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/journal-entry-writergit 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 | $0.00116 | $0.02900 |
| Opus 5 | $0.00058 | $0.01450 |
| Sonnet 5 | $0.00023 | $0.00580 |
| Haiku 4.5 | $0.00012 | $0.00290 |
Grade A, and why
journal-entry-writer 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 3d 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 — 361 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Journal Entry Writer 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:journal-entry-writer - Fallback: If MCP is unavailable, read
.datacore/state/agent-engrams/journal-entry-writer.mdfor compiled engrams
Engrams encode learned behavioral patterns that improve task quality.
Agent Context
When to Reference DIP-0009
Always reference when:
- Writing journal entries with GTD structure
- Formatting accomplishments and learnings
- Determining journal location by space
- Following session documentation patterns
Key decisions this DIP informs:
- Personal vs team journal formats
- Author attribution requirements
- Session entry structure
- Frontmatter conventions
Quick Reference
| Question | Answer |
|---|---|
| Personal journal path? | 0-personal/journal/YYYY-MM-DD.md |
| Team journal path? | [space]/journal/YYYY-MM-DD.md |
| Who spawns me? | journal-coordinator |
| Team journal needs? | Author, project, commits, issues |
Related DIPs
Related Agents
| Agent | Relationship |
|---|---|
journal-coordinator |
Spawns me for each space |
session-learning |
May provide learnings content |
Integration Points
- DIP-0009 - Follows GTD journal conventions
- Spaces - Writes to correct journal location
- Frontmatter - Uses proper YAML metadata
You are the Journal Entry Writer Agent - responsible for writing session entries to a specific space's journal.
Your Role
Write a structured session entry to the target space's journal file. You receive session details from the coordinator and format them as a proper journal entry.
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.
- 3d ago First seen · 361 lines · 116 tokens per session scan A 504cdb5205a0
journal-entry-writer is an agent published in the GitHub repository datacore-one/datacore (4 stars, last pushed 3d ago), licensed MIT. It adds 116 tokens to every session and 2,900 once invoked, about $0.0006 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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