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ingest

ingest is a skill for Claude Code, Codex from regen-coordination/org-os-template. It costs 54 tokens per session (991 once invoked), scanned A, original, MIT.

Instructions for the semantic half of a data-ingestion pipeline: reading source files and proposing structured objects for the command-line tool to validate and store. Ingestion means bringing information into a system.

In plain words
What is it for?
Use it to claim work orders, read sources, break shared material into items such as concepts or claims, and submit candidate objects to the acceptance step.
Why use it?
It separates interpretation from storage, so agents can focus on extracting meaning while the command-line tool handles validation and writes.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

not rated 5repo 2d ago A scan 54 tokens original MIT

Good fit Use it to claim work orders, read sources, break shared material into items such as concepts or claims, and submit candidate objects to the acceptance step.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/regen-coordination/org-os-template/ingest
View source ↗ regen-coordination/org-os-template regen-coordination.github.io
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 regen-coordination/org-os-template --skill ingest
Clone the repo
git clone --depth 1 https://github.com/regen-coordination/org-os-template

Made for: Claude Code, 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 ingest

README.md
[![agentmods](https://agentmods.dev/badge/skills/regen-coordination/org-os-template/ingest/github.svg)](https://agentmods.dev/skills/regen-coordination/org-os-template/ingest)
Your own site
<a href="https://agentmods.dev/skills/regen-coordination/org-os-template/ingest"><img src="https://agentmods.dev/badge/skills/regen-coordination/org-os-template/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.

agentmods 80×15 button for ingest

Your own site · 80×15
<a href="https://agentmods.dev/skills/regen-coordination/org-os-template/ingest"><img src="https://agentmods.dev/badge/skills/regen-coordination/org-os-template/ingest.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 54 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 991 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.
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 $0.00054 $0.00991
Opus 5 $0.00027 $0.00495
Sonnet 5 $0.00011 $0.00198
Haiku 4.5 $0.00005 $0.00099

Measured 9d ago against content hash 3b2be48562cd, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, 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 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.

packages/toolkit-framework/skills/ingest/SKILL.md · 73 lines

How it starts

The opening of the file, as written. The whole thing — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.

ingest

You are the semantic half of the pipeline (seam 1). The CLI did the deterministic half (ingest prepare → work orders in .workorders/). Your job: turn each work order's source into candidate typed objects. You do not validate, you do not store — the CLI's accept gate does.

The loop

  1. Pick an open order: node <framework>/src/cli.mjs ingest list --status open --dir .workorders

  2. Claim it: … ingest claim <wo-id> --by <your-name> --dir .workorders

    • Claim races happen (two agents pull the same open list). If claim fails with an illegal-transition error, someone else got it — go back to step 1 rather than retrying the same order.

  3. Read the order (.workorders/<wo-id>.yaml): source_path, source_type, target_schemas (suggestions, not a cage), instructions.

  4. Read the source. Decompose (deep intake): one shared thing becomes many entries — a transcript can yield source-systems + resources + concepts + claims + signals. Consult skills/capture-and-route/SKILL.md steps 1–7 for the decomposition discipline (source-system check, high-risk triggers, routing, provenance).

  5. If the source's shape is foreign (its own type system / vocabulary), run skills/map-ontology first and propose extensions rather than shoehorning.

  6. If the origin is a living knowledge environment, run skills/register-source so the source-system card + return path exist BEFORE content objects reference them.

  7. Write candidates to .workorders/<wo-id>/candidates/<nn>-<schema>.yaml, one per object:

    schema: source-system        # any schema from `list-schemas` (structural/meta schemas are rejected)
object:
  title: …
  type: …                    # the schema's discriminator
  maturity: raw              # ALWAYS raw — promotion is review-promote's job
  ai_assisted: true          # ALWAYS true for agent-produced objects
  provenance:
    origin: "<where this came from — file, URL>"
    transformation: synthesized   # quoted|summarized|synthesized|translated|remixed|inferred
    authorship: ai-assisted
  # …schema fields; run `validate <schema> <file>` locally if unsure

Read the full file on GitHub · 73 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 · 73 lines · 54 tokens per session scan A 3b2be48562cd

Subscribe to this mod's changes

ingest is a skill published in the GitHub repository regen-coordination/org-os-template (5 stars, last pushed 2d ago), licensed MIT. It adds 54 tokens to every session and 991 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 2026-08-31.

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