6efd2b4016
A phase whose slug's first word is a bare single digit (dir
`46-6-rs-pipeline-orchestrator`, roadmap phase "6 Rs Pipeline Orchestrator" →
slug `6-rs-…`) had its phase token over-collected as `46-6` instead of `46`, so
`gsd-tools` phase-by-number lookups (init.plan-phase, init.phase-op, and
downstream execute/verify/ship) resolved phase_dir=null / has_context=false.
Root cause: an over-broad "looks numeric" test (`/^\d/`, `\d+(?:-\d+)*`)
classified token segments and could not distinguish a legitimate zero-padded
sub-phase segment (`01`, `02`) from a single-digit slug word (`6`). Zero-padded
phase/sub-phase segments are always ≥2 digits, so requiring ≥2 digits is the
structural distinguisher. Applied consistently across every same-class
implementation the triage identified (fixing extractPhaseToken alone leaves the
health-check and milestone-filter subsystems exposed):
- src/phase-id.cts extractPhaseToken — a pure-numeric leading segment continues
only with ≥2-digit segments; a letter-prefixed milestone id (`M1`) still
admits its single-digit sub-phase (`M1-2`).
- src/validate.cts PHASE_TOKEN_FROM_DIR_RE (W005/W006/W007 health checks) and
canonicalPlanStem (I001 plan/summary pairing).
- src/roadmap-parser.cts isDirInMilestone numericRe (getMilestonePhaseFilter —
highest blast radius: a false negative silently excludes a phase from the
milestone).
- src/core-utils.cts + its verbatim duplicate src/phase.cts extractCanonicalPlanId.
Regression tests added across tests/{phase-id,health-validation,core-utils,
roadmap-parser}.test.cjs using the shared `46-6-rs-…` fixture, asserting the
token resolves to `46` (not `46-6`) while legit multi-segment tokens
(`01-02`, `02-03-04`, `M1-2`, `68-01`, `02-01`) are unchanged. The
roadmap-parser test exercises the real getMilestonePhaseFilter end-to-end.
The residual ≥2-digit-leading-slug case (e.g. `NN-2024-roadmap`) stays out of
scope — subsumed by the #612 / #565 phase-ID convention work, per the issue.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
GSD Core
Git. Ship. Done.
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A light-weight meta-prompting, context engineering, and spec-driven development system for Claude Code, OpenCode, Antigravity CLI, Kimi CLI, Kilo, Codex, Copilot, Cursor, Windsurf, and more.
What is GSD Core
GSD Core is a context-engineering and spec-driven development framework that drives AI coding agents (Claude Code, Codex, Antigravity CLI, Kimi CLI, Copilot, Cursor, and more) through a disciplined phase loop. It solves context rot — the quality degradation that accumulates as an AI fills its context window — by running all heavy research, planning, and execution work in fresh-context subagents while keeping your main session lean.
How it works
Each milestone repeats the same five-step loop, one phase at a time:
- Discuss — capture implementation decisions before anything is planned
- Plan — research, decompose, and verify the plan fits a fresh context window
- Execute — run plans in parallel waves; each executor starts with a clean 200k-token context
- Verify — walk through what was built; diagnose and fix before declaring done
- Ship — create the PR, archive the phase, repeat for the next one
Quickstart
npx @opengsd/gsd-core@latest
The installer prompts for your runtime (Claude Code, OpenCode, Antigravity CLI, Kimi CLI, Kilo, Codex, Copilot, Cursor, Windsurf, and more) and whether to install globally or locally. The installer is required for cross-runtime compatibility — do not copy files from agents/ or commands/ directly.
On another runtime or without Node.js? See Install on your runtime.
Once installed, start a new project or onboard an existing repo:
/gsd-new-project # greenfield project
/gsd-onboard # existing codebase
New here? Follow Your first project for a guided walkthrough from install to first shipped phase, or Onboarding an existing codebase for brownfield setup.
Documentation
Tutorials — learning by doing:
How-to guides — task-focused recipes:
Reference — authoritative facts:
Explanation — concepts and design decisions:
Full index: docs/README.md. Other languages: 日本語 · 한국어 · Português · 简体中文.
Why it works
Most AI-coding setups fail at scale because context bloat silently degrades output quality, there is no shared memory between sessions, and nothing verifies that code actually works. GSD Core solves all three: heavy work runs in fresh subagents, structured artifacts like STATE.md and CONTEXT.md survive session boundaries, and the verify step walks through what was built and generates fix plans before a phase is declared done. See docs/explanation/context-engineering.md for the full reasoning.
Troubleshooting? See docs/how-to/recover-and-troubleshoot.md.
Community
| Project | Platform |
|---|---|
| gsd-opencode | Original OpenCode port |
| Discord | Community support |
Star History
License
MIT License. See LICENSE for details.
Claude Code is powerful. GSD Core makes it reliable.