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/tomzx/agents/audit-performance-efficiencynpx skills add tomzx/agents --skill audit-performance-efficiencygit clone --depth 1 https://github.com/tomzx/agentsWhat 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 | $0.00115 | $0.02010 |
| Opus 5 | $0.00057 | $0.01005 |
| Sonnet 5 | $0.00023 | $0.00402 |
| Haiku 4.5 | $0.00012 | $0.00201 |
Grade A, and why
audit-performance-efficiency scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
rg -n "requests\.(get|post)|urllib\.request|open\(|subprocess\.(run|call|Popen)" -g '*handler*.py' . How it starts
The opening of the file, as written. The whole thing — 183 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TODAY=!date +%Y-%m-%d
Performance Efficiency Audit (ISO/IEC 25010)
Audits the codebase for performance efficiency: response time, resource utilization, and capacity limits. It finds statically detectable performance antipatterns before they show up under load.
This is the Performance efficiency characteristic of the ISO/IEC 25010 quality model. Distinct from observe-production (runtime latency/error measurement), this is static analysis of code that will be slow or wasteful.
Prerequisites
- Working directory is the root of the repository
- Read
.sdlc/context/architecture.mdif present (to identify hot paths and data stores) - Language-specific tooling optional (see Useful Commands Reference)
What This Checks
| Sub-characteristic | What it means | Signals scanned |
|---|---|---|
| Time behavior | response/processing times under load | queries in loops (N+1), O(n²) algorithms, blocking I/O in request handlers, time.sleep in hot paths, missing pagination on list endpoints |
| Resource utilization | CPU, memory, file handles, connections | unbounded collections/caches, large allocations in loops, unclosed resources, missing connection pooling, full-table loads (SELECT *, .all(), fetchall) |
| Capacity | limits beyond which performance degrades | hardcoded single-thread assumptions, missing rate limiting, unbounded queues, no backpressure, missing indexes on queried columns |
Steps
1. Identify hot paths and data stores
From architecture.md and route/endpoint discovery, identify request handlers, batch jobs, and data-access layers. These are where performance findings are most severe.
rg -n "@(app|router|api|blueprint)\.(get|post|put|delete|patch|route)" -g '*.py' .
rg -n "(get|post|put|delete|patch|use|all)\(['\"]" -g '*.{ts,js}' .
2. Query and data-access antipatterns (time behavior)
N+1 / queries-in-loops:
rg -n -B3 "for .+ in .+:" -g '*.py' . | rg "\.(get|filter|find|first|all|execute|query|load)\("
Full-table loads without limits:
rg -n "\.all\(\)|\.fetchall\(\)|SELECT \*|objects\.all|\.findAll\(" -g '*.{py,ts,js}' .
Unbatched writes inside loops:
rg -n "(\.save\(|\.insert\(|\.create\(|\.add\(|\.commit\(|db\.)" -g '*.py' .
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 · 183 lines · 115 tokens per session scan A 8a099b077093
audit-performance-efficiency is a skill published in the GitHub repository tomzx/agents (5 stars, last pushed 5d ago), licensed MIT. It adds 115 tokens to every session and 2,010 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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