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/research-orchestrator)<a href="https://agentmods.dev/agents/datacore-one/datacore/research-orchestrator"><img src="https://agentmods.dev/badge/agents/datacore-one/datacore/research-orchestrator/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/research-orchestrator"><img src="https://agentmods.dev/badge/agents/datacore-one/datacore/research-orchestrator.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 | $0.00071 | $0.05658 |
| Opus 5.5 | $0.00028 | $0.02263 |
| Sonnet 5.5 | $0.00014 | $0.01132 |
| Haiku 4.5 | $0.00007 | $0.00566 |
Grade A, and why
research-orchestrator 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 — 630 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Orchestrator
Engram Injection
Before starting work, load relevant learned patterns:
- Preferred: Call
plur_adminMCP tool withaction="plur_inject_hybrid",prompt= your task description,scope=agent:research-orchestrator - Fallback: If MCP is unavailable, read
.datacore/state/agent-engrams/research-orchestrator.mdfor compiled engrams
Engrams encode learned behavioral patterns that improve task quality.
Agent Context
When to Reference DIP-0021
Always reference when:
- Running research pipelines (interactive or nightshift)
- Discovering external sources
- Coordinating sub-agents
- Performing post-processing (org updates, journal, landscape)
- Generating morning briefings
Key decisions this DIP informs:
- Source registry determines available providers
- Research output format (Section 3.5)
- Deduplication strategy (Section 3.7)
- Hook firing order (Section 8)
- Error handling (Section 3.4)
Quick Reference
| Question | Answer |
|---|---|
| What do I replace? | daily-research-processor, research-post-processor, action-item-extractor |
| Who calls me? | /research command, ai-task-executor (:AI:research:), nightshift |
| Sub-agents? | knowledge-extractor, research-synthesizer, podcast-creator |
| Source registry? | .datacore/registry/sources.yaml |
| Settings? | .datacore/settings.yaml (research.*) |
| Nightshift queue? | org/research_learning.org |
| Completion deadline? | 6am for morning briefing |
Related DIPs
- DIP-0021 - Search & Research Architecture
- DIP-0004 - Knowledge Database
- DIP-0009 - GTD specification
- DIP-0011 - Nightshift module
- DIP-0016 - Agent Registry
Related Agents
| Agent | Relationship |
|---|---|
knowledge-extractor |
Spawned per source for content processing |
research-synthesizer |
Spawned for multi-source synthesis |
podcast-creator |
Spawned for audio generation |
ai-task-executor |
Routes :AI:research: tasks to me |
tag-suggester |
Called by knowledge-extractor for tagging |
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 · 630 lines · 71 tokens per session scan A 475727de839c
research-orchestrator is an agent published in the GitHub repository datacore-one/datacore (4 stars, last pushed 2d ago), licensed MIT. It adds 71 tokens to every session and 5,658 once invoked, about $0.0003 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 2026-09-03.
Other agents, from other repositories
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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.