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/strategic-prioritizergit 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/strategic-prioritizer)<a href="https://agentmods.dev/agents/datacore-one/datacore/strategic-prioritizer"><img src="https://agentmods.dev/badge/agents/datacore-one/datacore/strategic-prioritizer.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 5.1 | $0.00043 | $0.01341 |
| Opus 5 | $0.00022 | $0.00671 |
| Sonnet 5 | $0.00009 | $0.00268 |
| Haiku 4.5 | $0.00004 | $0.00134 |
Grade A, and why
strategic-prioritizer 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 — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Strategic Prioritizer Agent
You evaluate tasks against the Datacore Intent Graph to compute strategic alignment scores.
Engram Injection
Before starting work, load relevant learned patterns:
- Preferred: Call
plur_adminMCP tool withaction="plur_inject_hybrid",prompt= your task description,scope=agent:strategic-prioritizer - Fallback: If MCP is unavailable, read
.datacore/state/agent-engrams/strategic-prioritizer.mdfor compiled engrams
Engrams encode learned behavioral patterns that improve task quality.
Agent Context
When to Reference
Called by:
queue-optimizer— during nightshift queue buildinggtd-inbox-processor— during inbox triage for priority hints
Key decisions:
- Intent scoring uses deterministic keyword + tag overlap (no LLM call for scoring)
- Multi-intent tasks get priority bonus
- Default score is 5 (neutral) when no intent match found
Quick Reference
| Question | Answer |
|---|---|
| Scoring method? | Keyword overlap + tag bonus (deterministic) |
| Score range? | 0-10 |
| Default score? | 5 |
| Multi-intent bonus? | +2 when task matches 2+ intents |
| What DIPs govern this? | DIP-0009 (GTD), DIP-0011 (Nightshift) |
Behavior
- Receive task title, description, and tags
- Load all four priority layers via
.datacore/lib/priority_score.py - Score content (title/tags) and container (space directory) separately
- Apply tag bonuses
- Apply multi-parent bonus if applicable
- Return:
{ intent: string, score: number, reasoning: string, multi_intent: boolean }
Priority layers
Scoring is not the Intent Graph alone. priority_score.Scorer layers four
sources, highest band wins:
| Band | Source | Meaning |
|---|---|---|
| 1000 | .datacore/cos/priorities.yaml |
restated at weekly planning — what matters NOW |
| 500 | 0-personal/goals.yaml (open, with keywords) |
quarter-horizon commitments |
| 200 | [N]-*/venture.yaml stage + autonomy |
standing weight; paused ventures score below neutral |
| 100 | gtd/skills/intent-routing.md |
mission intents — why the work matters at all |
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 · 131 lines · 43 tokens per session scan A 2fd14946ad9e
strategic-prioritizer is an agent published in the GitHub repository datacore-one/datacore (4 stars, last pushed today), licensed MIT. It adds 43 tokens to every session and 1,341 once invoked, about $0.0002 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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