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* fix(#1936): reconstruct OpenCode review from JSON events; diagnosable empty-output stub On a large review prompt, OpenCode's default `build` agent runs a few read tool calls then ends its turn with zero output tokens (reason:"stop", output:0), so `opencode run --format default` emits empty stdout. The reviewer block redirected stderr to /dev/null and wrote a generic "failed or returned empty output" stub — so the phase silently lost its second independent reviewer with no diagnostic and no timeout. Rewrite the OpenCode reviewer block to invoke `--format json` as the primary call and reconstruct the review from the assistant `text` parts (jq). Capture stderr to a `.err` sidecar (mirrors the Codex block). When the agent emits no text, surface the stop reason, output-token count, and stderr so the failure is diagnosable. Gate the stub on the extracted CONTENT, not the output file size — an empty jq extraction still prints a lone newline that a `[ -s file ]` check would treat as populated. Document the wall-clock timeout as a Bash-tool param (macOS lacks GNU timeout; opencode has no native timeout flag). review.md was already at the DEFAULT size-tier ceiling (40956/40960), so the fix cannot fit without reclassifying it into the LARGE tier (it is a multi-reviewer orchestration file that outgrew "focused single-purpose"; 43.4 KB sits well under the LARGE high-water mark). Recapture the 16 golden-install fixtures — the diff is exactly one review.md hash per runtime. Regression block folded into review-default-reviewers-workflow.test.cjs (new bug-NNNN test files are not accepted). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * chore(#1936): add changeset * test(#1936): property-test the OpenCode review jq reconstruction Address the re-review's one actionable finding: the jq JSON-event → text reconstruction had no fast-check property test. Add tests/opencode-review-reconstruction.property.test.cjs. It extracts the two shipped jq programs (OPENCODE_REVIEW, OPENCODE_DIAG) verbatim from gsd-core/workflows/review.md and runs the real jq — not a reimplementation — so the shipped logic is what gets tested. Properties: the reconstructed review equals the newline-join of every assistant text part (order preserved); a stream with no text part reconstructs to empty (drives the #1936 stub); null/absent text parts are dropped, never rendered as "null". Plus example-based coverage of the diagnostic edges the reviewer cited: missing .tokens.output and no step_finish degrade to "?"; non-JSON stdout makes jq fail rather than masquerade as a review. Verified the invariant has teeth (a comma-join jq fails the property). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * test(#1936): skip jq reconstruction property test when jq is absent The property test shells out to `jq`, which GitHub's windows-latest runners do not ship (macOS/Linux runners do). `execFileSync('jq')` therefore ENOENT-failed the whole file on `test (windows-latest, *)`. Probe `jq --version` at load and skip the suite when jq is not on PATH — the reconstruction logic is platform-independent, so the assertions still run in full on every jq-present runner (mirrors how golden-install-parity skips on win32). Verified: jq present → 7 pass; jq removed from PATH → 7 skipped, 0 fail. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * test(#1936): skip jq reconstruction property test on Windows, not just when jq is absent The prior guard skipped only when `jq` was absent from PATH — but the windows-latest runners DO ship jq, so the suite still ran there and failed with `jq: parse error: Invalid numeric literal` (confirmed from the CI job log). Root cause is Node's child_process argument quoting mangling the jq program (it embeds double quotes) on Windows, not the shipped review.md logic — the macOS/Linux legs pass. Gate the suite on `process.platform === 'win32'` (still also skipping when jq is absent), mirroring golden-install-parity's win32 skip. Logic is platform-independent and fully asserted on every macOS/Linux CI leg. Verified: macOS → 7 pass; simulated win32 → skips. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.8 (1M context) <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 your first project:
/gsd-new-project
New here? Follow Your first project for a guided walkthrough from install to first shipped phase.
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.