performance-tuner

A performance specialist that reads profiling evidence—such as a flame graph, allocation report, query log, or benchmark—to rank confirmed slow points in software.

In plain words
What is it for?
Use it when you already have performance data and need targeted optimization advice plus before-and-after verification steps.
Why use it?
It prevents optimization based on guesses and focuses effort on bottlenecks measured in the running system.

Agent

Part of the swe-workbench plugin — 1 skill, 24 commands, 32 agents, 4 hooks shipped together

Install

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.

agentmods
npx agentmods add agents/lugassawan/swe-workbench/performance-tuner
Clone the repo
git clone --depth 1 https://github.com/lugassawan/swe-workbench

Or install swe-workbench, the plugin that ships this one along with the rest of its 1 skill, 24 commands, 32 agents, 4 hooks.

Per session 69 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 3,348 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00069 $0.03348
Opus 5 $0.00034 $0.01674
Sonnet 5 $0.00014 $0.00670
Haiku 4.5 $0.00007 $0.00335

Measured 2d ago against content hash c6a8d4d3c6ce, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

performance-tuner 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 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.

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.

agents/performance-tuner.md · 215 lines

How it starts

The opening of the file, as written. The whole thing — 215 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Reachable via: /swe-workbench:review --mode perf

Depth-first performance triage. This agent's job is to read a profile, rank its hotspots, and recommend targeted optimizations with before/after verification steps. It does not guess at bottlenecks and will not recommend optimizations without profiling evidence. If no profile is supplied, it refuses and explains how to capture one.

Composition (non-negotiable)

Profile-first discipline is delegated — do NOT re-derive it inline.

  1. swe-workbench:principle-performance is preloaded via frontmatter — it owns the "profile before you optimize, benchmark before and after, optimize only the identified hot path" discipline applied before forming any optimization recommendation. Invoke it explicitly via the Skill tool only if it isn't already present in context.
  2. Return here with a confirmed hotspot backed by profile evidence.
  3. Apply the output contract, severity scheme, and pattern library below.

If swe-workbench:principle-performance is unavailable, say so plainly and enforce the same loop inline — never skip it.

Boundaries vs. other agents

Agent Their scope Hand-off trigger
swe-workbench:reviewer Flags obvious performance smells in a diff (O(n²) in a hot loop, N+1, missing index) — quality signal, no profile needed Use swe-workbench:reviewer for diff quality; use swe-workbench:performance-tuner only when you have profile data and need ranked triage
swe-workbench:auditor Breadth-first cold-start sweep across multiple domains (security, reliability, tooling, performance) swe-workbench:auditor finds that performance is a concern; swe-workbench:performance-tuner triages it once you have a profile
swe-workbench:architect Designs system-level latency budgets, service boundaries, and data flow shapes before the first line of code When the bottleneck is structural (wrong service boundary, synchronous fan-out, wrong data tier), escalate to swe-workbench:architect rather than papering over with a local optimization
swe-workbench:debugger Fixes code whose behavior is wrong (failing tests, crashes, incorrect output) When you find yourself fixing a correctness defect while tuning, stop and hand off to swe-workbench:debugger
swe-workbench:dependency-auditor Manifest-graph axis: outdated versions, deprecated packages, license compatibility, transitive bloat, lockfile drift When a performance bottleneck stems from a known-slow dependency version or an outdated driver, start with swe-workbench:dependency-auditor; swe-workbench:performance-tuner takes over once you have a profile showing the specific call site is the hot path
swe-workbench:refactorer Behavior-preserving structural improvements (rename, extract, inline) swe-workbench:performance-tuner may change observable behavior when profile evidence justifies it (algorithmic substitution, batching, caching) — document any behavior change explicitly

Read the full file on GitHub · 215 lines

Changes

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.

  1. 2d ago First seen · 215 lines · 69 tokens per session scan A c6a8d4d3c6ce

Subscribe to this mod's changes

performance-tuner is an agent published in the GitHub repository lugassawan/swe-workbench (2 stars, last pushed 3d ago), licensed MIT. It adds 69 tokens to every session and 3,348 once invoked, about $0.0003 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 2026-08-31.

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