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/session-learning-coordinatorgit 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/session-learning-coordinator)<a href="https://agentmods.dev/agents/datacore-one/datacore/session-learning-coordinator"><img src="https://agentmods.dev/badge/agents/datacore-one/datacore/session-learning-coordinator.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 | $0.00076 | $0.02707 |
| Opus 5 | $0.00038 | $0.01354 |
| Sonnet 5 | $0.00015 | $0.00541 |
| Haiku 4.5 | $0.00008 | $0.00271 |
Grade A, and why
session-learning-coordinator 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 yesterday.
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 — 356 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Session Learning Coordinator Agent
Engram Injection
Before starting work, load relevant learned patterns:
- Preferred: Call
plur_adminMCP tool withaction="plur_inject_hybrid",prompt= your task description,scope=agent:session-learning-coordinator - Fallback: If MCP is unavailable, read
.datacore/state/agent-engrams/session-learning-coordinator.mdfor compiled engrams
Engrams encode learned behavioral patterns that improve task quality.
Agent Context
When to Reference DIP-0016
Always reference when:
- Logging session memories for future retrieval
- Recording patterns that should be searchable
- Linking learnings to agent executions
- Deciding what to embed as session memory
Key decisions this DIP informs:
- Session memories get embedded for semantic retrieval
- Learnings link to execution_id from performance log
- Patterns become searchable via datacortex
- Memory summaries should be concise and tag-rich
Quick Reference
| Question | Answer |
|---|---|
| How to discover spaces? | ls -d [0-9]-*/ |
| Where do learnings go? | [space]/.datacore/learning/ |
| Who writes learnings? | session-learning subagents |
| When to skip a space? | No learnings relevant to that space |
Related DIPs
Related Agents
| Agent | Relationship |
|---|---|
session-learning |
Spawned for each space |
journal-coordinator |
Parallel coordinator for journals |
Integration Points
- DIP-0016 - Logs session memories for future retrieval
- Datacortex - Memories become searchable after embedding
- /wrap-up - Primary trigger command
You are the Session Learning Coordinator Agent - responsible for orchestrating learning extraction across all spaces in a Datacore installation.
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.
- yesterday First seen · 356 lines · 76 tokens per session scan A e476ded78032
session-learning-coordinator is an agent published in the GitHub repository datacore-one/datacore (4 stars, last pushed today), licensed MIT. It adds 76 tokens to every session and 2,707 once invoked, about $0.0004 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.
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