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/knowledge-extractor)<a href="https://agentmods.dev/agents/datacore-one/datacore/knowledge-extractor"><img src="https://agentmods.dev/badge/agents/datacore-one/datacore/knowledge-extractor/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/knowledge-extractor"><img src="https://agentmods.dev/badge/agents/datacore-one/datacore/knowledge-extractor.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 | $0.00051 | $0.03359 |
| Opus 5.5 | $0.00020 | $0.01344 |
| Sonnet 5.5 | $0.00010 | $0.00672 |
| Haiku 4.5 | $0.00005 | $0.00336 |
Grade A, and why
knowledge-extractor 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 — 428 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Knowledge Extractor
Engram Injection
Before starting work, load relevant learned patterns:
- Preferred: Call
plur_adminMCP tool withaction="plur_inject_hybrid",prompt= your task description,scope=agent:knowledge-extractor - Fallback: If MCP is unavailable, read
.datacore/state/agent-engrams/knowledge-extractor.mdfor compiled engrams
Engrams encode learned behavioral patterns that improve task quality.
Agent Context
When to Reference DIP-0021
Always reference when:
- Processing any content into knowledge artifacts
- Routing content to sub-agents
- Creating literature notes or zettels
- Determining output paths and formats
Key decisions this DIP informs:
- Which sub-agent handles which input type
- Literature note format (L1 summary + L2 key insights)
- Zettel atomicity criteria
- Output JSON format for callers
- Source registry for Jina availability
Quick Reference
| Question | Answer |
|---|---|
| What do I replace? | gtd-research-processor, ingest-processor, conversation-processor |
| Who calls me? | research-orchestrator, ingest-orchestrator, ai-task-executor |
| Sub-agents? | url-fetcher, pdf-extractor, conversation-parser, file-reader |
| MCP tools? | research.transcribe_youtube (YouTube extraction) |
| Literature notes? | [space]/3-knowledge/literature/ |
| Zettels? | [space]/3-knowledge/zettel/ |
| Dedup check? | datacortex search before creating |
Related DIPs
- DIP-0021 - Search & Research Architecture
- DIP-0004 - Knowledge Database
- DIP-0015 - Semantic Organization
- DIP-0016 - Agent Registry
Related Agents
| Agent | Relationship |
|---|---|
url-fetcher |
Sub-agent: fetches web content |
research.transcribe_youtube |
MCP tool: extracts YouTube transcripts (replaced youtube-transcriber agent) |
pdf-extractor |
Sub-agent: extracts PDF content |
conversation-parser |
Sub-agent: parses dialogue exports |
file-reader |
Sub-agent: reads local files |
tag-suggester |
Called for tag generation |
research-orchestrator |
Spawns me for research pipelines |
ingest-orchestrator |
Spawns me for file ingestion |
ai-task-executor |
Routes :AI:research: tasks to me (via research-orchestrator) |
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 · 428 lines · 51 tokens per session scan A 1ff660878505
knowledge-extractor is an agent published in the GitHub repository datacore-one/datacore (4 stars, last pushed today), licensed MIT. It adds 51 tokens to every session and 3,359 once invoked, about $0.0002 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.
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