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 ai-analyst-lab/ai-analyst --skill knowledge-bootstrapgit clone --depth 1 https://github.com/ai-analyst-lab/ai-analystWrote 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/ai-analyst-lab/ai-analyst/knowledge-bootstrap)<a href="https://agentmods.dev/skills/ai-analyst-lab/ai-analyst/knowledge-bootstrap"><img src="https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst/knowledge-bootstrap/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/ai-analyst-lab/ai-analyst/knowledge-bootstrap"><img src="https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst/knowledge-bootstrap.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 | $0.00061 | $0.02890 |
| Opus 5 | $0.00030 | $0.01445 |
| Sonnet 5 | $0.00012 | $0.00578 |
| Haiku 4.5 | $0.00006 | $0.00289 |
Grade A, and why
knowledge-bootstrap 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.
How it starts
The opening of the file, as written. The whole thing — 250 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Knowledge Bootstrap
Purpose
Initialize the knowledge subsystems for a new session. Resolve the active context source, load the small resident layer, and inventory the selected and compiled context that can be supplied after the user asks a question.
When to Use
- At the start of any session
- After
/connect-dataor/switch-dataset - When the system detects missing or stale knowledge files
Instructions
Load each subsystem in order. Every file read MUST gracefully degrade: if the file does not exist, skip silently and note "not yet populated" in the summary. Never block the session on a missing subsystem.
Step 1: Setup State
Read .knowledge/setup-state.yaml.
- Parse
setup_completeand count phases withstatus: "complete". - If
setup_complete: false, note incomplete phases to offer/setup. - If missing: Note "Setup: not initialized -- offer /setup".
Step 2: Active Dataset
Read .knowledge/active.yaml.
-
If
active_datasetis null or missing, note "No active dataset" and continue. -
Resolve the context source first. Call
resolve_context_dir(active, project_root)fromhelpers/knowledge/context_sync.py->(ctx_dir, source). If.knowledge/context-source.yamlsayssource: git, it clones/pulls the team's communal context repo to a cache and returns that dataset dir; otherwise it returns the in-repo.knowledge/datasets/{active}/. Load the dataset knowledge (semantic/, metrics/, schema.md, quirks.md) fromctx_direither way - the same loader, the source just differs. Report the source ("context: local" or "context: team repo @ {ref}") in the readiness summary. -
Inventory from
ctx_dir. Loadcontext-policy.yamlandcustom_instructions.mdas the resident layer. Do not load every metric, relationship, query, and correction into the prompt by default. -
Confirm these components are available:
| File | Required | If Missing |
|---|---|---|
manifest.yaml |
Yes | Note "manifest missing -- not usable" |
schema.md |
Yes | Generate via schema_to_markdown() or profiling |
quirks.md |
No | Create empty template |
metrics/index.yaml |
No | Count as 0 |
custom_instructions.md (root) else semantic/custom_instructions.md |
No | Skip |
verified_queries.yaml (root) else semantic/verified_queries.yaml |
No | Skip |
corrections.md (root) |
No | Skip |
semantic/entities.yaml |
No | Note "no semantic layer" |
semantic/relationships.yaml |
No | Skip |
semantic/dimensions.yaml |
No | Skip |
semantic/measures.yaml |
No | Skip |
semantic/filters.yaml |
No | Skip |
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 · 250 lines · 61 tokens per session scan A 20102edb47ef
knowledge-bootstrap is a skill published in the GitHub repository ai-analyst-lab/ai-analyst (298 stars, last pushed 3d ago), licensed MIT. It adds 61 tokens to every session and 2,890 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 2026-09-12.
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