5ad9a36f35
`review-lane plan` resolved every cross-AI reviewer lane's reasoning effort by spawning `query resolve-execution gsd-plan-checker --host <slug>`. The agent id was a hardcoded literal, so `--host` chose only the argv RENDERING while the LEVEL always came from the installed plan-checker's frontmatter — `low` under every shipped model profile. Every prompt-fed lane therefore ran at a fast structural verifier's effort, and because the rendered argument is a CLI config override it silently beat the effort the operator had configured for that CLI. At `low` a large source-grounded prompt makes a model end its turn with no final message, so the lane came back empty and its stub read as a crash. Effort is a property of the review, so the lane declares it. Two new fields on ReviewerLane — `effortConfigKey` (`review.effort.<slug>`) and `defaultEffort` — carried through each capability manifest and the generated registry, set on the three lanes with an argv effort channel and null on the other nine. A new pure `resolveLaneEffort()` resolves config key -> lane default -> nothing, where "nothing" emits no effort argument at all and the reviewer CLI's own configuration decides; `inherit` selects that path explicitly and an unrecognized level falls back to the lane default rather than being forwarded to a CLI that would reject it. The host's negotiated effortSurface still gates the rendering, so ADR-1239/#2481's trust boundary holds on this path too. Resolving in-process also removes up to twelve subprocess spawns per review. The empty-output stub now names the effort the lane ran at and distinguishes a clean exit from a timeout kill, a non-zero exit, and a process that never ran — `status` is null for both a timeout and a signal, so those were indistinguishable before. The hint is hedged: a clean empty exit is most often a model stopping short, but it is also consistent with a CLI writing its output elsewhere. Also: the capability validator now knows both fields, rejects a malformed key or an out-of-vocabulary default, and rejects a default declared without a config key (a level the operator could never override). An existing end-to-end row in tests/effort-surface-axis.test.cjs asserted the old coupling; it now configures the lane's own key and pins the decoupling in the same real spawn, with the agent execution tier set to a level that must not appear. Emitted-Drift-Ack-Growth: review.md — the effort/model resolution-order table this fix adds. The workflow is where an operator looks to find out which knob set a lane's model and effort; leaving the new key undocumented there is the same invisibility that made the plan-checker coupling survive this long. Emitted-Drift-Ack-Growth: review.md — the effort/model resolution-order table this fix adds. The workflow is where an operator looks to find out which knob set a lane's model and effort, so leaving the new key undocumented there is the same invisibility that let the plan-checker coupling survive. Claude-Session: https://claude.ai/code/session_01CRMEuzNMWn3gs5uUW2ghcF Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com> Co-authored-by: Tom Boucher <trekkie@nomorestars.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.