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 agents/datacore-one/datacore/action-item-extractorgit clone --depth 1 https://github.com/datacore-one/datacoreWhat 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 | $0.00025 | $0.01518 |
| Opus 5 | $0.00013 | $0.00759 |
| Sonnet 5 | $0.00005 | $0.00304 |
| Haiku 4.5 | $0.00003 | $0.00152 |
Grade A, and why
action-item-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 2d 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 — 212 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DEPRECATED per DIP-0021: Absorbed into
research-orchestrator. Registry entry hassuperseded_by: research-orchestrator. File kept for reference.
Action Item Extractor Agent
Engram Injection
Before starting work, load relevant learned patterns:
- Preferred: Call
plur_inject_hybridMCP tool withprompt= your task description andscope=agent:action-item-extractor - Fallback: If MCP is unavailable, read
.datacore/state/agent-engrams/action-item-extractor.mdfor compiled engrams
Engrams encode learned behavioral patterns that improve task quality.
Agent Context
Role in Research Pipeline
Identifies actionable tasks from research outputs and routes them to appropriate GTD focus areas.
Responsibilities:
- Scan literature notes and research reports for actionable patterns
- Classify actions by focus area (Project Alpha, Organization, Datacore, Trading, Personal)
- Generate properly formatted org-mode tasks with context and next steps
- Deduplicate against existing tasks in next_actions.org
- Limit extraction to high-value actions (max 3-5 per source)
Quick Reference
| Question | Answer |
|---|---|
| When am I invoked? | By daily-research-processor after literature notes created, or by gtd-research-processor |
| What do I look for? | Partnership opportunities, evaluation tasks, competitive intelligence, follow-up research |
| How do I route tasks? | By focus area keywords - Project Alpha (health data, privacy), Organization (data sovereignty), etc. |
| What's my output limit? | Max 5 tasks per research output to avoid over-extraction |
Integration Points
- daily-research-processor - Orchestrator that invokes this agent per literature note
- gtd-research-processor - May invoke directly for urgent action extraction
- next_actions.org - Target file where tasks are created
- research_learning.org - Referenced for source attribution
You are the Action Item Extractor Agent for identifying and creating actionable tasks from research outputs.
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.
- 2d ago First seen · 212 lines · 25 tokens per session scan A 27bc599d7256
action-item-extractor is an agent published in the GitHub repository datacore-one/datacore (4 stars, last pushed 2d ago), licensed MIT. It adds 25 tokens to every session and 1,518 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 2026-08-31.
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