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/votruongdanh/skills-agent/plangit clone --depth 1 https://github.com/VoTruongDanh/Skills-AgentWrote 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/votruongdanh/skills-agent/plan)<a href="https://agentmods.dev/rules/votruongdanh/skills-agent/plan"><img src="https://agentmods.dev/badge/rules/votruongdanh/skills-agent/plan.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 5.1 | $0.00055 | $0.00794 |
| Opus 5 | $0.00028 | $0.00397 |
| Sonnet 5 | $0.00011 | $0.00159 |
| Haiku 4.5 | $0.00006 | $0.00079 |
Grade A, and why
plan 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 today.
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 — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memory Protocol
START: Read .ai-memory.md from project root. Check existing plans, milestones, project structure, tech stack, ongoing work, and past decisions.
END: Update .ai-memory.md using Memory Compaction Rules with: plan summary, phases/tasks, key risks, and first recommended action.
Goal
Turn a request into an actionable plan with clear phases and tasks.
Agent Routing
- For architecture planning → read
.kiro/skills/agents/agents/project-planner.mdand apply its knowledge - For multi-domain coordination → read
.kiro/skills/agents/agents/orchestrator.mdand apply its knowledge - For technical feasibility → read the relevant specialist agent file:
- Backend:
.kiro/skills/agents/agents/backend-specialist.md - Frontend:
.kiro/skills/agents/agents/frontend-specialist.md - Database:
.kiro/skills/agents/agents/database-architect.md
- Backend:
Socratic Gate
Before planning, verify:
- What is the desired outcome? (specific deliverable)
- What constraints exist? (time, resources, tech stack)
- What is already built vs needs to be built? If any answer is unclear, ASK before proceeding.
Workflow
- Read Memory — Load
.ai-memory.mdfor project context and history. - Define the objective and acceptance criteria.
- Break the work into phases and tasks.
- Note file areas, systems, or modules likely to change.
- Estimate complexity and highlight risky items.
- End with a recommended first task.
- Update Memory — Save plan details to
.ai-memory.md.
Output format
- Objective
- Acceptance criteria
- Phases (with tasks under each)
- File areas / modules affected
- Risks (with mitigation)
- Recommended first action
Checklist
-
Objective clearly stated
-
Acceptance criteria defined
-
Phases broken down
-
Tasks are specific and actionable
-
Complexity estimated per task
-
Risks identified with mitigation
-
First action recommended
-
Memory file updated
-
Clean code chuẩn (Standard clean code applied)
-
Cập nhật đầy đủ tất cả các file liên quan (All related files fully updated)
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.
- today Changed · +3 lines 060611206046
- 5d ago First seen · 74 lines · 55 tokens per session scan A 022a0ab2a2a4
plan is a cursor rule published in the GitHub repository VoTruongDanh/Skills-Agent (2 stars, last pushed yesterday), licensed MIT. It adds 55 tokens to every session and 794 once invoked, about $0.0003 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
ponytail
Ponytail, lazy senior dev mode. Always pick the simplest solution that works.
angular-20
This rule provides comprehensive best practices and coding standards for Angular development, focusing on modern TypeScript, standalone components, signals, and performance optimizations.
dev-standard
Apache Superset development standards and guidelines for Cursor IDE.
cli-error-handling
CLI command error handling patterns.
prefer-direct-imports-over-module-mocks
Prefer extracting a testable core over vi.mock / vi.resetModules when unit tests need to reach production logic entangled with config, env, or singletons.
control-plane-descriptors
Control plane descriptor and instance implementation patterns.