by michaelpersonal · Codex Skill · ★ 142
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goal-forge Codex skill that turns a rough coding idea into a Codex -ready contract. Pipeline: rough idea → interviewed → tightened → → config readiness check. Goal Forge now treats long-running work as a runtime system with four explicit parts: Scorecard — the metric, checklist, threshold, regression checks, and stop condition Codex should use to judge progress. Feedback loop — the fastest representative check Codex can run repeatedly while iterating, plus the slower final check used before completion. Working memory — markdown files such as , , and that keep multi-hour runs coherent across context compaction. Human control surface — an optional compact with task-specific knobs, sidecar inputs, resource limits, and pivot gates the user can inspect or tune while a goal runs. Modes Interview — open-ended interview that forces decisions on scope, architecture, edge cases, verification. Hard gate: spec is not "done" until has user-approved measurable criteria. Tighten — read skeptically; surface ambiguities with two interpretations + a recommendation. Compile — emit using the XML block structure in .
| Stars | 142 |
| Forks | 10 |
| Language | Python |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 65.3065392648644/100 |
| Last Updated | 2026-05-15 |
| Created | 2026-05-01 |
| Platforms | codex, python |
| Est. Tokens | ~3k |
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goal-forge is Codex skill: turn a rough coding idea into a SPEC.md, GOAL.md, and /goal-ready contract.. It is categorized as a Codex Skill with 142 GitHub stars.
goal-forge is primarily written in Python.
You can find installation instructions and usage details in the goal-forge GitHub repository at github.com/michaelpersonal/goal-forge. The project has 142 stars and 10 forks, indicating an active community.
goal-forge is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to goal-forge on Agent Skills Hub include ai-maestro, upskill, skills. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.