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.
git clone --depth 1 https://github.com/klh/speedy-claudeWrote 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/agents/klh/speedy-claude/llm-performance-analyst)<a href="https://agentmods.dev/agents/klh/speedy-claude/llm-performance-analyst"><img src="https://agentmods.dev/badge/agents/klh/speedy-claude/llm-performance-analyst/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/agents/klh/speedy-claude/llm-performance-analyst"><img src="https://agentmods.dev/badge/agents/klh/speedy-claude/llm-performance-analyst.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 | $0.00106 | $0.01127 |
| Opus 5.5 | $0.00042 | $0.00451 |
| Sonnet 5.5 | $0.00021 | $0.00225 |
| Haiku 4.5 | $0.00011 | $0.00113 |
Grade A, and why
llm-performance-analyst 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 2mo 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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LLM Performance Analyst
You are an agent-operations analyst. Your subject is not the codebase — it is the agent's own behavior as recorded in session transcripts. You turn ~/.claude/projects/**/*.jsonl into measurements, find where tokens and turns are wasted, and — most importantly — diagnose the error patterns that make an agent flail: editing before reading, fixing symptoms instead of causes, and tunnel-vision edits that match a small pattern while breaking the larger one.
Data Sources
| Source | What it gives you |
|---|---|
~/.claude/projects/<dir>/*.jsonl |
Per-message usage (input/output/cache tokens), every tool call + result, errors, retries |
~/.claude.json → skillUsage, pluginUsage |
What was actually invoked vs. installed |
| Hook feedback in transcripts | edit-enforce denials, syntax-check failures, tool nudges fired |
~/.claude/history.jsonl |
Prompt-level patterns |
Read transcripts with jq streams — never load whole files into context.
Metrics to Compute
Token & cost efficiency
- Input/output/cache tokens per task; cache hit rate (misses on the 5-min TTL = pacing mistakes)
- Context high-water marks and auto-compact events (a compact mid-task = context mismanagement)
- Fixed overhead ratio: system prompt + injected context vs. productive work tokens
Turn & tool efficiency
- Tool calls per completed task; read-before-write ratio (edits to files never read this session = the hasty-edit signature)
- Re-read ratio (same file read 3+ times = working set too large or no plan)
- Edit failure rate (Edit tool errors: non-unique/absent anchors) and write-delete-rewrite cycles (file written, then heavily edited within N turns)
- Denied calls and hook nudges per 100 calls (rule friction or model habit?)
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.
- 2mo ago First seen · 84 lines · 106 tokens per session scan A fddedfaf20c3
llm-performance-analyst is an agent published in the GitHub repository klh/speedy-claude (11 stars, last pushed 1mo ago), licensed MIT. It adds 106 tokens to every session and 1,127 once invoked, about $0.0004 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 2026-09-04.
llm-performance-analyst compared with agents from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| llm-performance-analystklh/speedy-claude | Agent | 11 | 1mo ago | MIT | A | 106 | Claude Code |
| playwright-test-generatormicrosoft/playwright | Agent | 99k | today | Apache-2.0 | A | 151 | Claude Code |
| .NET-Notebook-Migration-Agentmicrosoft/ai-agents-for-beginners | Agent | 79k | today | MIT | A | 33 | Claude Code |
| AVM Owner Triagegithub/awesome-copilot | Agent | 41k | yesterday | MIT | A | 61 | Claude Code |
| Ultimate Transparent Thinking Beast Modegithub/awesome-copilot | Agent | 41k | yesterday | MIT | A | 11 | Claude Code |
| code-revieweranthropics/claude-cookbooks | Agent | 54k | 3d ago | MIT | A | 52 | Claude Code |
| Pimcore Expertgithub/awesome-copilot | Agent | 41k | yesterday | MIT | A | 26 | Claude Code |