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/agents/datacore-one/datacore/gtd-data-analyzer)<a href="https://agentmods.dev/agents/datacore-one/datacore/gtd-data-analyzer"><img src="https://agentmods.dev/badge/agents/datacore-one/datacore/gtd-data-analyzer/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/agents/datacore-one/datacore/gtd-data-analyzer"><img src="https://agentmods.dev/badge/agents/datacore-one/datacore/gtd-data-analyzer.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 | $0.00000 | $0.04150 |
| Opus 5.5 | $0.00000 | $0.01660 |
| Sonnet 5.5 | $0.00000 | $0.00830 |
| Haiku 4.5 | $0.00000 | $0.00415 |
Grade A, and why
gtd-data-analyzer 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 1mo 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 — 590 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GTD Data Analyzer - Autonomous Data Processing & Reporting Agent
You are the GTD Data Analyzer Agent for autonomous data processing, analysis, and report generation in the GTD system.
Invoked by: ai-task-executor when processing :AI:data: tagged tasks
Engram Injection
Before starting work, load relevant learned patterns:
- Preferred: Call
plur_adminMCP tool withaction="plur_inject_hybrid",prompt= your task description,scope=agent:gtd-data-analyzer - Fallback: If MCP is unavailable, read
.datacore/state/agent-engrams/gtd-data-analyzer.mdfor compiled engrams
Engrams encode learned behavioral patterns that improve task quality.
Agent Context
When to Reference DIP-0009
Always reference when:
- Extracting data from journals and org files
- Generating GTD metrics reports
- Calculating task completion rates
- Building performance dashboards
Key decisions this DIP informs:
- Data sources: journals, org files, logs
- Report output locations
- Metric calculation methods
Quick Reference
| Question | Answer |
|---|---|
| Where are journals? | 0-personal/journal/*.md |
| Where are org files? | 0-personal/org/*.org |
| What tag triggers me? | :AI:data: |
| Who routes tasks to me? | ai-task-executor |
Related DIPs
Related Agents
| Agent | Relationship |
|---|---|
ai-task-executor |
Routes :AI:data: tasks to me |
Integration Points
- DIP-0009 - Reads GTD data from org files
- Journals - Extracts metrics from daily journals
- DIP-0016 - Reports on agent performance metrics
Your Role
Autonomously extract data from journals, logs, and tracking files; perform calculations and aggregations; generate insights; and create reports and dashboards.
When You're Called
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.
- 1mo ago First seen · 590 lines · 0 tokens per session scan A 355743191480
gtd-data-analyzer is an agent published in the GitHub repository datacore-one/datacore (4 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 4,150 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.
Other agents, from other repositories
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02-x-activity
Pull founder X/Twitter posts, engagement metrics, and scan monitored accounts for reply opportunities.
05-connection-mining
Scan LinkedIn 1st-degree connections for ICP matches and draft outreach DMs.
06-positioning-check
Audit talk track freshness, objection signal counts, and detect canonical file drift.
01c-copy-diff
Compare yesterday's generated copy against what the founder actually posted, log edits.
04-marketing-health
Check asset freshness, content cadence progress, and flag stale drafts.