a sample. Use when a sample fails because it uses a placeholder/hardcoded projectendpoint (for example "https://your-project.services.ai.azure.com") or a hardcoded model instead of reading them from the environment.
haiku
When writing a haiku for this bot, follow these conventions.
deploy-docker-compose
Run the Omnigent server as a Docker compose stack (server + Postgres) on any Docker host — your laptop, a VPS, EC2 by hand, or as the base layer of any container-platform deploy. Invoke when the user wants to build the image, bring up the compose stack, debug the stack on a host they already have, or extend the stack…
azure-mgmt-botservice-dotnet
Azure Resource Manager SDK for Bot Service in .NET. Management plane operations for creating and managing Azure Bot resources, channels (Teams, DirectLine, Slack), and connection settings. Triggers: "Bot Service", "BotResource", "Azure Bot", "DirectLine channel", "Teams channel", "bot management .NET", "create bot".
dogfood
Systematically explore and test a mobile app on iOS/Android with agent-device to find bugs, UX issues, and other problems. Use when asked to dogfood, QA, exploratory test, find issues, bug hunt, or test this app on mobile.