Borrowing it
Nothing to install: this file belongs to ozgurkarahan/ai-agent-memory. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/ozgurkarahan/ai-agent-memory/master/.claude/skills/ingest/SKILL.mdgit clone --depth 1 https://github.com/ozgurkarahan/ai-agent-memoryWrote 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/ozgurkarahan/ai-agent-memory/ingest)<a href="https://agentmods.dev/skills/ozgurkarahan/ai-agent-memory/ingest"><img src="https://agentmods.dev/badge/skills/ozgurkarahan/ai-agent-memory/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.
<a href="https://agentmods.dev/skills/ozgurkarahan/ai-agent-memory/ingest"><img src="https://agentmods.dev/badge/skills/ozgurkarahan/ai-agent-memory/ingest.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00067 | $0.03681 |
| Opus 5 | $0.00034 | $0.01840 |
| Sonnet 5 | $0.00013 | $0.00736 |
| Haiku 4.5 | $0.00007 | $0.00368 |
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.
How it starts
The opening of the file, as written. The whole thing — 256 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ingest Source — LLM-Compiled Wiki Ingestion (Karpathy Pattern)
Two triggers:
- "ingest" — single-source ingest. Follow Phase 1 → Phase 7 as written.
- "mass ingest" / "bulk ingest" / "ingest all ... from ..." — multi-source batch. MUST insert Phase 0 (Inventory) and Phase 2b.5 (Consolidation Planning) before compiling anything. See [[ingest]] consolidation section for the full pattern and rationale.
Resolve the memory root
- If
memory/schema.mdexists in the current workspace, setWIKI_ROOTtomemory/. - Else if
schema.mdexists, setWIKI_ROOTto the current directory. - Else follow the memory-wiki path declared in the current project's
AGENT.md. - If no folder containing both
schema.mdandindex.mdresolves, report the missing path and stop.
All relative paths below are under WIKI_ROOT.
Phase 0: Inventory (mass ingest only)
Before pulling content from files or any connected source tool:
- List every source you intend to ingest (OneNote sections, files, URLs, query topics).
- Save the inventory to the session folder or as a temp list. Do NOT start pulling yet.
- If the source system can't be enumerated, explicitly state the limitation and the coverage estimate before proceeding.
- Ask the user to confirm scope if the inventory is >10 sources — consolidation decisions are cheaper to agree on upfront than to refactor later.
Phase 1: Gather Context
- Read
schema.md— understand wiki structure, categories, naming rules - Read
index.md— get the full list of existing articles with paths (needed for backlink resolution) - Read
glossary.md— check existing terms - Read the source content — the file, topic, or conversation findings to ingest
Phase 2: LLM Compilation (this is YOU — use your reasoning)
Compile the source into structured wiki knowledge. Ask yourself:
2a. What type of knowledge is this?
- Project knowledge →
wiki/projects/{slug}.md(create or update) - Domain/technology knowledge →
wiki/domains/{slug}.md(create or update) - Reusable pattern →
wiki/patterns/{slug}.md(create or update) - Debugging lesson / gotcha →
wiki/lessons/{slug}.md(create or update) - Skill / command / triggerable workflow →
wiki/skills/{slug}.md(create or update) - Agent / subagent / role-based executor →
wiki/agents/{slug}.md(create or update) - Tool knowledge →
wiki/tools/{slug}.md(create or update; only for products, CLIs, SDKs, APIs, services, utilities) - Platform/env knowledge →
agent-config/platform.mdoragent-config/knowledge/{domain}.md(update) - If a source does not fit the installed taxonomy, ask before extending
schema.md; do not invent a private or organization-specific category.
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 · 256 lines · 67 tokens per session scan A ec5878bd549e
ingest is a skill published in the GitHub repository ozgurkarahan/ai-agent-memory (8 stars, last pushed 1mo ago), licensed MIT. It adds 67 tokens to every session and 3,681 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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