This skill did not exist in Grok Build. I created it after seeing the power of structured autonomous looping in Claude Code’s /goal and the Ralph Wiggum technique. LLM looping until a clear goal is verifiably met is far more powerful than bespoke agents or constant manual prompting. This is my personal extension for Grok Build.
After using Claude Code’s /goal and the Ralph Wiggum looping pattern, the difference became obvious:
- Ad-hoc prompting and custom one-off agents create high friction and inconsistent results.
- Persistent, goal-driven looping with clear verification is dramatically more reliable for complex work.
Grok Build did not have an equivalent first-class mechanism, so I built one as a personal extension.
This repo exists to illustrate the repeatable pattern:
Clear objective + /goal skill discipline → High-reliability delivery
It shows the difference between traditional LLM loops / bespoke agents and the structured, verifiable goal pursuit I created for my personal use cases.
- A demonstration of the
/goalskill in action. - A case study in structured, goal-driven development using Grok.
- Evidence that complex, multi-component applications with real integration requirements (Amazon Connect screen pop + bidirectional sync + iframe embedding) can be delivered with high fidelity using the goal skill process.
The included CRM application is not the point. It is the test workload.
Everything here (application + all documentation) was generated from one prompt using the /goal skill.
See the exact prompt: PROMPT.md
The skill enforced:
- Explicit
GOAL.mdwith measurable acceptance criteria - Canonical todo-driven execute + verify cycles
- Layered testing (structure → backend → integration → persistence → runtime)
- Mandatory real runtime verification ("open the app and prove the full flow works")
- Self-documenting outputs
Full details: GOAL-SKILL.md
To properly test the skill, I chose a non-trivial real-world scenario:
- Build a Salesforce-style CRM panel that can be embedded in Amazon Connect
- Must support real screen pops via ANI/phone
- Must support bidirectional actions (create case, log call outcome) that persist via real APIs
- Must work when loaded as an iframe
- Must be runnable with one command
- Must be production-demo ready with zero console errors
This requirement forced the skill (and the model) to handle:
- Full-stack development
- External system integration (Amazon Connect Streams)
- Persistence
- UI/UX constraints
- Verification under realistic constraints
The resulting CRM (called ConnectCRM internally) is a solid, self-contained demo — but its real value is proving what the /goal skill can achieve.
The repo has since been extended with deep Restart Scheme (UK DWP employment support) research, participant/advisor journey maps, and rich ML trajectory synthesis for advisor case management actions (see RESTART_RESEARCH_AND_JOURNEYS.md + lam/restart_* + data/trajectories/restart_*).
| File | Purpose |
|---|---|
GOAL.md |
The live goal definition and verification log created by the skill |
PROMPT.md |
The single prompt that triggered the entire build |
GOAL-SKILL.md |
Deep explanation of the /goal skill and how it was used |
README.md |
This file — positions the skill as the primary deliverable |
- Built from one prompt
- All acceptance criteria met with runtime proof
- Full working application + high-quality documentation
- Clean git history and public GitHub project
- Approximate duration: 30–40 minutes of active work
See the metrics and process details in the documentation files.
I built this skill for my own workflows because I needed a more disciplined way to deliver complex projects with Grok Build. This repo exists to illustrate the repeatable pattern I use:
Clear objective + /goal skill discipline → High-reliability delivery
It shows the difference between traditional LLM loops and bespoke agents and the structured, verifiable goal pursuit I created for my personal use cases.
npm install
npm startOpen http://localhost:3000 to see the CRM that was built as the test case.
The technical implementation details are still present in the code and older sections of the docs for reference.
The ideas and patterns behind this skill come from:
-
Geoffrey Huntley — Ralph Wiggum technique (the original “autonomous loop until done” pattern)
https://ghuntley.com/ralph/ -
Anthropic — Claude Code
/goalcommand
https://code.claude.com/docs/en/goal -
xAI — Grok Build platform
https://x.ai/news/grok-build-cli -
Amazon Connect (used in the proof-of-concept)
- Streams API: https://github.com/amazon-connect/amazon-connect-streams
- Third-party apps: https://docs.aws.amazon.com/connect/latest/adminguide/3p-apps.html
-
Salesforce CTI patterns (reference for realistic CRM integration behavior)
https://github.com/amazon-connect/amazon-connect-salesforce-cti
Special thanks to my mentors for their guidance, insights, and inspiration in AI, agentic systems, Amazon Connect, and generative AI patterns:
- Chris Lott (@chrislott)
- Court Schuett (@schuettc)
Thanks to Wayne Bevan for critical feedback that directly shaped the hardening of this project — the independent verifier, the skeptics doc, and the structural fixes to the skill all came out of taking that feedback seriously.
MIT
This is primarily a demonstration of the /goal skill I created for my personal use cases. The CRM was only the Proof of Concept used to validate it.