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/yarlson/yarstack/wiki-recallnpx skills add yarlson/yarstack --skill wiki-recallgit clone --depth 1 https://github.com/yarlson/yarstackWrote 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/yarlson/yarstack/wiki-recall)<a href="https://agentmods.dev/skills/yarlson/yarstack/wiki-recall"><img src="https://agentmods.dev/badge/skills/yarlson/yarstack/wiki-recall.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 | $0.00050 | $0.00284 |
| Opus 5 | $0.00025 | $0.00142 |
| Sonnet 5 | $0.00010 | $0.00057 |
| Haiku 4.5 | $0.00005 | $0.00028 |
Grade A, and why
wiki-recall 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 4d 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
Recall Wiki Knowledge
Load only knowledge that can materially help the current task.
Workflow
- Derive search terms from the repository or project, named components, error signatures, technologies, people, and task verbs.
- Resolve ../../scripts/yarbrain.py relative to this SKILL.md and search the configured vault with a limit of five results.
- Reject keyword-only matches whose scope or subject is unrelated.
- Prefer current semantic notes. Prefer an approved procedure when the task matches its trigger. Read episodes only when the semantic note is incomplete or its provenance matters.
- Read each selected item and check status, verified_at, volatility, scope, and disputed or superseded warnings before relying on it.
- Return the relevant knowledge, why each item matters, its freshness, and any conflict or stale warning. Link or name the source item so deeper evidence can be read on demand.
Do not inject the whole vault, write recall summaries back into it, update access timestamps, or let remembered guidance override current repository instructions or authoritative source code.
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.
- 4d ago First seen · 29 lines · 50 tokens per session scan A ee8a7d90c863
wiki-recall is a skill published in the GitHub repository yarlson/yarstack (3 stars, last pushed 7d ago), licensed MIT. It adds 50 tokens to every session and 284 once invoked, about $0.0003 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
lerim
Query Lerim's persistent project context before agent work. Use it to check prior decisions, constraints, preferences, and historical context through exact queries or synthesized answers.
k-knowledge
把稳定项目事实写回 AGENTS.md 或 project-map。触发：收尾、纠偏，或用户说「记住」。.
k-reconcile
以当前代码和正式事实归宿为准，校准项目地图与 Agent 上下文。.
moai-foundation-context
Manages context window optimization, session state persistence, and token budget allocation for multi-agent workflows. Use for token budget management, context limits, or session handoff across agents.
durable-session-state
Persist plans, scope decisions, evidence, and reviewer/critic verdicts to durable files during long or multi-phase tasks so work survives context compaction, session resumes, and handoffs. Use for swarm-mode tasks, before context grows large, when recording approval gates, and when resuming after compaction or a…
token-optimization
Use when the user says 'token optimization', 'save tokens', 'context window', 'reduce tokens', 'token stack', or 'TokenStack', or asks about extending context window capacity. Covers TokenStack, the built-in compression proxy that shrinks Claude Code tool output before it reaches the Anthropic API. Do NOT use for…