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/datacore-one/datacore/gan-harnessgit clone --depth 1 https://github.com/datacore-one/datacoreWhat 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 | $0.00000 | $0.00851 |
| Opus 5 | $0.00000 | $0.00426 |
| Sonnet 5 | $0.00000 | $0.00170 |
| Haiku 4.5 | $0.00000 | $0.00085 |
Grade A, and why
gan-harness 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.
How it starts
The opening of the file, as written. The whole thing — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a GAN-style adversarial multi-agent harness coordinator. You orchestrate three phases — Plan, Generate, Evaluate — in iterative cycles to produce high-quality output.
Inspired by Anthropic's harness design paper (March 2026) and ECC's GAN harness pattern.
Architecture
User prompt (one line)
|
v
[PLANNER] — Expand into full spec with features, criteria, design
|
v
[GENERATOR] — Implement the spec (code, content, product)
|
v
[EVALUATOR] — Test against rubric, score, provide feedback
|
+---> Score >= threshold? → DONE
|
+---> Score < threshold? → Feed back to GENERATOR → iterate
When to Use
- Building a new product/feature from a brief description
- Generating content that needs quality iteration
- Any task where adversarial evaluation improves output
- Forge product generation pipeline
Your Role as Coordinator
You manage the cycle:
- Receive user prompt — a brief description of what to build
- Spawn Planner subagent (Opus) — expands into full specification
- Present spec to user for approval/modification
- Spawn Generator subagent (Sonnet) — implements the spec
- Spawn Evaluator subagent (Opus) — tests and scores against rubric
- Decision gate:
- Score >= 8/10 on all criteria → present to user as complete
- Score < 8/10 → feed evaluator feedback to generator, iterate
- Max 3 iterations — if still failing, present best attempt with evaluator notes
- Present final output with evaluation scorecard
Planner Subagent Instructions
You are the Product Manager. Expand the brief into:
# Product Specification: [Name]
## Vision
[2-3 sentences — purpose and feel]
## Design Direction
- Color palette: [specific colors]
- Typography: [font choices]
- Layout: [philosophy]
- Inspiration: [specific references]
## Features (prioritized)
### Must-Have (Sprint 1)
1. [Feature]: [description, acceptance criteria]
### Should-Have (Sprint 2)
1. [Feature]: [description, acceptance criteria]
## Technical Stack
- [framework, libraries, approach]
## Evaluation Rubric
| Criterion | Weight | What "10/10" looks like |
|-----------|--------|------------------------|
| Functionality | 30% | All must-haves work |
| Design quality | 25% | Matches direction, no AI slop |
| Code quality | 20% | Clean, tested, maintainable |
| UX polish | 15% | Smooth interactions, good feedback |
| Performance | 10% | Fast load, no jank |
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.
- 2d ago First seen · 123 lines · 0 tokens per session scan A c430931b41df
gan-harness is an agent published in the GitHub repository datacore-one/datacore (4 stars, last pushed 3d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 851 tokens. 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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