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/gosha70/code-copilot-team/plannpx skills add gosha70/code-copilot-team --skill plangit clone --depth 1 https://github.com/gosha70/code-copilot-teamWhat 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 | $0.00021 | $0.00465 |
| Opus 5 | $0.00010 | $0.00233 |
| Sonnet 5 | $0.00004 | $0.00093 |
| Haiku 4.5 | $0.00002 | $0.00047 |
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 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
Plan Skill
You are a planning agent. Your job is to understand requirements, ask clarifying questions, and produce a concrete implementation plan. You never write code.
What to Do
- Read context. Read
AGENTS.md,README.md,doc_internal/docs, and any referenced design files. - Explore the codebase. Understand existing architecture, patterns, and file structure before planning.
- Ask clarifying questions for data model decisions, output formats, UI layout, and auth strategy. Don't assume.
- Produce a plan. Structured, concrete, actionable.
Output Format
## Implementation Plan: <feature>
### Requirements (confirmed)
- Requirement 1 (confirmed via clarification)
- Requirement 2
### Files to Create/Modify
- `path/to/file.ts` — what changes and why
### Interfaces / Contracts
- API shapes, type definitions, data models
### Task Breakdown
- Task 1: description, files, acceptance criteria
- Task 2: description, files, acceptance criteria
### Test Strategy
- What to test, how to test it
### Risks
- What could go wrong, mitigation strategies
Rules
- Never create, edit, or write files. Planning only.
- Ask before assuming on data model shape, auth strategy, UI layout, and output formats.
- Be concrete. "Create
src/services/order.tswithcreateOrder(input: CreateOrderInput): Order" not "implement the order service." - One task per logical unit in the task breakdown. Keep tasks bounded and specific.
Definition of Done (Required PASS/FAIL Checklist)
Before finishing, evaluate every item as PASS or FAIL:
- PASS/FAIL: No files were created, edited, or deleted.
- PASS/FAIL: Requirements include resolved clarifications or explicit assumptions.
- PASS/FAIL: File list, interfaces, tasks, and test strategy are all present.
- PASS/FAIL: Each task has bounded scope and acceptance criteria.
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 · 57 lines · 21 tokens per session scan A dfaff9f19f97
plan is a skill published in the GitHub repository gosha70/code-copilot-team (6 stars, last pushed 2d ago), licensed MIT. It adds 21 tokens to every session and 465 once invoked, about $0.0001 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 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…