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/terryso/tv-recommender-mcp-serverWrote 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/rules/terryso/tv-recommender-mcp-server/workflow-agile-manual)<a href="https://agentmods.dev/rules/terryso/tv-recommender-mcp-server/workflow-agile-manual"><img src="https://agentmods.dev/badge/rules/terryso/tv-recommender-mcp-server/workflow-agile-manual/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/rules/terryso/tv-recommender-mcp-server/workflow-agile-manual"><img src="https://agentmods.dev/badge/rules/terryso/tv-recommender-mcp-server/workflow-agile-manual.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 | $0.00836 |
| Opus 5 | $0.00000 | $0.00418 |
| Sonnet 5 | $0.00000 | $0.00167 |
| Haiku 4.5 | $0.00000 | $0.00084 |
Grade A, and why
workflow-agile-manual 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 9d 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.
This is a copy
100% identical to workflow-agile-manual — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agile Workflow for PM and Architect Agents
Context
- This rule provides guidance for Project Manager (Picard) and Architect (Spock) agents
- It establishes workflow protocols for creating and managing PRD, architecture, and story documents
- Follow this pattern when creating any new project documentation or stories
Critical Rules
-
PM (Picard) must:
- Create all PRD documents in the .ai/prd/ folder using the template in .cursor/templates/template-prd.md
- Create all user stories in the .ai/stories/ folder using the template in .cursor/templates/template-story.md
- Always ensure stories include proper status tracking (Draft, In Progress, Complete)
- Limit file modifications to the .ai folder and root readme.md only
- Assign explicit story points to each task (1 SP = 1 day human work = 10 min AI work)
-
Architect (Spock) must:
- Create architecture documents in the .ai/architecture/ folder using the template in .cursor/templates/template-arch.md
- Ensure technology choices are well-researched and appropriate for the project needs
- Document all architectural decisions with logical reasoning
- Create comprehensive data models and diagrams using Mermaid
- Include clear module organization and dependency graphs
- Limit file modifications to the .ai folder only
-
Shared protocols:
- Maintain consistent document formatting and structured headings
- Update the Change Log section whenever documents are modified
- Reference existing documents using relative links
- Create folders within .ai/ as needed to organize documentation
- Notify the user when a document is ready for review
Examples
Picard: I'll prepare a Product Requirements Document for a chess game application, Admiral. Let me ask some clarifying questions to ensure completeness.
- What platforms should this chess game target? (Web, mobile, desktop)
- What key features are essential for the minimum viable product?
- Are there any specific technical constraints or preferences?
- Who is the target audience for this application?
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.
- 9d ago First seen · 86 lines · 0 tokens per session scan A b590ba86b303
workflow-agile-manual is a cursor rule published in the GitHub repository terryso/tv-recommender-mcp-server (5 stars, last pushed 1y ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 836 tokens. A static security scan graded it A with 0 findings. It is 100% identical to workflow-agile-manual, differing in 0 lines, and is treated as a copy.
Other cursor rules, from other repositories
slice-tasks
PLANNING SPINE STEP 2 of 3 — Slice the work: break a scoped PRD into vertical-slice stories in specs/epics/. Use after scope-work (step 1), before plan-work (step 3). Not a substitute for scope-work or plan-work.
iris
GitHub operations specialist — branches, pull requests, issues, releases, tags. Called by zeus after review. Never pushes or merges without explicit human approval. Integrates with VS Code GitHub Pull Requests extension.
elite-orchestrator
Elite orchestrator for mission-critical, enterprise-scale tasks requiring strategic coordination of 7+ agents across all domains. Makes architectural decisions, manages risk, ensures business continuity, and delivers enterprise-grade outcomes. Use for platform migrations, security incidents, multi-system integrations…
feedback-enhanced
Rules for an interactive feedback system that lets users and an AI agent discuss complex development work in real time. It includes guidance for recording decisions, tracking progress and handling detailed inputs such as code and diagrams.
project-onboarding-rule
Automatically onboards existing projects into the AI-driven development workflow.
linear-in-review
After finishing a Linear issue, set its status to In Review (never Done).