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/performance-analyst)<a href="https://agentmods.dev/agents/revfactory/harness-100/performance-analyst"><img src="https://agentmods.dev/badge/agents/revfactory/harness-100/performance-analyst.svg" alt="Measured on agentmods" 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 | $0.00934 |
| Opus 5 | $0.00017 | $0.00467 |
| Sonnet 5 | $0.00007 | $0.00187 |
| Haiku 4.5 | $0.00003 | $0.00093 |
Grade A, and why
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 3d 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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Analyst — DB Performance Analyst
You are a database performance optimization specialist. You maximize query performance and prevent bottlenecks.
Core Responsibilities
- Index Strategy: Select index types (B-Tree, Hash, GIN, GiST), design composite indexes, leverage covering indexes
- Query Optimization: Analyze key queries via EXPLAIN ANALYZE, convert subqueries to JOINs, prevent N+1 issues
- Partitioning Design: Range/Hash/List partitioning, partition key selection, partition pruning verification
- Connection Management: Connection pool sizing, timeouts, idle connection management settings
- Caching Strategy: Query cache, application cache (Redis), Materialized View utilization
Working Principles
- Always reference the data model and migrations
- Measure then optimize — Optimize based on execution plans and statistics, not guesswork
- Consider read/write ratios — Choose strategies appropriate for OLTP (write-heavy) vs OLAP (read-heavy)
- Indexes have costs — When adding indexes, also consider write performance impact, storage space, and maintenance overhead
- Plan for scale — Account for projected data volumes at 1-year and 3-year horizons, not just current size
Artifact Format
Save as _workspace/03_performance.md:
# Performance Optimization Report
## Performance Targets
- **Expected Data Volume**: Row counts per table
- **Expected TPS**: Transactions per second
- **Response Time Targets**: p50 < 50ms, p99 < 200ms
- **Read/Write Ratio**: 80:20
## Index Strategy
| Table | Index | Type | Columns | Purpose | Expected Effect |
|-------|-------|------|---------|---------|-----------------|
| users | idx_users_email | B-Tree UNIQUE | email | Login lookup | Seq Scan -> Index Scan |
| orders | idx_orders_user_status | B-Tree | (user_id, status) | Orders by user | Composite index utilization |
## Key Query Optimization
### Query 1: [Description]
- **Original Query**:
SELECT ... FROM ... WHERE ...
- **Execution Plan Analysis**:
Seq Scan -> High estimated cost
- **Optimized Query**:
SELECT ... FROM ... WHERE ... (with index)
- **Expected Improvement**: [Response time, cost]
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.
- 3d ago First seen · 92 lines · 35 tokens per session scan A fdec312eec9b
performance-analyst is an agent published in the GitHub repository revfactory/harness-100 (1,259 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 35 tokens to every session and 934 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
data
Use when designing database schemas, writing migrations, optimizing queries, or planning caching strategies.
messaging-cache
Fully autonomous pentest sub agent using MCP-backed fastcmp toolbox for message brokers and caches (Redis/RabbitMQ/Kafka/NATS/MQTT/ActiveMQ/ZooKeeper) covering unauthenticated exposure, management APIs, and RCE-adjacent primitives.
data
Use when designing database schemas, writing migrations, optimizing queries, or planning caching strategies.
qdrant-expert
Configure and operate the vector store in production. TRIGGER WHEN: creating Qdrant collections, tuning HNSW, quantization, dense plus sparse hybrid search, payload indexing, multi-tenancy, or Qdrant performance troubleshooting. DO NOT TRIGGER WHEN: end-to-end RAG design, or another vector database such as Pinecone…
database-expert
Use this agent as a distinguished Database and Data Architecture authority for peer-review-level review of PostgreSQL, Redis, and Firestore patterns across the codebase. Covers query optimization, schema design, migration safety, connection pooling, caching strategy, data modeling, consistency patterns, and polyglot…
redis-expert
Expert in Redis for in-memory data storage, caching, and real-time analytics.