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* docs(#3247): record the capability instruction-surface trust model ADR-2363 records the trust posture for third-party capability SKILL.md bodies, which #2322/#2340 made agent-invocable without any content-level control. The path-level protections that fix shipped are all present; no content scanner exists, and external-descriptor-trust.cts never had one. Nothing was bypassed - the control did not exist and the boundary was never written down. D1 records the posture: skill bodies are trusted, unscanned agent instructions. D2 rejects content scanning on Kerckhoffs (a shipped rule set is readable by the adversary who installs it), on threat-model non-transfer from ADR-1577 (there, instructions are anomalous inside data; here they are the payload's legitimate form), and on Goodhart (a scanned-OK line displaces the judgment the consent prompt exists to provoke). D3 replaces the executable/non-executable binary with three classes, adding instruction surface. D4 keeps instruction surfaces out of the v1 disclosureSignature. The signature is NOT the activation binding - hasProjectConsent compares contentHash only, and a global install carries no consent record at all. What re-encoding would do is perturb the signature of every skill-bearing capability and fire a spurious re-consent prompt on its next upgrade, which is what ADR-2782 D4 rule 5 already forbids. If instruction surfaces ever need to be signature-bound, that lands as a versioned v2 signature with a migration, never an in-place re-encoding. Corrects capability-trust-model.md, which claimed skills get lighter consent because they do not execute code - true, and not the relevant property, since the agent is the interpreter. Adds the author-side boundary to develop-a-capability.md and links it from publish-a-capability.md. Both state that per-skill disclosure at the consent prompt lands with #3248 and does not happen today. Docs-only. No behavior change; no consent record perturbed. D5's mechanism is Phase 1 (#3248), which is why the ADR is Proposed. Refs #2363 * chore(#3247): backfill changeset pr number to 3249 --------- Co-authored-by: sim <sim@local>
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.