docs-notes-retrieval

docs-notes-retrieval is a skill for Codex from vasilyu1983/AI-Agents-public. It costs 40 tokens per session (3,135 once invoked), scanned A, original, MIT.

A local search and packaging system for notes stored in Obsidian, Markdown notebooks, or similar exports. It inventories notes and prepares selected material as context for an AI chat.

In plain words
What is it for?
It helps list notes, summarise tags and links, search for matching notes, select specific files, split them by headings or paragraphs, and create a shareable context bundle.
Why use it?
It makes it easier to find relevant notes and stay within a chosen text limit when giving them to an AI model.

Skill for Codex

Written for Codex: agents/openai.yaml present. Also seen: mentions CLAUDE.md; mentions Claude Code; mentions Codex.

Good fit It helps list notes, summarise tags and links, search for matching notes, select specific files, split them by headings or paragraphs, and create a shareable context bundle.

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Install with agentmods
npx agentmods add skills/vasilyu1983/ai-agents-public/docs-notes-retrieval
Install

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.

Any agent
npx skills add vasilyu1983/AI-Agents-public --skill docs-notes-retrieval
Clone the repo
git clone --depth 1 https://github.com/vasilyu1983/AI-Agents-public

Made for: Codex.

Wrote 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.

agentmods badge for docs-notes-retrieval

README.md
[![agentmods](https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/docs-notes-retrieval/github.svg)](https://agentmods.dev/skills/vasilyu1983/ai-agents-public/docs-notes-retrieval)
Your own site
<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.

agentmods 80×15 button for docs-notes-retrieval

Your own site · 80×15
<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>
Per session 40 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,135 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 9d ago against content hash fe656c163844, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

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.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/build_context_pack.py, scripts/scan_vault.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

frameworks/shared-skills/skills/docs-notes-retrieval/SKILL.md · 250 lines

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:

  1. Run orphans to get the list.
  2. Review word count: orphans < 50 words are likely stubs — archive or delete.
  3. Orphans > 200 words with no links may be valuable but unconnected — add wikilinks or a backlink from a map-of-content note.
  4. Re-run orphans after cleanup to verify the count dropped.

Read the full file on GitHub · 250 lines

Changes

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.

  1. 9d ago First seen · 250 lines · 40 tokens per session scan A fe656c163844

Subscribe to this mod's changes

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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