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

Use it to create knowledge-base entries from discussions and reference material, including technical ideas, business insights, methods, and related concepts.

It makes scattered information easier to reuse by identifying concepts, assigning their subject area, recording relationships, and preserving their source.

How jacob-dietle/context-os configures Claude Code

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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/jacob-dietle/context-os/ingest/github.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/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.

Your own site · 80×15

<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="Reviewed on agentmods" width="80" 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.

Model	Per session	Once invoked
Fable 5.1	$0.00104	$0.00810
Opus 5.5	$0.00042	$0.00324
Sonnet 5.5	$0.00021	$0.00162
Haiku 4.5	$0.00010	$0.00081

Measured 2mo ago against content hash fdc883bc4943, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, 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 2mo 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

Analyze Content Type
- Transcript: Extract decisions, action items, concepts discussed
- Document: Extract core thesis, key points, relationships
- Notes: Extract ideas, questions, insights
Identify Concepts
- What atomic ideas are present?
- What domain do they belong to? (technical/business/methodology)
- What relationships exist between them?

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

Save to Appropriate Location
- Technical concepts → knowledge_base/technical/
- Business concepts → knowledge_base/business/
- Methodology → knowledge_base/methodology/
- Uncertain → knowledge_base/emergent/
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.

2mo 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 (115 stars, last pushed 2mo ago), licensed MIT. It adds 104 tokens to every session and 810 once invoked, about $0.0004 per session on Opus 5.5. 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.

Memory and context

ingest next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
ingestjacob-dietle/context-os	Skill	115	2mo ago	MIT	A	104	Claude Code
knowledge-ingestopenclaw/openclaw	Skill	394k	yesterday	MIT	A	80	Claude Code, Codex
mem0-oss-to-platformmem0ai/mem0	Skill	68k	2d ago	Apache-2.0	A	217	Claude Code, Codex
forgetasgeirtj/system_prompts_leaks	Skill	71k	2d ago	CC0-1.0	A	74	Claude Code, Codex
Cortexdanielmiessler/LifeOS	Skill	20k	yesterday	MIT	A	184	Claude Code
memoryopensquilla/opensquilla	Skill	7.1k	6d ago	Apache-2.0	A	44	Claude Code, Codex
ha-data-storesshiwenwen/hope-agent	Skill	1.9k	yesterday	MIT	A	115	Claude Code, Codex

context-os: Skill for Claude Code