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/create-spacegit 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 | $0.00110 | $0.02869 |
| Opus 5 | $0.00055 | $0.01435 |
| Sonnet 5 | $0.00022 | $0.00574 |
| Haiku 4.5 | $0.00011 | $0.00287 |
Grade A, and why
create-space 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 — 353 lines — stays where its author put it; the contents beside it link to each section on GitHub.
create-space 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:create-space - Fallback: If MCP is unavailable, read
.datacore/state/agent-engrams/create-space.mdfor compiled engrams
Engrams encode learned behavioral patterns that improve task quality.
Agent Context
When to Reference DIP-0002
Always reference when:
- Creating CLAUDE layer files (base, space, local)
- Setting up .gitignore for composed files
- Understanding what should be tracked vs gitignored
Key decisions this DIP informs:
- CLAUDE.base.md vs CLAUDE.space.md separation
- What gets gitignored (composed CLAUDE.md, *.local.md)
- Layer inheritance and composition rules
Quick Reference
| Question | Answer |
|---|---|
| Where do spaces live? | ~/Data/[N]-[name]/ |
| Required files? | CLAUDE.base.md, CLAUDE.space.md, config.yaml, org/*.org, .gitignore |
| What gets gitignored? | CLAUDE.md, *.local.md, 2-projects/, .datacore/learning/ |
| Template source? | GitHub: datacore-one/datacore-org (fallback: .datacore/templates/space/) |
| Space numbering? | Sequential: 0-personal, 1-[first], 2-[second], etc. |
Related DIPs
Related Agents
| Agent | Relationship |
|---|---|
context-maintainer |
Rebuilds CLAUDE.md after space creation |
structural-integrity |
Can also audit space folder structure |
Integration Points
- datacore-org repo - Primary template source for new spaces
- install.yaml - Space registration (if exists)
- context_merge.py - Generates composed CLAUDE.md from layers
Trigger
- User says "create a new space", "set up a space", "new team space"
:AI:space:create:or:AI:space:audit:tag in org-mode tasks- Direct invocation when user wants to fix a broken space
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 · 353 lines · 110 tokens per session scan A 7ce3d40ce4b3
create-space is an agent published in the GitHub repository datacore-one/datacore (4 stars, last pushed 2d ago), licensed MIT. It adds 110 tokens to every session and 2,869 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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