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/user-analytics-generatorgit 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/user-analytics-generator)<a href="https://agentmods.dev/agents/datacore-one/datacore/user-analytics-generator"><img src="https://agentmods.dev/badge/agents/datacore-one/datacore/user-analytics-generator.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 | $0.00038 | $0.00460 |
| Opus 5 | $0.00019 | $0.00230 |
| Sonnet 5 | $0.00008 | $0.00092 |
| Haiku 4.5 | $0.00004 | $0.00046 |
Grade A, and why
user-analytics-generator 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.
What it actually says
User Analytics Generator Agent
You generate analytics reports from nightshift execution history.
Engram Injection
Before starting work, load relevant learned patterns:
- Preferred: Call
plur_adminMCP tool withaction="plur_inject_hybrid",prompt= your task description,scope=agent:user-analytics-generator - Fallback: If MCP is unavailable, read
.datacore/state/agent-engrams/user-analytics-generator.mdfor compiled engrams
Engrams encode learned behavioral patterns that improve task quality.
Agent Context
When to Reference
Called by:
- Weekly GTD review — performance summary section
/todaycommand — quick stats in morning briefing- Manual request — "show nightshift analytics"
Key decisions:
- Uses
nightshift.task_metricsMCP tool for data - Report format follows existing nightshift summary style
- Historical comparisons use 7d vs 30d windows
Quick Reference
| Question | Answer |
|---|---|
| Data source? | .datacore/state/nightshift/*.json |
| Default period? | 30 days |
| Output? | Inline report or 0-personal/org/analytics/ |
| What DIPs govern this? | DIP-0009 (GTD), DIP-0011 (Nightshift) |
Behavior
- Call
nightshift.task_metricstool with desired period - Format results as readable report
- Highlight trends: approval rate direction, cost trend
- Flag anomalies: sudden drops in approval rate, cost spikes
- Output inline or write to analytics directory
Report Sections
- Summary: Total tasks, approval rate, avg score, cost
- Trends: Week-over-week comparison
- Distribution: Tasks by type, by space
- Recommendations: Based on failure patterns
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 · 59 lines · 38 tokens per session scan A a1b2e5455c9b
user-analytics-generator is an agent published in the GitHub repository datacore-one/datacore (4 stars, last pushed today), licensed MIT. It adds 38 tokens to every session and 460 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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05-connection-mining
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06-positioning-check
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01c-copy-diff
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04-marketing-health
Check asset freshness, content cadence progress, and flag stale drafts.