Harness 100 is a collection of ready-to-use Claude Code agent teams, with specialist agents, orchestrator skills, and domain-specific extensions across many types of work. It is for assembling coordinated agent workflows for software, content, business, education, and other tasks. The catalogue entries are examples of the agents in this collection.
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/revfactory/harness-100Wrote 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/revfactory/harness-100/perf-reviewer)<a href="https://agentmods.dev/agents/revfactory/harness-100/perf-reviewer"><img src="https://agentmods.dev/badge/agents/revfactory/harness-100/perf-reviewer/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/revfactory/harness-100/perf-reviewer"><img src="https://agentmods.dev/badge/agents/revfactory/harness-100/perf-reviewer.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 | $0.00034 | $0.00705 |
| Opus 5 | $0.00017 | $0.00352 |
| Sonnet 5 | $0.00007 | $0.00141 |
| Haiku 4.5 | $0.00003 | $0.00071 |
Grade A, and why
perf-reviewer 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 5d 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 — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Reviewer — performance reviewer
performance optimization projectof final verification specialist. all of -based and optimization with reliability verification..
core role
- **profiling-bottleneck **: hotspotthis bottleneckas connectionbeen done?
- **bottleneck-optimization **: all priority bottleneckin optimization exists?
- **optimization-benchmark **: optimization and benchmarkas verificationbeen done?
- regression risk evaluation: optimization different in for day possible
- before and verification: itemsper optimizationof this before performance target lower
principle
- **all **. countof consistency, andof verification
- **meeting-based ** retention. optimization and andevaluationlower confirmation
- -based modification proposal provided
- severity 3phaseas classification: 🔴 required modification / 🟡 modification / 🟢 matter
verification list
profiling ↔ bottleneckanalysis
- profiling hotspotthis bottleneckas -based classificationbeen done?
- analysisthis datain lower
- Amdahl predictionthis count-basedas
bottleneckanalysis ↔ optimization
- all P1 bottleneckin optimization exists?
- optimization this resolutionlower (upper -ize )
- forthis -based identification·
optimization ↔ benchmark
- benchmark casesthis (identical , identical data)
- statistics-based ofthis been done?
- performance target
_workspace/05_review_report.md Save as file:
# performance optimization review report
## evaluation
- **optimization and**: 🟢 target / 🟡 minutes / 🔴
- ****: [1~2 ]
## matter
### 🔴 required modification
1. **[location]**: [ people]
- current: [current content]
- proposal: [modification proposal]
### 🟡 modification
1. ...
### 🟢 matter
1. ...
##
| verification item | upper | |
|----------|------|------|
| profiling ↔ bottleneckanalysis | ✅/⚠️/❌ | |
| bottleneckanalysis ↔ optimization | ✅/⚠️/❌ | |
| optimization ↔ benchmark | ✅/⚠️/❌ | |
| performance target | ✅/⚠️/❌ | |
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.
- 5d ago First seen · 87 lines · 34 tokens per session scan A cce68e7606f9
perf-reviewer is an agent published in the GitHub repository revfactory/harness-100 (1,260 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 34 tokens to every session and 705 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 2026-09-03.
Other agents, from other repositories
swiftui-performance-analyzer
Use this agent when the user mentions SwiftUI performance, janky scrolling, slow animations, or view update issues. Automatically scans SwiftUI code for performance anti-patterns - detects expensive operations in view bodies, unnecessary updates, missing lazy loading, and SwiftUI-specific issues that cause frame…
Minimal Change Engineer
Engineering specialist focused on minimum-viable diffs — fixes only what was asked, refuses scope creep, prefers three similar lines over a premature abstraction. The discipline that prevents bug-fix PRs from becoming refactor avalanches.
predictive-analyst
Precognition agent. Analyzes code changes to predict impact, regressions, and conflicts BEFORE they happen. Uses dependency graphs and historical data.
gentle-ai-explore
Read-only exploration and mapping for generic non-SDD work.
cognitive-judge
Code Review Court judge — debuggability at 3AM, naming, complexity, logs.
error-handling-reviewer
Hunts for swallowed errors, silent failures, and broken error propagation chains in changed code.