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/commands/datacore-one/datacore/learn)<a href="https://agentmods.dev/commands/datacore-one/datacore/learn"><img src="https://agentmods.dev/badge/commands/datacore-one/datacore/learn/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/commands/datacore-one/datacore/learn"><img src="https://agentmods.dev/badge/commands/datacore-one/datacore/learn.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 | $0.00004 | $0.01275 |
| Opus 5.5 | $0.00002 | $0.00510 |
| Sonnet 5.5 | $0.00001 | $0.00255 |
| Haiku 4.5 | $0.00000 | $0.00128 |
Grade A, and why
learn 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 — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/learn
Capture an engram into PLUR memory.
Purpose
/learn is the front door to your persistent AI memory. Use it to record corrections, preferences, decisions, conventions, or any reusable knowledge that should survive across sessions and projects.
This command wraps mcp__plur__plur_learn with sensible defaults so you can capture in one line without thinking about engram metadata.
Note: this is the Datacore /learn command (PLUR-backed). It explicitly supersedes the gstack
learnskill. Do not invokegstack:learn— engrams are the canonical learning artifact in this Datacore installation.
When to use
- User corrects you ("no, use X not Y") → capture the correction immediately
- User states a preference ("always do X", "never do Y") → capture
- You discover a codebase convention or pattern that future sessions should know
- You make a decision worth preserving (architectural, naming, branding)
- You want to remember a competitive insight, market datum, or contact detail
- You realize you got something wrong in this session and want it recorded so you don't repeat it
If unsure whether something is worth saving: it probably is. Engrams are cheap; re-deriving lost context is expensive.
Usage
Quick capture
/learn The current price of X is €Y as of 2026-05-07
The command will:
- Call
mcp__plur__plur_learnwith the statement - Auto-classify type (terminological/behavioral/procedural/architectural) from content
- Default scope to
global - Return the engram ID
Structured capture
When the user provides additional metadata in natural language, parse it:
/learn [decided] [domain: cos.pricing] €19 entry tier is correct, €200 floor was wrong
Recognized inline modifiers:
[exploring]/[leaning]/[decided]/[locked]→ commitment level[domain: x.y]→ domain tag[scope: project:name]→ custom scope[type: behavioral|terminological|procedural|architectural]→ explicit type[tags: a,b,c]→ searchable tags[derived_from: ENG-id]→ link to source engram[pinned]→ always-load flag (use sparingly — meta-rules only)
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 Changed 71cfc3640a1c
- 1mo ago First seen · 122 lines · 4 tokens per session scan A 4b0032ef7883
learn is a command published in the GitHub repository datacore-one/datacore (4 stars, last pushed yesterday), licensed MIT. It adds 4 tokens to every session and 1,275 once invoked, about $0.0000 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 commands, from other repositories
compress
Save this Claude Code session to the vault and update the semantic memory index.
checkpoint
Save a mid-session checkpoint — for continuity between sessions of the same day.
recall
Recall memories with filtering and search.
query
Structured frontmatter queries using yq.
search
Full-text search across all memories.
resume
Load context from the vault before starting work.