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 skills add event4u-app/agent-config --skill performance-analysisgit clone --depth 1 https://github.com/event4u-app/agent-configWrote 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/event4u-app/agent-config/performance-analysis)<a href="https://agentmods.dev/skills/event4u-app/agent-config/performance-analysis"><img src="https://agentmods.dev/badge/skills/event4u-app/agent-config/performance-analysis/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/event4u-app/agent-config/performance-analysis"><img src="https://agentmods.dev/badge/skills/event4u-app/agent-config/performance-analysis.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00031 | $0.01148 |
| Opus 5 | $0.00015 | $0.00574 |
| Sonnet 5 | $0.00006 | $0.00230 |
| Haiku 4.5 | $0.00003 | $0.00115 |
Grade A, and why
performance-analysis 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 8d 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 — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
performance-analysis
Mission
Find performance bottlenecks before they affect users. This skill is proactive — it analyzes code for performance issues, not just responds to "it's slow" reports.
For writing performant code patterns (caching, eager loading, Redis), use the performance skill.
For test suite performance, use test-performance.
When to use
Use this skill when:
- Auditing a codebase or flow for performance bottlenecks
analysis-autonomous-moderoutes here after detecting slow patterns- Reviewing code that handles large datasets, loops, or external calls
- Investigating why a specific endpoint or job is slow
Do NOT use when:
- Writing new caching/optimization code → use
performance - Optimizing test suite speed → use
test-performance - Hunting for functional bugs → use
bug-analyzer(proactive mode)
Procedure: Performance analysis
1. Identify hotspots
Focus on code paths with high execution frequency or large data volumes:
- API endpoints called frequently (list endpoints, dashboards)
- Queue jobs processing batches
- Scheduled commands running on large datasets
- Import/export operations
- Report generation
2. Database query analysis
| Pattern | What to look for |
|---|---|
| N+1 queries | ->load() or relationship access in loops, missing ->with() |
| Missing indexes | WHERE clauses on unindexed columns, slow ORDER BY |
| Full table scans | SELECT * without WHERE, LIKE '%term%' |
| Unnecessary queries | Same query executed multiple times in one request |
| Large result sets | Loading thousands of models when only counts or IDs are needed |
| Missing pagination | ->get() on unbounded queries |
| Suboptimal joins | Multiple queries that should be a single JOIN |
| Transaction scope | Transactions holding locks longer than necessary |
3. Application-level bottlenecks
| Pattern | What to look for |
|---|---|
| Synchronous I/O | HTTP calls, file operations, or API calls in the request cycle |
| Memory bloat | Loading entire collections when chunking would work |
| Redundant computation | Same calculation repeated without caching |
| Missing cache | Data that rarely changes but is queried on every request |
| Stale cache | Cache that is never invalidated or has wrong TTL |
| Serialization overhead | Large models serialized to JSON unnecessarily |
| Loop inefficiency | O(n²) patterns with nested loops or repeated array searches |
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.
- 8d ago First seen · 130 lines · 31 tokens per session scan A f40a4d028454
performance-analysis is a skill published in the GitHub repository event4u-app/agent-config (10 stars, last pushed today), licensed MIT. It adds 31 tokens to every session and 1,148 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 2026-09-04.
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