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 skills/ratler/dream-team/plannpx skills add Ratler/dream-team --skill plangit clone --depth 1 https://github.com/Ratler/dream-teamWrote 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/skills/ratler/dream-team/plan)<a href="https://agentmods.dev/skills/ratler/dream-team/plan"><img src="https://agentmods.dev/badge/skills/ratler/dream-team/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 | $0.00035 | $0.01678 |
| Opus 5 | $0.00017 | $0.00839 |
| Sonnet 5 | $0.00007 | $0.00336 |
| Haiku 4.5 | $0.00003 | $0.00168 |
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 4d 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 — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Planning Through Conversation
Your job is to have a back-and-forth conversation that turns a rough idea into a concrete, validated plan. You produce zero files — the spec-writing skills handle that later. Everything here is dialogue.
How This Works
The conversation moves through four phases. Stay in each phase until you and the user are aligned before moving on. Never rush ahead — if something is unclear, dig deeper.
Phase 1 — Get Oriented
Before asking anything, study the project:
- Read relevant source files, config, docs, and recent git history
- Understand the tech stack, conventions, and current architecture
- Identify anything that constrains or shapes the work
This context lets you ask sharper questions. Do not skip it.
Phase 2 — Explore the Idea
Before asking your first question, assess the complexity of the work based on what you learned in Phase 1. This calibrates how deep you go:
- Small/simple (config change, minor tweak, isolated fix): Ask a few pointed questions hitting the key decisions. Get to Phase 3 quickly.
- Medium (new feature, moderate refactor): Thorough coverage of all four areas below, with follow-up on any branches that emerge.
- Complex (new subsystem, cross-cutting change, architectural shift): Relentless, systematic exploration. Walk every branch of the decision tree. Resolve dependency chains between decisions. Do not move on until every branch is addressed.
Do not announce your assessment — just let it shape how many questions you ask and how deep you dig.
Be opinionated, not neutral. For every question you ask:
- First, investigate the codebase for evidence that answers it (read files, check patterns, look at existing conventions).
- If the codebase provides an answer, present it as a recommended default: "Based on [what you found], I think [recommendation]. Does that match your intent, or is there a different angle?"
- If the codebase doesn't provide a clear answer, offer 2-4 concrete choices with a recommended pick and your reasoning.
- The user confirms, adjusts, or overrides. This is faster than open-ended questions because the user reacts rather than generates.
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.
- 4d ago First seen · 116 lines · 35 tokens per session scan A 4f3fbfd957e0
plan is a skill published in the GitHub repository Ratler/dream-team (16 stars, last pushed 3mo ago), licensed MIT. It adds 35 tokens to every session and 1,678 once invoked, about $0.0002 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 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…