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 commands/datacore-one/datacore/gangit clone --depth 1 https://github.com/datacore-one/datacoreWrote 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/commands/datacore-one/datacore/gan)<a href="https://agentmods.dev/commands/datacore-one/datacore/gan"><img src="https://agentmods.dev/badge/commands/datacore-one/datacore/gan.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 | $0.00036 | $0.00536 |
| Opus 5 | $0.00018 | $0.00268 |
| Sonnet 5 | $0.00007 | $0.00107 |
| Haiku 4.5 | $0.00004 | $0.00054 |
Grade A, and why
gan 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 today.
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.
What it actually says
/gan Command
Build products through adversarial multi-agent iteration.
Usage
/gan <one-line description> # Start from scratch
/gan --spec <path> # Start from existing spec
/gan --from-office-hours # Continue from last /office-hours output
Recommended Pipeline
For best results, chain with gstack skills:
/office-hours → Brainstorm & validate the idea (forcing questions)
↓
/plan-ceo-review → Expand scope, find the 10-star product
↓
/gan → Adversarial build: Plan → Generate → Evaluate → iterate
↓
/plan-eng-review → Lock in architecture before shipping
↓
/qa → Systematic QA testing
You can enter at any stage. /gan works standalone for well-defined briefs.
How It Works
Step 1: Plan (Opus)
- Expand brief into full product spec
- Features, design direction, evaluation rubric
- Present to user for approval/modification
Step 2: Generate (Sonnet)
- Implement the spec
- Follow technical stack and design direction exactly
- On subsequent iterations, focus ONLY on evaluator feedback
Step 3: Evaluate (Opus)
- Test against rubric (functionality, design, code quality, UX, performance)
- Score each criterion 0-10
- List specific, actionable fixes with file:line references
Step 4: Decision Gate
- All criteria >= 8/10 → DONE, present to user
- Any criterion < 8/10 → Feed feedback to Generator, iterate
- Max 3 iterations → Present best attempt with evaluator notes
Integration with Forge
For Forge product generation:
/gan "Etsy listing for [product idea]"
The GAN harness is particularly powerful for Forge because:
- Planner generates product spec with marketplace positioning
- Generator creates the product assets/listing
- Evaluator checks against marketplace best practices
Agent
Coordinator: gan-harness agent
Subagents: Planner (Opus), Generator (Sonnet), Evaluator (Opus)
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.
- today First seen · 80 lines · 36 tokens per session scan A 373238ea498b
gan is a command published in the GitHub repository datacore-one/datacore (4 stars, last pushed today), licensed MIT. It adds 36 tokens to every session and 536 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 commands, from other repositories
prd-review
Review the active PRD with Codex and stream normalized findings to JSONL.
prd-archive
Archive the active PRD (blocked until every accepted finding has a receipt).
prd-map
Build a codebase map so PRDs are written with repo context, not blind.
prd-split
Split the approved PRD into one issue spec per manifest entry.
rca-check
Lint an RCA or premortem document against the canonical template.
prd-os-init
Initialize prd-os in this repo (writes .prd-os/config.json).