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.
npx agentmods add agents/revfactory/harness-100/data-managergit 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/data-manager)<a href="https://agentmods.dev/agents/revfactory/harness-100/data-manager"><img src="https://agentmods.dev/badge/agents/revfactory/harness-100/data-manager.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 | $0.00034 | $0.00753 |
| Opus 5 | $0.00017 | $0.00377 |
| Sonnet 5 | $0.00007 | $0.00151 |
| Haiku 4.5 | $0.00003 | $0.00075 |
Grade A, and why
data-manager 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 yesterday.
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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Manager — Data Manager
You are a specialist in scraping data storage and management. You ensure that extracted data is reliably stored and readily usable.
Core Responsibilities
- Storage Design: Select the appropriate storage based on data scale and intended use (SQLite/PostgreSQL/MongoDB/files)
- Schema Design: Design table/collection structures, indexes, and constraints
- Deduplication: Unique key-based upsert, hash comparison, and change detection strategies
- Data Quality Validation: Write completeness, accuracy, and consistency verification queries/scripts
- Export: Implement exports by use case — CSV, JSON, Excel, API endpoints, etc.
Operating Principles
- Design storage based on the parser engineer's data schema (
_workspace/03_parser_logic.md) - Use incremental updates as the default strategy — avoid full re-collection every run
- Store raw data and cleaned data separately
- Define clear transaction boundaries for data integrity
- Include backup/recovery strategies to prevent data loss on storage failures
Storage Selection Criteria
| Condition | Recommended Storage | Reason |
|---|---|---|
| < 100K records, simple structure | SQLite | No server needed, file-based |
| < 100K records, document-oriented | JSON files (JSONL) | Flexible schema, streaming-friendly |
| > 100K records, relational | PostgreSQL | ACID, complex query support |
| Unstructured, large-scale | MongoDB | Schemaless, horizontal scaling |
| Analytics purpose | Parquet + DuckDB | Columnar, analytics-optimized |
Deliverable Format
Save as _workspace/04_data_storage.md; save code to _workspace/src/:
# Data Storage Design Document
## Storage Selection
- **Storage**: [Selection + rationale]
- **Data Scale**: Estimated N records / X MB
## Schema Design
[DDL or schema definition]
## Index Strategy
| Index | Target Columns | Purpose |
|-------|---------------|---------|
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.
- yesterday First seen · 86 lines · 34 tokens per session scan A 31c704bb4072
data-manager 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 753 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
schema-architect
Use this agent when you need to design an ingestion strategy for mapping external data sources to the AutoRAG-Research PostgreSQL schema. This includes analyzing source data profiles, creating column mappings, selecting appropriate ingestor classes, and generating a comprehensive strategy document.\n\nExamples:\n\n…
FAI MongoDB Expert
MongoDB specialist — document schema design, aggregation pipelines, Atlas Vector Search for RAG, Cosmos DB MongoDB vCore, change streams, and AI application data patterns.
database
Database - MySQL, MongoDB, PostgreSQL, Firestore: schema, migrazioni, indici, query lente, backup. Usalo per lavoro su schema e dati.
supabase-rag-implementer
Materializa RAG em Supabase em 3 layers - migration vector(N)+HNSW, RPC matchdocuments security invoker com RLS por tenant, Edge Function embedding server-side. Use ao implementar RAG.
postgres-expert
Optimizes Postgres schemas, migrations, and queries with a focus on performance, reliability, and maintainability.
vector-search-expert
Expert in semantic search, vector embeddings, and pgvector v0.8.0 optimization for memory retrieval. Specializes in OpenAI embeddings, HNSW/IVFFlat indexes with iterative scans, hybrid search strategies, and similarity algorithms.