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.
npx agentmods add skills/xcota/pos/ingestnpx skills add xcota/pos --skill ingestgit 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.svg" alt="Measured on agentmods" 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 | $0.00019 | $0.01040 |
| Opus 5 | $0.00010 | $0.00520 |
| Sonnet 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 4d 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 — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/ingest — Structured Data Extraction
Extract structured knowledge from raw data sources into the knowledge graph.
Arguments
[source_type]— source kind: telegram | transcript | youtube | notes | bio | survey | audio[path]— path to the file or directory holding the data[topic]— optional topic/context to focus extraction
Pipeline
Phase 1: R2C (Raw → Characteristics)
- Detect source type (auto-detect, or from the argument).
- YouTube URL: create
inbox/youtube/{slug}_{video_id}/, fetch auto-subs withyt-dlp --skip-download --write-auto-subs --sub-langs "en.*,<primary>.*" --sub-format vtt(use the subject's primary language fromcontext/identity.mdif profiled, elseen; runyt-dlp --list-subs <url>first if unsure). Needsyt-dlpinstalled. If no.vttis produced (yt-dlp exits 0 even when it finds nothing), do NOT fabricate a transcript — mark the sourceunresolvedand ask the user to paste one. Otherwise normalize VTT intotranscript.md+transcript.txt, then process astranscript.
- YouTube URL: create
- Load the source-specific extraction prompt from
projects/ingest/prompts/{source_type}.md.- If
projects/ingest/prompts/youtube.mdis missing, usetranscript.mdand add focus on: source metadata, chapter outline, reusable concepts, architecture deltas, action items.
- If
- Split input into chunks (~1500 tokens, with overlap).
- Parallel subagents: each chunk × each dimension —
- bio — biographical facts, events, chronology
- patterns — patterns of behavior, decisions, thinking
- values — values, beliefs, priorities
- connections — people, relationships, social graph
- insights — insights, unique ideas, non-obvious observations
- Output format per chunk:
date/period – dimension – fact/observation – "evidence quote" – confidence (0-1)
Phase 2: C2F (Characteristics → Final)
- Collect all Phase 1 results.
- Deduplicate: merge identical facts, raise confidence.
- Consolidate: group by topic/entity.
- Create/update files in
knowledge/:- New entity →
knowledge/{type}/{entity-name}.mdwith frontmatter +[[wikilinks]]. - Existing entity → append/merge new facts.
- Wikilink validation: every
[[link]]must point at a file that exists.
- New entity →
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.
- 4d ago First seen · 59 lines · 19 tokens per session scan A 1af28baa6571
ingest is a skill published in the GitHub repository xcota/pos (43 stars, last pushed 1mo ago), licensed MIT. It adds 19 tokens to every session and 1,040 once invoked, about $0.0001 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 2026-08-30.
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