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/knowledge-promotergit 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 | $0.00000 | $0.00606 |
| Opus 5 | $0.00000 | $0.00303 |
| Sonnet 5 | $0.00000 | $0.00121 |
| Haiku 4.5 | $0.00000 | $0.00061 |
Grade A, and why
knowledge-promoter 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 2d 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 — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Knowledge Promoter
Scans journal entries for high-value content and promotes it to permanent knowledge artifacts via knowledge-extractor.
When to Use
- Weekly review (automated scan of past week's journals)
- Manual
/promotecommand for on-demand promotion - Nightshift weekly task (scheduled Sunday night)
Inputs
- Space: Target space to scan (default: detect from cwd)
- Period: How far back to scan (default: 7 days)
- Threshold: Minimum promotion score (default: 0.4)
Workflow
- Scan: Run
journal_scanner.pyon journal entries within the period - Present: Show scored sections to user with promotion recommendations
- Confirm: User approves/rejects each candidate (or auto-approve in nightshift mode)
- Extract: For each approved section, spawn
knowledge-extractorwith:- Input: the journal section text
- Source type: "journal entry"
- Target space: same space as journal
- Instruction: create zettels for reusable concepts, skip literature note (journal IS the source record)
- Link back: Add
Promoted: [[Zettel Name]]annotation to original journal section - Log: Append promotion record to journal for the day
Heuristics (journal_scanner.py)
The scanner uses pattern matching, not LLM inference:
- Positive: root cause analysis, architecture decisions, research findings, wiki-links, code blocks, paper references, substantial bullet lists
- Negative: standup notes, WIP items, quick syncs
- Length bonus: longer sections score higher (capped)
- Threshold 0.4 = moderate confidence. Adjust per space.
Modes
Interactive (/promote)
- Shows each candidate with score and preview
- User confirms or skips each one
- Can override threshold:
/promote --threshold 0.6 - Can target specific date:
/promote --date 2026-04-01
Nightshift (autonomous)
- Runs with threshold 0.6 (higher bar for unsupervised)
- Auto-approves all candidates above threshold
- Creates promotion summary in morning briefing
- Skips sections already annotated with
Promoted:
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.
- 2d ago First seen · 67 lines · 0 tokens per session scan A 8075872f32d3
knowledge-promoter is an agent published in the GitHub repository datacore-one/datacore (4 stars, last pushed 2d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 606 tokens. 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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