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/krzysztofsurdy/code-virtuoso/performancenpx skills add krzysztofsurdy/code-virtuoso --skill performancegit clone --depth 1 https://github.com/krzysztofsurdy/code-virtuosoWrote 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/krzysztofsurdy/code-virtuoso/performance)<a href="https://agentmods.dev/skills/krzysztofsurdy/code-virtuoso/performance"><img src="https://agentmods.dev/badge/skills/krzysztofsurdy/code-virtuoso/performance.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 | $0.00084 | $0.02098 |
| Opus 5 | $0.00042 | $0.01049 |
| Sonnet 5 | $0.00017 | $0.00420 |
| Haiku 4.5 | $0.00008 | $0.00210 |
Grade A, and why
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 6d 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 — 197 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Optimization
Performance work follows one rule above all others: measure before you change anything. Intuition about bottlenecks is wrong more often than it is right. Every optimization should start with profiling, produce a hypothesis, apply a targeted fix, and verify with another measurement.
Core Principles
| Principle | Meaning |
|---|---|
| Measure first | Never optimize without profiling data - gut feelings about bottlenecks are unreliable |
| Optimize the critical path | Focus on the code that runs most frequently or blocks user-visible latency |
| Set budgets | Define acceptable latency, throughput, and resource usage before you start |
| Avoid premature optimization | Readable, correct code first - optimize only when measurements show a real problem |
| Know your tradeoffs | Every optimization trades something (memory for speed, complexity for throughput, freshness for latency) |
Profiling and Benchmarking
Profiling identifies where time and resources are spent. Without it, you are guessing.
Types of Profiling
| Type | What It Reveals | When to Use |
|---|---|---|
| CPU profiling | Hot functions, call frequency, execution time distribution | Slow request handling, high CPU usage |
| Memory profiling | Allocation rates, heap size, object retention, leaks | Growing memory usage, OOM errors, GC pressure |
| I/O profiling | Disk reads/writes, network calls, blocking waits | Slow file operations, external service latency |
| Database profiling | Query execution time, query count per request, slow queries | High DB load, N+1 patterns, missing indexes |
The Profiling Workflow
- Baseline - Capture metrics under normal conditions before any changes
- Identify - Find the hotspot consuming the most time or resources
- Hypothesize - Form a specific theory about why it is slow
- Fix - Apply a single, targeted change
- Verify - Measure again to confirm improvement and check for regressions
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 6d ago First seen · 197 lines · 84 tokens per session scan A d2173ab3aec0
performance is a skill published in the GitHub repository krzysztofsurdy/code-virtuoso (20 stars, last pushed 3mo ago), licensed MIT. It adds 84 tokens to every session and 2,098 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 2026-08-30.
Other skills, from other repositories
django-ticket-triage
Analyze a Django Trac ticket and produce a triage recommendation report — duplicate search, related PRs, forum threads, and the affected source code. Use when the user gives a Django ticket number, or asks whether a ticket is valid, a duplicate, or ready for a triage stage.
gh-issues
Fetch GitHub issues, spawn sub-agents to implement fixes and open PRs, then monitor and address PR review comments. Usage: /gh-issues [owner/repo] [--label bug] [--limit 5] [--milestone v1.0] [--assignee @me] [--fork user/repo] [--watch] [--interval 5] [--reviews-only] [--cron] [--dry-run] [--model glm-5]…
clawkeeper
Install, configure, verify, and debug the Clawkeeper watcher stack in this workspace. Use when a user asks to set up clawkeeper, initialize remote or local mode, enable clawkeeper-watcher, wire clawkeeper-bands, diagnose context-judge routing, inspect watcher logs, fix mode/config mismatches, or troubleshoot startup…
openclaw-parallels-smoke
End-to-end Parallels smoke, upgrade, and rerun workflow for OpenClaw across macOS, Windows, and Linux guests. Use when Codex needs to run, rerun, debug, or interpret VM-based install, onboarding, gateway smoke tests, latest-release-to-main upgrade checks, fresh snapshot retests, or optional Discord roundtrip…
node-connect
Diagnose OpenClaw node connection and pairing failures for Android, iOS, and macOS companion apps. Use when QR/setup code/manual connect fails, local Wi-Fi works but VPS/tailnet does not, or errors mention pairing required, unauthorized, bootstrap token invalid or expired, gateway.bind, gateway.remote.url, Tailscale…
openclaw-pr-maintainer
Maintainer workflow for reviewing, triaging, preparing, closing, or landing OpenClaw pull requests and related issues. Use when Codex needs to validate bug-fix claims, search for related issues or PRs, apply or recommend close/reason labels, prepare GitHub comments safely, check review-thread follow-up, or perform…