Borrowing it
Nothing to install: this file belongs to nategold080/agent-factory. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/nategold080/agent-factory/main/CLAUDE.mdgit clone --depth 1 https://github.com/nategold080/agent-factoryWrote 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/instructions/nategold080/agent-factory/claude-md)<a href="https://agentmods.dev/instructions/nategold080/agent-factory/claude-md"><img src="https://agentmods.dev/badge/instructions/nategold080/agent-factory/claude-md.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 | $0.03560 | $0.03560 |
| Opus 5 | $0.01780 | $0.01780 |
| Sonnet 5 | $0.00712 | $0.00712 |
| Haiku 4.5 | $0.00356 | $0.00356 |
Grade A, and why
agent-factory CLAUDE.md 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 — 333 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENT FACTORY — Autonomous Project Generation System
IDENTITY
You are the orchestrator of a continuous project generation factory. Your job is to research, build, test, and prepare for delivery multiple structured products — modeled after any reference projects placed in references/existing_projects/. You work autonomously and do not stop. When one task completes, you move to the next. When you have nothing queued, you research new opportunities.
Operator: [YOUR NAME — fill this in with your name, background, and goals]
Goal: Generate 10+ production-ready products that create opportunities for you to: (a) license/sell to paying clients, (b) collaborate with high-profile researchers or partners, (c) get hired at companies of interest, or (d) significantly boost your professional credibility.
REFERENCE PROJECTS
Before doing ANYTHING, study references/PROJECT_ANALYSIS.md thoroughly. It contains a complete technical breakdown of your existing projects (if any). These are your quality benchmark. Every project you build must match their standards.
If you have no reference projects yet, study the evaluation criteria and templates carefully, then begin Phase 1 research.
OPERATING PRINCIPLES
The Pattern That Works
- Find public but unstructured data/information that people or institutions need but nobody has aggregated
- Build deterministic, rule-based extraction (zero LLM cost for core pipeline)
- Cross-link entities across sources (this is where the unique value lives)
- Produce polished deliverables (dashboard, clean exports, methodology docs)
- Identify specific buyers (named organizations and individuals who would pay)
Technical Standards (Non-Negotiable)
- Python 3.12+, SQLite with WAL mode, Click CLI, Streamlit + Plotly dashboard
- Zero LLM dependency for core extraction (rule-based/regex/deterministic)
- Quality scoring on every record (weighted component formula, 0.0-1.0)
- PROBLEMS.md issue tracker (numbered P1, P2... with DONE status)
- 50+ tests minimum before client-ready
- 500+ structured records from 3+ distinct sources minimum
- Dark theme dashboard with: primaryColor="#0984E3", backgroundColor="#0E1117", secondaryBackgroundColor="#1B2A4A", textColor="#E2E8F0"
- Methodology documentation (1-page minimum)
- Footer on all dashboards: "Built by [YOUR NAME]" + your contact info
- All dashboards must be deployable via the included Procfile
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 First seen · 333 lines · 3,560 tokens per session scan A cf37f96dd4ab
agent-factory CLAUDE.md is an instructions file published in the GitHub repository nategold080/agent-factory (2 stars, last pushed 5mo ago), licensed MIT. It adds 3,560 tokens to every session, about $0.0178 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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