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 vasilyu1983/AI-Agents-public --skill docs-notes-retrievalgit clone --depth 1 https://github.com/vasilyu1983/AI-Agents-publicWrote 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/vasilyu1983/ai-agents-public/docs-notes-retrieval)<a href="https://agentmods.dev/skills/vasilyu1983/ai-agents-public/docs-notes-retrieval"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/docs-notes-retrieval/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/vasilyu1983/ai-agents-public/docs-notes-retrieval"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/docs-notes-retrieval.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00040 | $0.03135 |
| Opus 5 | $0.00020 | $0.01568 |
| Sonnet 5 | $0.00008 | $0.00627 |
| Haiku 4.5 | $0.00004 | $0.00314 |
Grade A, and why
docs-notes-retrieval 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 9d 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.
Notes Retrieval
Quick Start — Concrete Commands
Three common scenarios using the scripts in scripts/.
Scenario 1: Vault inventory
Before indexing or packaging, understand what is in the vault.
# Full inventory as JSON — one record per note with title, frontmatter, wikilinks, tags, word count, mtime
python scripts/scan_vault.py inventory /path/to/vault > vault-inventory.json
# Tag summary sorted by note count
python scripts/scan_vault.py tags /path/to/vault
# CSV for spreadsheet review
python scripts/scan_vault.py inventory /path/to/vault --format csv > vault-inventory.csv
Scenario 2: Packaged context for an LLM session
Build a markdown bundle ready to paste into a Claude or GPT session.
# Keyword search — include all notes matching "weekly review", budget 40k chars
python scripts/build_context_pack.py /path/to/vault \
--query "weekly review" \
--max-chars 40000 > context-pack.md
# Specific notes with heading-level chunking
python scripts/build_context_pack.py /path/to/vault \
--notes "Projects/Alpha.md" "Projects/Beta.md" \
--chunk-strategy heading \
--max-chars 60000 > project-alpha-pack.md
# Most recent notes first, paragraph chunking, written to file
python scripts/build_context_pack.py /path/to/vault \
--query "architecture decision" \
--order-by recency \
--chunk-strategy paragraph \
--out session-context.md
Scenario 3: Orphan note cleanup
Find and review notes with no inbound or outbound wikilinks — usually stale or misplaced.
# List orphans as JSON
python scripts/scan_vault.py orphans /path/to/vault
# Pipe into jq to see just paths and word counts
python scripts/scan_vault.py orphans /path/to/vault | jq '.[] | {path, word_count}'
# CSV for manual triage
python scripts/scan_vault.py orphans /path/to/vault --format csv > orphans.csv
Orphan cleanup workflow:
- Run
orphansto get the list. - Review word count: orphans < 50 words are likely stubs — archive or delete.
- Orphans > 200 words with no links may be valuable but unconnected — add wikilinks or a backlink from a map-of-content note.
- Re-run
orphansafter cleanup to verify the count dropped.
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- agents/openai.yaml 352 B
- data/sources.json 3.9 KB
- learnings.consolidated.md 596 B
- learnings.md 1.5 KB
- references/ai-curated-wiki-pattern.md 7.2 KB
- references/context-packaging-patterns.md 7.7 KB
- references/obsidian-and-local-vaults.md 5.2 KB
- references/retrieval-anti-patterns.md 8.5 KB
- scripts/build_context_pack.py 14 KB runs code
- scripts/scan_vault.py 10 KB runs 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.
- 9d ago First seen · 250 lines · 40 tokens per session scan A fe656c163844
docs-notes-retrieval is a skill published in the GitHub repository vasilyu1983/AI-Agents-public (87 stars, last pushed 7d ago), licensed MIT. It adds 40 tokens to every session and 3,135 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 2026-08-30.
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