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/drvoss/everything-copilot-cli/llm-wikinpx skills add drvoss/everything-copilot-cli --skill llm-wikigit clone --depth 1 https://github.com/drvoss/everything-copilot-cliWrote 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/drvoss/everything-copilot-cli/llm-wiki)<a href="https://agentmods.dev/skills/drvoss/everything-copilot-cli/llm-wiki"><img src="https://agentmods.dev/badge/skills/drvoss/everything-copilot-cli/llm-wiki.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 | $0.00035 | $0.01814 |
| Opus 5 | $0.00017 | $0.00907 |
| Sonnet 5 | $0.00007 | $0.00363 |
| Haiku 4.5 | $0.00003 | $0.00181 |
Grade A, and why
llm-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 yesterday.
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 — 203 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LLM Wiki
Build a persistent, interlinked markdown wiki for domain knowledge, research notes, and long-lived synthesized context. This follows Andrej Karpathy's LLM Wiki pattern, but keeps the repository boundary clear: the wiki is a supplementary knowledge layer, not the system of record for issues, PRs, shipped decisions, or canonical project rules.
In this repository's philosophy:
- GitHub stays the system of record for tasks, code review, and shipped artifacts
- project-specific durable guidance still belongs in
.github/copilot-instructions.md,CONTRIBUTING.md, guides, or ADRs - the wiki is best for domain knowledge that would otherwise be re-derived every session
When to Use
- You are researching a domain across many external sources and want the knowledge to compound
- You want a persistent knowledge base about a technology landscape, product area, or codebase-adjacent domain
- The same background explanation keeps getting repeated across sessions
- A query answer would be expensive to reconstruct from raw notes every time
When NOT to Use
| Instead of llm-wiki | Use |
|---|---|
| One-off research or a single answer with citations | deep-research |
| Promoting repeated repo-specific lessons into durable project guidance | knowledge-curator |
| Recovering what happened in prior Copilot sessions | cross-session-memory |
| Issue tracking, delivery planning, or implementation state | GitHub Issues / PRs / session SQL |
Boundary with Other Skills
| Skill | Best for |
|---|---|
llm-wiki |
Compounding external or domain knowledge in a separate markdown wiki |
knowledge-curator |
Graduating stable repository guidance into committed project docs |
deep-research |
Evidence-heavy investigation that may or may not be worth filing permanently |
cross-session-memory |
Looking up prior local session history and decisions |
If a wiki insight becomes a stable repository rule, promote it out of the wiki into the committed docs that future contributors actually depend on.
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.
- yesterday First seen · 203 lines · 35 tokens per session scan A 2fa8951c03dd
llm-wiki is a skill published in the GitHub repository drvoss/everything-copilot-cli (45 stars, last pushed 8d ago), licensed MIT. It adds 35 tokens to every session and 1,814 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 2026-09-03.
Other skills, from other repositories
using-total-recall
Initialize total-recall memory system and ensure sessionstart has been called. Use at session start or when memory features aren't working.
total-recall:commands
Manage the total-recall memory system and govern automatic capture, retrieval, and compaction. Use when the user invokes /total-recall:commands, asks to search/store/forget memories, inspect tiers, manage the knowledge base, run eval, or configure total-recall.
malformed-skill
name: malformed-skill description: [unterminated array.
bundle-skill
Multi-file bundled skill.
ignored-skill
Tests .skillignore honoring.
engram-close-session
Engram의 명시적 세션 종료·수동 반성을 수행한다. 트리거: 세션 종료, 대화 종료, 수고, 끝, /reflect, 반성, close session. 중간 메모리/Wiki 정리는 이 skill이 아니라 engramsummarizesession을 사용한다.