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 commands/datacore-one/datacore/ingestgit 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/commands/datacore-one/datacore/ingest)<a href="https://agentmods.dev/commands/datacore-one/datacore/ingest"><img src="https://agentmods.dev/badge/commands/datacore-one/datacore/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 | $0.03546 |
| Opus 5 | $0.00010 | $0.01773 |
| Sonnet 5 | $0.00004 | $0.00709 |
| Haiku 4.5 | $0.00002 | $0.00355 |
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 today.
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 — 505 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/ingest Command
Command Context
When to Reference DIP-0015
Always reference when:
- Determining file destinations
- Creating companion files
- Routing to semantic folders
- Extracting knowledge (zettels, insights)
Key decisions this DIP informs:
- Semantic routing (active/knowledge/archive)
- Companion file format
- Tag application (DIP-0014)
- YAML frontmatter requirements
Quick Reference
| Question | Answer |
|---|---|
| Default inbox? | 0-inbox/ in each space |
| Active work? | 1-tracks/ or 1-active/ |
| Reference? | 3-knowledge/ |
| Archive? | 4-archive/ |
| What DIPs govern this? | DIP-0015 (Semantic Org), DIP-0014 (Tags) |
Agents This Command Invokes
| Agent | Purpose |
|---|---|
ingest-orchestrator |
Orchestration, planning (replaces ingest-coordinator, DIP-0021) |
knowledge-extractor |
Per-file knowledge extraction (replaces ingest-processor, DIP-0021) |
docx-reader |
DOCX conversion |
Integration Points
- DIP-0021 - Search & Research Architecture
- DIP-0015 - Semantic organization
- DIP-0014 - Tag taxonomy
- Git LFS - Large file handling
Process files from inbox folders or external sources into Datacore with deep knowledge extraction - not just file sorting, but reading, analyzing, extracting insights, and discovering actionable items.
Usage
/ingest [optional: folder path]
- Default: Processes
0-inbox/folders across all spaces - With path: Processes specified folder (e.g.,
~/Documents/Migration/)
Workflow
Pre-Phase: Goal Clarification
Before scanning, ask user about extraction goals:
What's your primary goal for this ingest?
1. Contact extraction - Build CRM entries from documents
2. Knowledge capture - Extract zettels, insights, literature notes
3. File organization - Route files to semantic destinations
4. Archive migration - Move historical content with minimal processing
5. All of the above - Comprehensive processing
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.
- today First seen · 505 lines · 19 tokens per session scan A 04878823fb46
ingest is a command published in the GitHub repository datacore-one/datacore (4 stars, last pushed today), licensed MIT. It adds 19 tokens to every session and 3,546 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 2026-09-03.
Other commands, from other repositories
prd-review
Review the active PRD with Codex and stream normalized findings to JSONL.
prd-archive
Archive the active PRD (blocked until every accepted finding has a receipt).
prd-map
Build a codebase map so PRDs are written with repo context, not blind.
prd-split
Split the approved PRD into one issue spec per manifest entry.
rca-check
Lint an RCA or premortem document against the canonical template.
prd-os-init
Initialize prd-os in this repo (writes .prd-os/config.json).