9c2927bff4
* test(#4733): pin the cap, unknown-file weight, and isolation rules Failing-first coverage for the three defects that let a Windows conformance chunk be killed at the 600s per-chunk backstop with zero failing tests. The previous boundary rows were VACUOUS: they asserted literal arithmetic (21 * 18122 <= 400000) that cannot fail, and in doing so masked a shipped win32 cap of 23 -- a value that violates the very inequality they claimed to pin. These rows constrain defaultMaxFilesPerChunk itself, from both sides, so the shipped value is a derived maximum rather than a magic number. A second vacuous row was caught by review and removed: it recomputed the isolated set from the function under test using the identical predicate, so it was empty by construction. It is replaced by an exact deepEqual against the expected basenames, a cross-platform identity row, dynamism rows in both directions, an inclusive boundary triplet, and invalid-threshold throw rows. The cross-platform identity row is the regression guard for a threshold that was briefly anchored to the per-platform file-COUNT cap; it fails if isolation ever becomes platform-dependent again. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> * fix(#4733): derive the win32 cap, isolation bar, and unknown weight A Windows conformance chunk was killed at the 600000ms per-chunk backstop with no test having failed, taking next red. Three compounding defects. The win32 cap of 40 permitted 40 * 18122 = 724880ms against a 600000ms backstop -- 121% of it -- so two rounds of budget-tuning could not hold. The cap is now derived: 22 is the largest value satisfying cap * 18122 <= 400000. The budget is 400000, not the raw backstop, because the chunk that died summed to only ~348328ms of per-file time -- a per-chunk overhead gap of at least 1.72x that no per-file table models. A file absent from the timings table was priced at medianWeight. The table is skewed 18.8x, so an unknown weighed 0.0533 -- 19x cheaper than average, and measured 17.5x under its real cost. Unknowns are now priced at the mean. ISOLATED_HEAVY_FILES was a static Set, stale by construction. Isolation is now derived from an absolute ms bar (0.3 * 400000 = 120000ms) converted to weight units via the live table's mean, so a file that gets heavy is isolated automatically instead of waiting for someone to edit a list. Review caught that an earlier cut anchored that bar to the per-platform file-COUNT cap -- a category error, count vs weight, which silently returned seven of the historical eight files to the shared pool on linux/darwin. Since macOS runs the full matrix only after merge, that would have planted a red next no PR could catch. The bar is absolute and platform-independent. Also from review: isolation no longer requires unit-suite membership, so fragment-single-edit-propagation.install.test.cjs -- 575000ms, 96% of the backstop in one file -- is eligible; partitionIsolatedFiles throws on a non-finite or non-positive threshold instead of silently isolating nothing; and stale per-shard figures no test pinned are removed rather than recomputed. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> * chore(#4733): backfill changeset pr number --------- Co-authored-by: sim <sim@local> Co-authored-by: Claude Opus 5 <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
What's new in 1.7.0 → docs/whats-new-1.7.0.md
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.