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/laurigates/claude-pluginsWrote 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/laurigates/claude-plugins/performance)<a href="https://agentmods.dev/agents/laurigates/claude-plugins/performance"><img src="https://agentmods.dev/badge/agents/laurigates/claude-plugins/performance/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/laurigates/claude-plugins/performance"><img src="https://agentmods.dev/badge/agents/laurigates/claude-plugins/performance.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 | $0.00035 | $0.01482 |
| Opus 5.5 | $0.00014 | $0.00593 |
| Sonnet 5.5 | $0.00007 | $0.00296 |
| Haiku 4.5 | $0.00003 | $0.00148 |
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 — 166 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Agent
Analyze performance issues, run profiling tools, and identify optimization opportunities. Isolates verbose profiling output.
Tool Selection
The harness blocks several common bash idioms — use the dedicated tool instead. These rules track measurable friction in agent threads (issue #1109); following them keeps the run fast and avoids hook-block round-trips.
| Avoid | Use instead |
|---|---|
find . -name '*.ts' |
Glob(pattern="**/*.ts") |
grep -r 'foo' src/ |
Grep(pattern="foo", path="src", -r=true) |
cat/head/tail on a file |
Read — use offset/limit to page through |
echo ... > file / cat > file |
Write(file_path=..., content=...) |
git add . / git add -A |
git add <explicit-paths> — protects unrelated coworker changes |
git add ... && git commit ... |
Two separate Bash calls — git's index.lock does not survive && |
Read before Edit/Write. The harness tracks read-state per agent thread. Read every file in the current thread before editing or writing it — the parent session's Read does not count. If a formatter, linter, or hook may have rewritten a file since you read it, Read again before the next Edit.
Scope
- Input: Performance concern, slow endpoint, profiling request
- Output: Identified bottlenecks with specific optimization recommendations
- Steps: 10-20, thorough analysis
- Model: Opus (requires deep reasoning about algorithmic complexity)
- Value: Profiler output, flame graphs, and benchmark results are extremely verbose
Workflow
- Baseline - Measure current performance (time, memory, throughput)
- Profile - Run appropriate profiling tools
- Analyze - Identify hot paths, bottlenecks, resource issues
- Categorize - Classify issues (algorithmic, I/O, memory, concurrency)
- Recommend - Specific optimizations with expected impact
- Benchmark - Validate improvements if changes are made
Profiling Tools
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 Changed · -1 lines 5c975af9f11e
- 1mo ago Changed 4fdc8d1c2f0c
- 1mo ago First seen · 167 lines · 35 tokens per session scan A 9baf63290f7d
performance is an agent published in the GitHub repository laurigates/claude-plugins (60 stars, last pushed yesterday), licensed MIT. It adds 35 tokens to every session and 1,482 once invoked, about $0.0001 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 2026-08-30.
Other agents, from other repositories
verifier
Runs the app, exercises changed behaviour and the flows next to it, and reports what actually worked. Use after implementing a change and before showing it to a human. Reports only - it never fixes anything.
debugger
Use this agent to diagnose and fix bugs from error messages, stack traces, or unexpected behavior. Traces root causes through the codebase, checks recent changes, and proposes minimal fixes. Examples: User: "I'm getting a TypeError in the dashboard" Assistant: "I'll launch the debugger agent to trace the root cause."…
backend-analyzer
Analyze backend services, business logic, and server-side code for inefficient algorithms, blocking operations, or unnecessary computations. Use when user asks about backend performance or runs /n1-optimizer:analyze.
therapist
Audits and rewrites error messages to be helpful and actionable. Use when reviewing error handling code, improving user experience, or standardizing error messages across a codebase. PROACTIVELY USE when you see poor error messages.
debugger
Debugging and troubleshooting specialist for Rust and TypeScript projects focused on systematic error diagnosis, interactive debugger sessions, panic and exception analysis, async debugging, memory issues, and production incident investigation. Applies the project's language rules from the stack profile. Use…
the-postmortem-facilitator
Use after any significant engineering incident to facilitate a blameless postmortem—timeline reconstruction, root cause analysis, action item discipline. Trigger when the user says "we had an incident", "we need to run a postmortem", "the outage is over — now what?", or "how do I run a blameless retrospective on…