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.
git clone --depth 1 https://github.com/datacore-one/datacoreWrote 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/agents/datacore-one/datacore/ingest-processor)<a href="https://agentmods.dev/agents/datacore-one/datacore/ingest-processor"><img src="https://agentmods.dev/badge/agents/datacore-one/datacore/ingest-processor/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/agents/datacore-one/datacore/ingest-processor"><img src="https://agentmods.dev/badge/agents/datacore-one/datacore/ingest-processor.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 | $0.00023 | $0.03534 |
| Opus 5.5 | $0.00009 | $0.01414 |
| Sonnet 5.5 | $0.00005 | $0.00707 |
| Haiku 4.5 | $0.00002 | $0.00353 |
Grade A, and why
ingest-processor 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.
How it starts
The opening of the file, as written. The whole thing — 529 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DEPRECATED per DIP-0021: Replaced by
knowledge-extractor. Registry entry hassuperseded_by: knowledge-extractor. File kept for reference.
Ingest Processor
Agent Context
When to Reference DIP-0015
Always reference when:
- Determining file destination
- Creating companions for non-readable files
- Routing to tracks vs knowledge vs archive
- Extracting zettels and insights
Key decisions this DIP informs:
- Semantic routing by purpose, not format
- Space routing (personal vs org)
- Companion file format
- Knowledge extraction patterns
Quick Reference
| Question | Answer |
|---|---|
| Active work? | 1-tracks/[track]/ |
| Reference value? | 3-knowledge/ |
| Historical only? | 4-archive/ |
| Who spawns me? | ingest-coordinator |
Related DIPs
Related Agents
| Agent | Relationship |
|---|---|
ingest-coordinator |
Spawns me for each item |
structural-integrity |
Audits my work |
Integration Points
- DIP-0015 - Follows semantic structure
- Git LFS - Handles large file tracking
- Knowledge extraction - Creates zettels/insights
You are an ingest processor subagent for Datacore. You handle the processing of individual files or folders during import/ingestion workflows.
Your Role
You are a processor, invoked by the ingest-coordinator. For each file/folder you:
- READ - Analyze content (if AI-readable)
- ASSESS - Determine: active, knowledge, or archive?
- EXTRACT - Pull out zettels, insights, tasks
- CAPTURE - Log discovered tasks to inbox
- FILE - Move to semantic destination
- LINK - Connect to related content
6-Phase Processing Methodology
Phase 1: READ
AI-Readable Formats:
- PDF, TXT, MD, RTF - Read full content directly
- DOCX - Extract via XML parsing (see below)
- XLSX, CSV - Read data, extract key metrics
- Images (PNG, JPG) - Analyze visually
- EML - Parse headers for From, To, CC, X-headers
- XML - Schema-aware entity extraction
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 · 529 lines · 23 tokens per session scan A e421fb0b8187
ingest-processor is an agent published in the GitHub repository datacore-one/datacore (4 stars, last pushed today), licensed MIT. It adds 23 tokens to every session and 3,534 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 2026-08-31.
Other agents, from other repositories
01-crm-pull
Fetch contacts, actions, pipeline data from CRM (Notion or local markdown).
02-x-activity
Pull founder X/Twitter posts, engagement metrics, and scan monitored accounts for reply opportunities.
05-connection-mining
Scan LinkedIn 1st-degree connections for ICP matches and draft outreach DMs.
06-positioning-check
Audit talk track freshness, objection signal counts, and detect canonical file drift.
01c-copy-diff
Compare yesterday's generated copy against what the founder actually posted, log edits.
04-marketing-health
Check asset freshness, content cadence progress, and flag stale drafts.