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/nickpolyder/np.copilot.config/full-code-reviewnpx skills add NickPolyder/NP.CoPilot.Config --skill full-code-reviewgit clone --depth 1 https://github.com/NickPolyder/NP.CoPilot.ConfigWhat 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 | $0.00042 | $0.01352 |
| Opus 5 | $0.00021 | $0.00676 |
| Sonnet 5 | $0.00008 | $0.00270 |
| Haiku 4.5 | $0.00004 | $0.00135 |
Grade A, and why
full-code-review 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.
How it starts
The opening of the file, as written. The whole thing — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Intent (anchor): Perform an exhaustive, multi-reviewer analysis only when the user explicitly requests it. Always: define the review scope; use three distinct core review hats; include all severity levels; produce a detailed report. Never: run automatically during normal pre-commit work; edit the reviewed worktree; or create a commit.
Shared policy: Follow
instructions/coordination.instructions.mdfor precedence, invocation, delegation, and handoffs. Applyinstructions/workflow.instructions.mdfor proportional work and verification.
This is an analysis workflow, not a commit workflow.
After it completes, the user may explicitly invoke git-commit-review to create an atomic commit.
When to use this skill
Use this skill only when the user explicitly asks for exhaustive review, or for:
- Release candidates.
- Major architectural changes.
- Security audits.
- Schema redesigns.
- Large, high-risk pull requests or branches.
Do not use it automatically after git-commit-review escalation.
Do not use it as a replacement for splitting large changes into logical commits.
1. Define the review target
Confirm the exact target: a staged candidate, a branch range, a pull request diff, or named files. For a large mixed diff, identify logical commit boundaries first and tell the user which scope each review covers.
Run directly applicable build, type, import, and test checks before reviewers so failures are reported as facts rather than speculation. Record validation failures in the report and do not disguise them as reviewer findings.
2. Select reviewers
Always run these three independent, read-only core hats:
| Hat | Focus |
|---|---|
| Architect | Structure, boundaries, dependencies, cross-cutting concerns, and shared-contract impact. |
| Principal Developer | Correctness, security, edge cases, error handling, performance, and maintainability. |
| Senior Developer | Functional completeness, repository conventions, tests, documentation, and long-term readability. |
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 · 136 lines · 42 tokens per session scan A 5525db07d4cc
full-code-review is a skill published in the GitHub repository NickPolyder/NP.CoPilot.Config (2 stars, last pushed 8d ago), licensed MIT. It adds 42 tokens to every session and 1,352 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 2026-08-31.
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…