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/nishilbhave/codeprobe/codeprobe-performancenpx skills add nishilbhave/codeprobe --skill codeprobe-performancegit clone --depth 1 https://github.com/nishilbhave/codeprobeWhat 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 | $0.00086 | $0.02187 |
| Opus 5 | $0.00043 | $0.01094 |
| Sonnet 5 | $0.00017 | $0.00437 |
| Haiku 4.5 | $0.00009 | $0.00219 |
Grade A, and why
codeprobe-performance 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 — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Standalone Mode
If invoked directly (not via the orchestrator), you must first:
- Read
../codeprobe/shared-preamble.md(resolve relative to this SKILL.md's location — the siblingcodeprobeskill directory — not the user's project) for the output contract, execution modes, and constraints. - Load applicable reference files from
../codeprobe/references/(same resolution) based on the project's tech stack. - Default to
fullmode unless the user specifies otherwise.
Performance & Scalability Auditor
Domain Scope
This sub-skill detects performance and scalability issues across these categories:
- N+1 Queries — Lazy-loading relationships inside loops
- Missing Indexes — WHERE/ORDER BY on non-indexed columns
- Unbounded Queries — Model::all() without pagination/limit
- Memory — Loading entire files into memory, array accumulation in loops
- Caching — Repeated identical queries, missing TTL, stale cache after writes
- Algorithmic Efficiency — O(n^2) in hot paths, nested loops, sorting in loops
- Concurrency — Race conditions, non-idempotent queue jobs, shared mutable state
- Frontend Performance — Unnecessary re-renders, bundle size, missing lazy loading
What It Does NOT Flag
- Premature optimization in non-hot-path code — proportional design matters. A utility function called once at startup doesn't need the same optimization as a request handler.
- Development-only debug queries — queries in seeders, dev-only commands, or debug endpoints.
- Batch processing scripts — scripts intentionally designed to process everything (migrations, data backfills) where unbounded queries may be appropriate.
- O(n^2) on small bounded collections (<100 items) — nested loops on small known-size arrays are fine.
- Frontend SSR/build-time code — server components and build scripts have different performance profiles than client-side code.
Detection Instructions
N+1 Queries
| ID Prefix | What to Detect | How to Detect | Severity |
|---|---|---|---|
PERF |
Eloquent relationship access inside loop without eager loading | Search for foreach/for loops iterating over a collection, then accessing a relationship property (e.g., $order->items, $user->profile) inside the loop body. Check whether the query that produced the collection includes with() or load() for that relationship. |
Major |
PERF |
Any ORM lazy-loading inside iteration | Look for patterns where a database query is implicitly triggered inside a loop: Django querysets accessed per-iteration, SQLAlchemy lazy loads, Prisma relation access in .map(). |
Major |
PERF |
Template/view triggering queries | Blade templates, Jinja2 templates, or React components calling relationship properties that trigger queries during rendering. | Major |
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 · 131 lines · 86 tokens per session scan A 535c8507eb36
codeprobe-performance is a skill published in the GitHub repository nishilbhave/codeprobe (5 stars, last pushed 1mo ago), licensed MIT. It adds 86 tokens to every session and 2,187 once invoked, about $0.0004 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.
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…
agent-host-chat-contributions
Build and review cross-cutting agent-host chat behavior through lifecycle contributions. Use when adding turn lifecycle side effects, prompt or context injection, restored-history transformation, protocol-action observation, or when reviewing changes that add code to AgentSideEffects or AgentService.
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.