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 RobinNorberg/oh-my-copilot --skill wikigit clone --depth 1 https://github.com/RobinNorberg/oh-my-copilotWrote 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/robinnorberg/oh-my-copilot/wiki)<a href="https://agentmods.dev/skills/robinnorberg/oh-my-copilot/wiki"><img src="https://agentmods.dev/badge/skills/robinnorberg/oh-my-copilot/wiki/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/robinnorberg/oh-my-copilot/wiki"><img src="https://agentmods.dev/badge/skills/robinnorberg/oh-my-copilot/wiki.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 | $0.00019 | $0.00684 |
| Opus 5 | $0.00010 | $0.00342 |
| Sonnet 5 | $0.00004 | $0.00137 |
| Haiku 4.5 | $0.00002 | $0.00068 |
Grade A, and why
wiki 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 5d 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.
This is a copy
92% identical to wiki — 10 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Wiki
Persistent, self-maintained markdown knowledge base for project and session knowledge. Inspired by Karpathy's LLM Wiki concept.
Operations
Ingest
Process knowledge into wiki pages. A single ingest can touch multiple pages.
wiki_ingest({ title: "Auth Architecture", content: "...", tags: ["auth", "architecture"], category: "architecture" })
Query
Search across all wiki pages by keywords and tags. Returns matching pages with snippets — YOU (the LLM) synthesize answers with citations from the results.
wiki_query({ query: "authentication", tags: ["auth"], category: "architecture" })
Lint
Run health checks on the wiki. Detects orphan pages, stale content, broken cross-references, oversized pages, and structural contradictions.
wiki_lint()
Quick Add
Add a single page quickly (simpler than ingest).
wiki_add({ title: "Page Title", content: "...", tags: ["tag1"], category: "decision" })
List / Read / Delete
wiki_list() # Show all pages (reads index.md)
wiki_read({ page: "auth-architecture" }) # Read specific page
wiki_delete({ page: "outdated-page" }) # Delete a page
Log
View wiki operation history by reading .omg/wiki/log.md.
Categories
Pages are organized by category: architecture, decision, pattern, debugging, environment, session-log
Storage
- Pages:
.omg/wiki/*.md(markdown with YAML frontmatter) - Index:
.omg/wiki/index.md(auto-maintained catalog) - Log:
.omg/wiki/log.md(append-only operation chronicle)
Cross-References
Use [[page-name]] wiki-link syntax to create cross-references between pages.
Auto-Capture
At session end, significant discoveries are automatically captured as session-log pages. Configure via wiki.autoCapture in .omc-config.json (default: enabled).
Hard Constraints
- NO vector embeddings — query uses keyword + tag matching only
- Wiki pages are git-ignored by default (
.omg/wiki/is project-local)
Model Routing
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.
- 5d ago First seen · 76 lines · 19 tokens per session scan A 7028a5746a79
wiki is a skill published in the GitHub repository RobinNorberg/oh-my-copilot (5 stars, last pushed 3d ago), licensed MIT. It adds 19 tokens to every session and 684 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to wiki, differing in 10 lines, and is treated as a copy.
Other skills, from other repositories
lane-memory
A file-based store for durable project facts that cannot be reliably inferred from source code or its module map.
conversation-memory
Persistent memory systems for LLM conversations including short-term, long-term, and entity-based memory.
data-structure-protocol
Give agents persistent structural memory of a codebase — navigate dependencies, track public APIs, and understand why connections exist without re-reading the whole repo.
ejentum-reasoning-harness
MCP server exposing four cognitive harness modes (reasoning, code, anti-deception, memory). Each call returns an engineered scaffold (failure pattern, procedure, suppression vectors, falsification test) the agent ingests before generating.
geminiignore-finops
Configure and optimize .geminiignore files for AI context window efficiency and token cost reduction (FinOps).
hierarchical-agent-memory
Scoped CLAUDE.md memory system that reduces context token spend. Creates directory-level context files, tracks savings via dashboard, and routes agents to the right sub-context.