context-os: Skill for Claude Code

.claude/skills/ingest/SKILL.md

ingest is a skill for Claude Code from jacob-dietle/context-os. It costs 104 tokens per session (810 once invoked), scanned A, original, MIT.

A process for turning raw transcripts, documents, notes, or conversations into linked, structured knowledge entries.

In plain words
What is it for?
Use it to create knowledge-base entries from discussions and reference material, including technical ideas, business insights, methods, and related concepts.
Why use it?
It makes scattered information easier to reuse by identifying concepts, assigning their subject area, recording relationships, and preserving their source.

Skill for Claude Code

Written for Claude Code: installed under .claude/.

This is jacob-dietle/context-os's own configuration. It tells Claude Code how to work on context-os itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything context-os configures →

Reuse

Borrowing it

Nothing to install: this file belongs to jacob-dietle/context-os. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/jacob-dietle/context-os/main/.claude/skills/ingest/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/jacob-dietle/context-os

Made for: Claude Code.

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 ingest

README.md
[![agentmods](https://agentmods.dev/badge/skills/jacob-dietle/context-os/ingest.svg)](https://agentmods.dev/skills/jacob-dietle/context-os/ingest)
Your own site
<a href="https://agentmods.dev/skills/jacob-dietle/context-os/ingest"><img src="https://agentmods.dev/badge/skills/jacob-dietle/context-os/ingest.svg" alt="Measured on agentmods" height="20"></a>
Per session 104 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 810 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 $0.00104 $0.00810
Opus 5 $0.00052 $0.00405
Sonnet 5 $0.00021 $0.00162
Haiku 4.5 $0.00010 $0.00081

Measured 8d ago against content hash fdc883bc4943, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

ingest 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 8d 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.

.claude/skills/ingest/SKILL.md · 116 lines

How it starts

The opening of the file, as written. The whole thing — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Content Ingestion

Transform raw content (transcripts, documents, notes) into structured knowledge nodes.

Input

User will provide either:

  • A file path: Process that file
  • Pasted content: Process the content directly
  • "this conversation": Extract insights from current chat

Process

  1. Analyze Content Type

    • Transcript: Extract decisions, action items, concepts discussed
    • Document: Extract core thesis, key points, relationships
    • Notes: Extract ideas, questions, insights
  2. Identify Concepts

    • What atomic ideas are present?
    • What domain do they belong to? (technical/business/methodology)
    • What relationships exist between them?
  3. Generate Knowledge Node(s)

    For each significant concept, create a node:

    ---
    name: CONCEPT_NAME_IN_CAPS
    description: One sentence description
    domain: technical|business|methodology
    node_type: concept|pattern|case-study|framework
    status: emergent
    last_updated: [today's date]
    tags:
      - [domain]  # First tag must be domain
      - [relevant-tags]
    topics:
      - [3-7 relevant topics]
    related_concepts:
      - "[[related-node-1]]"
      - "[[related-node-2]]"
    source:
      type: transcript|document|notes
      file: "[original filename]"
      date: "[date if known]"
    ---
    
    # [Concept Name]
    
    [2-3 paragraph explanation of the concept]
    
    ## Key Points
    
    - [Key point 1]
    - [Key point 2]
    - [Key point 3]
    
    ## Evidence
    
    > "[Direct quote from source if available]"
    
    ## Related Concepts
    
    - [[related-concept-1]] - How they relate
    - [[related-concept-2]] - How they relate
    
  4. Save to Appropriate Location

    • Technical concepts → knowledge_base/technical/
    • Business concepts → knowledge_base/business/
    • Methodology → knowledge_base/methodology/
    • Uncertain → knowledge_base/emergent/
  5. Report Results

    "Processed [filename]:

    Created [N] knowledge nodes:

    • knowledge_base/[domain]/[concept-name].md
    • knowledge_base/[domain]/[concept-name].md

Read the full file on GitHub · 116 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. 8d ago First seen · 116 lines · 104 tokens per session scan A fdc883bc4943

Subscribe to this mod's changes

ingest is a skill published in the GitHub repository jacob-dietle/context-os (108 stars, last pushed 25d ago), licensed MIT. It adds 104 tokens to every session and 810 once invoked, about $0.0005 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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