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ai-agent-memory: Skill for Claude Code

.claude/skills/ingest/SKILL.md

ingest is a skill for Claude Code from ozgurkarahan/ai-agent-memory. It costs 67 tokens per session (3,681 once invoked), scanned A, original, MIT.

A procedure for importing a source document into a structured personal wiki. It turns source material into pages with metadata and links, while supporting both one document and a batch of documents.

In plain words
What is it for?
Use it with “ingest [source]” or “mass ingest [batch].” It gathers context, plans how overlapping sources should be combined, creates or updates wiki pages, resolves links, runs a health check, and records the activity.
Why use it?
It removes the need to manually organise notes, connect related pages, and remember which sources were processed. It also includes checks to catch problems after the import.

Skill for Claude Code

Written for Claude Code: installed under .claude/. Also seen: mentions subagents.

This is ozgurkarahan/ai-agent-memory's own configuration. It tells Claude Code how to work on ai-agent-memory 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 ai-agent-memory configures →

not rated 8repo 3mo ago A scan 67 tokens original MIT
View source ↗ ozgurkarahan/ai-agent-memory
Reuse

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.

Copy the file
curl -O https://raw.githubusercontent.com/ozgurkarahan/ai-agent-memory/master/.claude/skills/ingest/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/ozgurkarahan/ai-agent-memory

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/ozgurkarahan/ai-agent-memory/ingest/github.svg)](https://agentmods.dev/skills/ozgurkarahan/ai-agent-memory/ingest)
Your own site
<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.

agentmods 80×15 button for ingest

Your own site · 80×15
<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>
Per session 67 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,681 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 $0.00067 $0.03681
Opus 5.5 $0.00027 $0.01472
Sonnet 5.5 $0.00013 $0.00736
Haiku 4.5 $0.00007 $0.00368

Measured 2mo ago against content hash ec5878bd549e, 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 · 256 lines

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

  1. If memory/schema.md exists in the current workspace, set WIKI_ROOT to memory/.
  2. Else if schema.md exists, set WIKI_ROOT to the current directory.
  3. Else follow the memory-wiki path declared in the current project's AGENT.md.
  4. If no folder containing both schema.md and index.md resolves, 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:

  1. List every source you intend to ingest (OneNote sections, files, URLs, query topics).
  2. Save the inventory to the session folder or as a temp list. Do NOT start pulling yet.
  3. If the source system can't be enumerated, explicitly state the limitation and the coverage estimate before proceeding.
  4. 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

  1. Read schema.md — understand wiki structure, categories, naming rules
  2. Read index.md — get the full list of existing articles with paths (needed for backlink resolution)
  3. Read glossary.md — check existing terms
  4. 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.md or agent-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.

Read the full file on GitHub · 256 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. 2mo ago First seen · 256 lines · 67 tokens per session scan A ec5878bd549e

Subscribe to this mod's changes

ingest is a skill published in the GitHub repository ozgurkarahan/ai-agent-memory (8 stars, last pushed 3mo ago), licensed MIT. It adds 67 tokens to every session and 3,681 once invoked, about $0.0003 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 2026-08-31.

DocumentsWriting and docs
Related

ingest compared with skills from other repositories

ingest next to 6 skills from other repositories
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ingestozgurkarahan/ai-agent-memorySkill83mo agoMITA67Claude Code
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feishuHmbown/CodeWhaleSkill41k5d agoMITA33Claude Code, Codex