Find agent skills by outcome

Forge - 9-Stage TDD Workflow

A TDD-first workflow for AI coding agents. Ship features with confidence.

- Recommendations for iteration (A/B testing

feedback loops)

- Keep it minimal and adaptable

including placeholders for iteration or error handling

- Instead

suggest non-AI or hybrid alternatives (e.g.

- Key risks or dependencies to monitor (e.g.

bias checks)

Creative

- Generate a simple

practical starter prompt

- Lightweight/chat-only models (older 7B–13B models

mini variants) — usually lack depth/context/tool reliability

- Mistral Large 2 / Devstral — Very strong price/performance

fast

- Gemini 2.5 Pro / Flash (Google) — Outstanding multimodal (especially video/document understanding)

very large context windows

- Claude 4 Opus/Sonnet (Anthropic) — Exceptional long-form reasoning

writing quality

Current Best-in-Class Ranking (January 2026 – general guidance

always tailor to the process):

- Privacy

open behavior

- Tool use

function calling

If AI is viable

recommend which AI engines are best suited and why.

- Call out misconceptions

dependencies

- Track KPIs (e.g.

20% time savings

Summarize the overall feasibility score (weighted average)

then issue your verdict with clear reasoning.

Interpretation: Clear patterns exist

but context accuracy is critical. Recommend hybrid approach with AI drafts + human review.

When scoring

evaluate these dimensions (suggested weights for averaging: e.g.

| 1 – Not Feasible | Process heavily dependent on expert judgment

implicit knowledge

- What is your budget or resource availability for AI implementation (e.g.

time

- How would you measure the success of AI support (e.g.

time saved

After the interview

provide a structured assessment.

- Are there legal

ethical