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 skills add wednesday-solutions/ai-agent-skills --skill greenfieldgit clone --depth 1 https://github.com/wednesday-solutions/ai-agent-skillsWrote 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/skills/wednesday-solutions/ai-agent-skills/greenfield)<a href="https://agentmods.dev/skills/wednesday-solutions/ai-agent-skills/greenfield"><img src="https://agentmods.dev/badge/skills/wednesday-solutions/ai-agent-skills/greenfield/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/wednesday-solutions/ai-agent-skills/greenfield"><img src="https://agentmods.dev/badge/skills/wednesday-solutions/ai-agent-skills/greenfield.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00050 | $0.00864 |
| Opus 5 | $0.00025 | $0.00432 |
| Sonnet 5 | $0.00010 | $0.00173 |
| Haiku 4.5 | $0.00005 | $0.00086 |
Grade A, and why
greenfield 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 12d 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 — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Greenfield Planning Skill
Trigger
Run once per project: ws-skills plan
Reads BRIEF.md from the project root (or prompts for one). Asks 5 clarifying questions before planning.
Flow
Brief + Q&A
↓
Research agent (sequential) ← domain landscape, ecosystem, hidden complexity
↓
┌─────────────────────────────────────┐
│ Architect │ PM │ Security (parallel)│ ← spawn 3 subagents simultaneously
└─────────────────────────────────────┘
↓
Synthesis ← combines all into PLAN.md
Agents
1. Research (sequential — runs first)
Builds domain context that all other agents receive. Covers:
- Existing solutions and their weaknesses
- Standard and emerging tech stacks for this domain
- Technologies to avoid and why
- Non-obvious domain challenges
- Integration landscape (auth, payments, comms, etc.)
- Regulatory and compliance context
- Realistic timeline based on similar projects
- Hidden complexity — things that take 3x longer than expected
- Success patterns from the best products in this space
Output: research.md
2–4. Architect, PM, Security (parallel subagents)
Spawn all three simultaneously using the Agent tool. Each receives the full brief, Q&A, and research output as context.
Agent 1 — Architect
Agent 2 — PM ← launch all three in a single message, do not wait
Agent 3 — Security
Wait for all three to complete before running Synthesis.
Architect output: architect.md
- System design overview
- Tech stack with rationale per layer
- Module boundaries and interfaces
- Infrastructure and CI/CD
- Scaling strategy
- Technical risks
PM output: pm.md
- Phases with tasks and acceptance criteria
- Success metrics
- Out of scope items
- Assumptions
Security output: security.md
- Threat model (likelihood + impact)
- Data classification
- Auth strategy recommendation
- Compliance flags
- Concrete security tasks
- Urgent flags
5. Synthesis
Combines research + all three persona outputs into a single PLAN.md covering:
- Overview
- Clarifications table
- Tech stack
- Architecture
- Phases with tasks and acceptance criteria
- Security plan
- Success metrics
- Risks
- Tensions (unresolved disagreements between personas)
- Assumptions
- Out of scope
- Branch naming (GIT-OS format)
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.
- 12d ago First seen · 132 lines · 50 tokens per session scan A 1ecad27105a2
greenfield is a skill published in the GitHub repository wednesday-solutions/ai-agent-skills (168 stars, last pushed 3mo ago), licensed MIT. It adds 50 tokens to every session and 864 once invoked, about $0.0003 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 2026-08-30.
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