OpenClaw Master Skills is a curated, regularly updated collection of skills that extends an AI personal assistant platform with capabilities such as research, browser automation, presentation creation, and prompt work. It is intended for people using OpenClaw or MyClaw.ai to give their agents additional tasks and workflows. The catalogue contains many skills and agents from this collection.
Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skillsnpx agentmods add skills/leoyeai/openclaw-master-skills/afrexai-performance-engineeringWrote 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/leoyeai/openclaw-master-skills/afrexai-performance-engineering)<a href="https://agentmods.dev/skills/leoyeai/openclaw-master-skills/afrexai-performance-engineering"><img src="https://agentmods.dev/badge/skills/leoyeai/openclaw-master-skills/afrexai-performance-engineering/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/leoyeai/openclaw-master-skills/afrexai-performance-engineering"><img src="https://agentmods.dev/badge/skills/leoyeai/openclaw-master-skills/afrexai-performance-engineering.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 7 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
- medium MCP Rug Pull · line 109 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
- medium MCP Rug Pull · line 110 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
- medium MCP Rug Pull · line 111 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
- medium Data Exfiltration · line 523 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium MCP Rug Pull · line 599 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
- medium MCP Rug Pull · line 600 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
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 | $0.00071 | $0.07688 |
| Opus 5.5 | $0.00028 | $0.03075 |
| Sonnet 5.5 | $0.00014 | $0.01538 |
| Haiku 4.5 | $0.00007 | $0.00769 |
Grade A, and why
afrexai-performance-engineering 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 1mo 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 — 935 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Engineering System
From "it's slow" to "here's why and here's the fix" — a complete methodology for measuring, diagnosing, optimizing, and preventing performance problems.
Phase 1: Performance Investigation Brief
Before touching anything, define the problem.
# performance-brief.yaml
investigation:
reported_by: ""
reported_date: ""
system: "" # service/app name
environment: "" # production, staging, dev
problem_statement:
symptom: "" # "API response time increased 3x"
impact: "" # "15% of users seeing timeouts"
since_when: "" # "After deploy v2.14 on Feb 20"
affected_scope: "" # "All endpoints" | "Only /search" | "Users in EU"
baselines:
target_p50: "" # e.g., "200ms"
target_p95: "" # e.g., "500ms"
target_p99: "" # e.g., "1000ms"
current_p50: ""
current_p95: ""
current_p99: ""
throughput_target: "" # e.g., "1000 rps"
error_rate_target: "" # e.g., "<0.1%"
constraints:
budget: "" # time/money for optimization
risk_tolerance: "" # "Can we change the schema?" "Can we add caching?"
deadline: "" # "Must fix before Black Friday"
hypothesis:
primary: "" # "N+1 queries in the new recommendation engine"
secondary: "" # "Connection pool exhaustion under load"
evidence: "" # "Slow query log shows 200+ queries per request"
Performance Budget Framework
Set budgets BEFORE building, not after complaints:
| Metric | Web App | API | Mobile | Batch Job |
|---|---|---|---|---|
| P50 response | <200ms | <100ms | <300ms | N/A |
| P95 response | <500ms | <250ms | <800ms | N/A |
| P99 response | <1s | <500ms | <1.5s | N/A |
| Error rate | <0.1% | <0.01% | <0.5% | <0.001% |
| Time to Interactive | <3s | N/A | <2s | N/A |
| Memory per request | <50MB | <20MB | <100MB | <1GB |
| CPU per request | <100ms | <50ms | <200ms | N/A |
| Throughput | 100+ rps | 500+ rps | N/A | items/min |
What ships with it
2 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.
- 1mo ago First seen · 935 lines · 71 tokens per session scan A 3d3f9fcfcce4
afrexai-performance-engineering is a skill published in the GitHub repository LeoYeAI/openclaw-master-skills (2,166 stars, last pushed 3mo ago), licensed MIT. It adds 71 tokens to every session and 7,688 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-09-03.
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