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 rules/redhat-vmeperf/agent-tasks-template/projectgit clone --depth 1 https://github.com/redhat-vmeperf/agent-tasks-templateWhat 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 | $0.00419 | $0.00419 |
| Opus 5 | $0.00210 | $0.00210 |
| Sonnet 5 | $0.00084 | $0.00084 |
| Haiku 4.5 | $0.00042 | $0.00042 |
Grade A, and why
project 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.
What it actually says
Agent Tasks Framework
This project uses the .agents/ framework for structured AI-assisted software development. All agents must read the shared context files before beginning any gate work.
Shared Context (read before every task)
.agents/CYNEFIN.md— Problem classification framework (5 domains).agents/PERSONALITY.md— Shared agent identity, values, and behavioral commitments.agents/LESSONS.md— Accumulated lessons from past sessions (index).agents/REQUIREMENTS.md— Non-negotiable project requirements (index).agents/SECURITY_REVIEW_CHECKLIST.md— Security review process for external context files
Framework Structure
.agents/pipelines/— Pipeline process definitions (SDLC, Jira, Skill Generation).agents/roles/— Role-specific instructions for each SDLC gate (Architect, Security Architect, Team Lead, Engineer, Code Reviewer, Quality Engineer, Security Auditor).agents/requirements/— Individual requirement definitions (REQ-001 through REQ-009).agents/lessons/— Themed lesson files (architecture, code-quality, communication, implementation, process, security)
Available Workflows
The following workflows are defined as separate Cursor rules. Ask the AI to run them by name or describe the workflow you need:
- SDLC pipeline — 7-gate software development lifecycle with human approval gates
- SDLC task — Autonomous SDLC pipeline without human approval gates
- Jira — 3-gate Jira ticket creation pipeline
- Skillgen — 4-gate skill generation pipeline
- Security review file — Review a file for prompt injection and security issues
Project-Specific Context
See AGENTS.md for project overview, tech stack, and development conventions. Update that file to reflect your project's context.
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 · 38 lines · 419 tokens per session scan A 9b76e910ecff
project is a cursor rule published in the GitHub repository redhat-vmeperf/agent-tasks-template (3 stars, last pushed 4mo ago), licensed Unlicense. It adds 419 tokens to every session, about $0.0021 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 2026-08-31.
Other cursor rules, from other repositories
050-plan
When the user types /plan or asks to create a project plan, feature PRD, or retrospective.
dreamd-recall
Recall lessons, decisions, and prior context from the .agent/ memory daemon. Use when starting work in a project that has a .agent/ folder, when the user references a past decision, or when you are about to make a choice that has a documented prior.
session-memory
Use at conversation wrap-up or when the user explicitly indicates end-of-session — capture residual lessons not captured in-flight.
context-recorder-system
Context Recorder System (记录员系统) - 模块化索引文件.
rules
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
misc-documenting-learnings-and-clarifying-assumptions
Documenting Learnings and Clarifying Assumptions for Efficient Task Execution.