Borrowing it
Nothing to install: this file belongs to xcota/pos. 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/xcota/pos/main/.claude/skills/ingest/SKILL.mdgit clone --depth 1 https://github.com/xcota/posWrote 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/xcota/pos/ingest)<a href="https://agentmods.dev/skills/xcota/pos/ingest"><img src="https://agentmods.dev/badge/skills/xcota/pos/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/xcota/pos/ingest"><img src="https://agentmods.dev/badge/skills/xcota/pos/ingest.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00019 | $0.01040 |
| Opus 5.5 | $0.00008 | $0.00416 |
| Sonnet 5.5 | $0.00004 | $0.00208 |
| Haiku 4.5 | $0.00002 | $0.00104 |
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 1mo 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.
The source is not reproduced here
Licensed AGPL-3.0
The repository is licensed AGPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
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.
- 1mo ago First seen · 59 lines · 19 tokens per session scan A 1af28baa6571
ingest is a skill published in the GitHub repository xcota/pos (32 stars, last pushed 10d ago), licensed AGPL-3.0. It adds 19 tokens to every session and 1,040 once invoked, about $0.0001 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.
Other skills, from other repositories
ori-memory
Persistent agent memory with learning retrieval. Knowledge graph on markdown files — capture insights, decisions, research, and learnings during work, then retrieve them weeks or months later. Use when knowledge is too valuable to lose but too much to inject into every prompt.
binder-import
Import external data into a Binder workspace. Handles CSV, JSON, YAML, Markdown files, and directories of Markdown. Use when asked to "import data", "load records from a file", "ingest documents", "migrate data into binder", or bulk-create records from an external source.
goodmem:help
Overview of all available GoodMem skills and what a user needs to get started. Invoke when a user asks what GoodMem skills are available, how to get started, or what they need to set up.
capture-knowledge
A guide for saving useful, reusable knowledge from conversations and documents into a structured personal knowledge base. It separates original source material from concise pages about individual concepts.
graph-ask
Ask any natural language question about the memory graph. You generate Cypher directly and execute it. Use when the user has a complex or ad-hoc question that the standard graph tools don't cover.
ingest-audio
Transcribe a local audio or video file using Whisper and ingest it into the memory graph. Use when the user has a local MP3, WAV, M4A, MP4, or similar audio/video file they want to add to their knowledge graph.