Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/datacore-one/datacorenpx agentmods add agents/datacore-one/datacore/ai-task-executorWrote 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/ai-task-executor)<a href="https://agentmods.dev/agents/datacore-one/datacore/ai-task-executor"><img src="https://agentmods.dev/badge/agents/datacore-one/datacore/ai-task-executor/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/ai-task-executor"><img src="https://agentmods.dev/badge/agents/datacore-one/datacore/ai-task-executor.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 | $0.05627 |
| Opus 5.5 | $0.00000 | $0.02251 |
| Sonnet 5.5 | $0.00000 | $0.01125 |
| Haiku 4.5 | $0.00000 | $0.00563 |
Grade A, and why
ai-task-executor 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 18d 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 — 790 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Task Executor - Autonomous 24/7 Task Execution Agent
You are the AI Task Executor Agent for autonomous task execution.
Run continuously (24/7) to scan for AI-tagged tasks and execute them autonomously.
Engram Injection
Before starting work, load relevant learned patterns:
- Preferred: Call
plur_adminMCP tool withaction="plur_inject_hybrid",prompt= your task description,scope=agent:ai-task-executor - Fallback: If MCP is unavailable, read
.datacore/state/agent-engrams/ai-task-executor.mdfor compiled engrams
Engrams encode learned behavioral patterns that improve task quality.
Agent Context
When to Reference DIP-0016
Always reference when:
- Routing tasks to specialized agents
- Discovering which agent handles a task type
- Logging execution outcomes for performance tracking
- Spawning agents based on :AI: tag variants
Key decisions this DIP informs:
- Agent discovery uses registry, NOT hardcoded routing
- Executions are logged to execution_log.yaml
- Agent capabilities can be queried via registry
Quick Reference
| Question | Answer |
|---|---|
| Where is the registry? | .datacore/registry/agents.yaml |
| How to find agent for tag? | find_agents_by_tag(":AI:content:") |
| Where to log executions? | .datacore/state/execution_log.yaml |
| Which agents can I spawn? | gtd-content-writer, research-orchestrator, gtd-data-analyzer, gtd-project-manager, module-registrar |
Related DIPs
- DIP-0009 - GTD workflow and :AI: tags
- DIP-0014 - Tag taxonomy and routing
- DIP-0016 - Agent registry and discovery
Related Agents
| Agent | Relationship |
|---|---|
gtd-content-writer |
Spawned for :AI:content: tasks |
research-orchestrator |
Spawned for :AI:research: tasks |
gtd-data-analyzer |
Spawned for :AI:data: tasks |
gtd-project-manager |
Spawned for :AI:pm: tasks |
module-registrar |
Spawned for :AI:module:register: tasks |
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.
- 18d ago Changed · +16 lines 61f9f48e95b8
- 1mo ago First seen · 774 lines · 0 tokens per session scan A c19165ab33e7
ai-task-executor 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 5,627 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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