gsd-2-agent-framework
reason-machines/trending-skills
Autonomous spec-driven agent framework for long-running coding tasks with context isolation and cost tracking
What is gsd-2-agent-framework?
GSD 2 is a CLI system that turns structured project specs into running software autonomously. It breaks work into milestones, slices, and context-window-sized tasks, dispatching each through a fresh LLM session with git worktree isolation, crash recovery, and cost tracking. Use it when you want to walk away from a project and return to a built, tested result with clean git history.
- Breaks projects into hierarchical work units (milestones → slices → tasks) that fit in single context windows
- Manages fresh LLM sessions per task with pre-inlined context (codebase, decisions, prior outputs)
- Isolates work in git worktrees and commits with structured messages; squash-merges milestones to main
- Detects stuck agents, recovers from crashes using lock files and session artifacts, and tracks token/cost per unit
- Enforces mechanically verifiable must-haves (shell commands, file checks) to confirm task completion
- Provides cost controls, dashboards, and detailed ledgers to monitor spending across units
How to install gsd-2-agent-framework
npx skills add https://github.com/reason-machines/trending-skills --skill gsd-2-agent-framework- Node.js 18 or later
- npm (to install gsd-pi globally)
- An Anthropic API key (Claude model access via Pi SDK)
- A project with ROADMAP.md and PROJECT.md files describing milestones, slices, and tech stack
How to use gsd-2-agent-framework
- 1.Install globally: npm install -g gsd-pi
- 2.Create ROADMAP.md listing milestones and slices with descriptions
- 3.Create PROJECT.md with project goals, tech stack, and coding conventions
- 4.Run /gsd init to scaffold the .gsd/ directory and initialize STATE.md
- 5.Run /gsd auto to start autonomous execution; the agent will research, plan, and execute each task in fresh sessions
- 6.Monitor progress with /gsd status and /gsd costs; pause with --budget flag if needed
- 7.Review git history and SUMMARY.md files in .gsd/ for each completed slice
- 8.Merge completed milestones to main via git; the framework manages worktrees and commit structure
Use cases
- Build a full REST API backend by specifying milestones (auth, core endpoints, advanced features) and letting the agent execute each slice autonomously
- Scaffold a React + TypeScript frontend by defining slices (component library, pages, state management) with research and planning phases per slice
- Migrate or refactor a legacy codebase by breaking the work into slices with explicit must-haves and letting the agent handle each in isolation
- Prototype a multi-service system where each service is a milestone, each major feature is a slice, and the agent handles research, planning, and implementation per task
- Engineering teams wanting to delegate multi-day coding projects to autonomous agents with human oversight via git history and cost dashboards
- Solo developers building large projects who want to set a spec and return to working code without micromanaging the agent
- Technical leads designing systems where work must be broken into verifiable, context-bounded units for cost and quality control
- Projects requiring clean git history and architectural decision tracking for compliance or knowledge transfer
gsd-2-agent-framework FAQ
GSD writes a lock file at .gsd/LOCK when a unit starts. If interrupted, the next /gsd auto run detects the lock, synthesizes a recovery briefing from all session artifacts (file writes, shell output, partial completions), and resumes with full context continuity.
Every task plan includes must-haves — explicit, checkable criteria like shell commands (npm test passes), file existence checks, or curl requests. The execute phase ends only when the LLM confirms all must-haves are met.
Yes. Use /gsd run --slice M1/S4 to run research + plan + execute for one slice, or /gsd run --task M1/S4/T2 for a single task, or /gsd run --phase research M1/S4 for just the research phase.
GSD creates a worktree branch (e.g., milestone/M1) at the start of each milestone. Each task commits with a structured message [M1/S1/T1]. Each slice commits a summary. When the milestone is done, GSD squash-merges to main as one clean entry, keeping the main branch readable.
Set a budget with /gsd auto --budget 5.00. The cost ledger at .gsd/costs/ledger.json tracks every unit's token count and cost. Auto mode pauses before overspending. Use /gsd costs for detailed breakdowns and projections.
Full instructions (SKILL.md)
Source of truth, from reason-machines/trending-skills.
name: gsd-2-agent-framework description: Meta-prompting, context engineering, and spec-driven development system for autonomous long-running coding agents triggers:
- gsd autonomous agent
- spec-driven development
- context engineering coding
- long running agent task
- gsd auto mode
- milestone slice task hierarchy
- gsd-pi cli agent
- autonomous coding agent framework
GSD 2 — Autonomous Spec-Driven Agent Framework
Skill by ara.so — Daily 2026 Skills collection
GSD 2 is a standalone CLI that turns a structured spec into running software autonomously. It controls the agent harness directly — managing fresh context windows per task, git worktree isolation, crash recovery, cost tracking, and stuck detection — rather than relying on LLM self-loops. One command, walk away, come back to a built project with clean git history.
Installation
npm install -g gsd-pi
Requires Node.js 18+. Works with Claude (Anthropic) as the underlying model via the Pi SDK.
Core Concepts
Work Hierarchy
Milestone → a shippable version (4–10 slices)
Slice → one demoable vertical capability (1–7 tasks)
Task → one context-window-sized unit of work
Iron rule: A task must fit in one context window. If it can't, split it into two tasks.
Directory Layout
project/
├── .gsd/
│ ├── STATE.md # current auto-mode position
│ ├── DECISIONS.md # architecture decisions register
│ ├── LOCK # crash recovery lock file
│ ├── milestones/
│ │ └── M1/
│ │ ├── slices/
│ │ │ └── S1/
│ │ │ ├── PLAN.md # task breakdown with must-haves
│ │ │ ├── RESEARCH.md # codebase/doc scouting output
│ │ │ ├── SUMMARY.md # completion summary
│ │ │ └── tasks/
│ │ │ └── T1/
│ │ │ ├── PLAN.md
│ │ │ └── SUMMARY.md
│ └── costs/
│ └── ledger.json # per-unit token/cost tracking
├── ROADMAP.md # milestone/slice structure
└── PROJECT.md # project description and goals
Commands
/gsd auto — Primary Autonomous Mode
Run the full automation loop. Reads .gsd/STATE.md, dispatches each unit in a fresh session, handles recovery, and advances through the entire milestone without intervention.
/gsd auto
# or with options:
/gsd auto --budget 5.00 # pause if cost exceeds $5
/gsd auto --milestone M1 # run only milestone 1
/gsd auto --dry-run # show dispatch plan without executing
/gsd init — Initialize a Project
Scaffold the .gsd/ directory from a ROADMAP.md and optional PROJECT.md.
/gsd init
Creates initial STATE.md, registers milestones and slices from your roadmap, sets up the cost ledger.
/gsd status — Dashboard
Shows current position, per-slice costs, token usage, and what's queued next.
/gsd status
Output example:
Milestone 1: Auth System [3/5 slices complete]
✓ S1: User model + migrations
✓ S2: Password auth endpoints
✓ S3: JWT session management
→ S4: OAuth integration [PLANNING]
S5: Role-based access control
Cost: $1.84 / $5.00 budget
Tokens: 142k input, 38k output
/gsd run — Single Unit Dispatch
Execute one specific unit manually instead of running the full loop.
/gsd run --slice M1/S4 # run research + plan + execute for a slice
/gsd run --task M1/S4/T2 # run a single task
/gsd run --phase research M1/S4 # run just the research phase
/gsd run --phase plan M1/S4 # run just the planning phase
/gsd migrate — Migrate from v1
Import old .planning/ directories from the original Get Shit Done.
/gsd migrate # migrate current directory
/gsd migrate ~/projects/old-project # migrate specific path
/gsd costs — Cost Report
Detailed cost breakdown with projections.
/gsd costs
/gsd costs --by-phase
/gsd costs --by-slice
/gsd costs --export costs.csv
Project Setup
1. Write ROADMAP.md
# My Project Roadmap
## Milestone 1: Core API
### S1: Database schema and migrations
Set up Postgres schema for users, posts, and comments.
### S2: REST endpoints
CRUD endpoints for all resources with validation.
### S3: Authentication
JWT-based auth with refresh tokens.
## Milestone 2: Frontend
### S1: React app scaffold
...
2. Write PROJECT.md
# My Project
A REST API for a blogging platform built with Express + TypeScript + Postgres.
## Tech Stack
- Node.js 20, TypeScript 5
- Express 4
- PostgreSQL 15 via pg + kysely
- Jest for tests
## Conventions
- All endpoints return `{ data, error }` envelope
- Database migrations in `db/migrations/`
- Feature modules in `src/features/<name>/`
3. Initialize
/gsd init
4. Run
/gsd auto
The Auto-Mode State Machine
Research → Plan → Execute (per task) → Complete → Reassess → Next Slice
Each phase runs in a fresh session with context pre-inlined into the dispatch prompt:
| Phase | What the LLM receives | What it produces |
|---|---|---|
| Research | PROJECT.md, ROADMAP.md, slice description, codebase index | RESEARCH.md with findings, gotchas, relevant files |
| Plan | Research output, slice description, must-haves | PLAN.md with task breakdown, verification steps |
| Execute (task N) | Task plan, prior task summaries, dependency summaries, DECISIONS.md | Working code committed to git |
| Complete | All task summaries, slice plan | SUMMARY.md, UAT script, updated ROADMAP.md |
| Reassess | Completed slice summary, full ROADMAP.md | Updated roadmap with any corrections |
Must-Haves: Mechanically Verifiable Outcomes
Every task plan includes must-haves — explicit, checkable criteria the LLM uses to confirm completion. Write them as shell commands or file existence checks:
## Must-Haves
- [ ] `npm test -- --testPathPattern=auth` passes with 0 failures
- [ ] File `src/features/auth/jwt.ts` exists and exports `signToken`, `verifyToken`
- [ ] `curl -X POST http://localhost:3000/auth/login` returns 200 with `{ data: { token } }`
- [ ] No TypeScript errors: `npx tsc --noEmit` exits 0
The execute phase ends only when the LLM can check off every must-have.
Git Strategy
GSD manages git automatically in auto mode:
main
└── milestone/M1 ← worktree branch created at start
├── commit: [M1/S1/T1] implement user model
├── commit: [M1/S1/T2] add migrations
├── commit: [M1/S1] slice complete
├── commit: [M1/S2/T1] POST /users endpoint
└── ...
After milestone complete:
main ← squash merge of milestone/M1 as "[M1] Auth system"
Each task commits with a structured message. Each slice commits a summary commit. The milestone squash-merges to main as one clean entry.
Crash Recovery
GSD writes a lock file at .gsd/LOCK when a unit starts and removes it on clean completion. If the process dies:
# Next run detects the lock and auto-recovers:
/gsd auto
# Output:
# ⚠ Lock file found: M1/S3/T2 was interrupted
# Synthesizing recovery briefing from session artifacts...
# Resuming with full context
The recovery briefing is synthesized from every tool call that reached disk — file writes, shell output, partial completions — so the resumed session has context continuity.
Cost Controls
Set a budget ceiling to pause auto mode before overspending:
/gsd auto --budget 10.00
The cost ledger at .gsd/costs/ledger.json:
{
"units": [
{
"id": "M1/S1/research",
"model": "claude-opus-4",
"inputTokens": 12400,
"outputTokens": 3200,
"costUsd": 0.21,
"completedAt": "2025-01-15T10:23:44Z"
}
],
"totalCostUsd": 1.84,
"budgetUsd": 10.00
}
Decisions Register
.gsd/DECISIONS.md is auto-injected into every task dispatch. Record architectural decisions here and the LLM will respect them across all future sessions:
# Decisions Register
## D1: Use kysely not prisma
**Date:** 2025-01-14
**Reason:** Better TypeScript inference, no code generation step needed.
**Impact:** All DB queries use kysely QueryBuilder syntax.
## D2: JWT in httpOnly cookie, not Authorization header
**Date:** 2025-01-14
**Reason:** Better XSS protection for the web client.
**Impact:** Auth middleware reads `req.cookies.token`.
Stuck Detection
If the same unit dispatches twice without producing its expected artifact, GSD:
- Retries once with a deep diagnostic prompt that includes what was expected vs. what exists on disk
- If the second attempt fails, stops auto mode and reports:
✗ Stuck on M1/S3/T1 after 2 attempts
Expected: src/features/auth/jwt.ts (not found)
Last session: .gsd/sessions/M1-S3-T1-attempt2.log
Run `/gsd run --task M1/S3/T1` to retry manually
Skills Integration
GSD supports auto-detecting and installing relevant skills during the research phase. Create SKILLS.md in your project:
# Project Skills
- name: postgres-kysely
- name: express-typescript
- name: jest-testing
Skills are injected into the research and plan dispatch prompts, giving the LLM curated knowledge about your exact stack without burning context on irrelevant docs.
Timeout Supervision
Three timeout tiers prevent runaway sessions:
| Timeout | Default | Behavior |
|---|---|---|
| Soft | 8 min | Sends "please wrap up" steering message |
| Idle | 3 min no tool calls | Sends "are you stuck?" recovery prompt |
| Hard | 15 min | Pauses auto mode, preserves all disk state |
Configure in .gsd/config.json:
{
"timeouts": {
"softMinutes": 8,
"idleMinutes": 3,
"hardMinutes": 15
},
"defaultModel": "claude-opus-4",
"researchModel": "claude-sonnet-4"
}
TypeScript Integration (Pi SDK)
GSD is built on the Pi SDK. You can extend it programmatically:
import { GSDProject, AutoRunner } from 'gsd-pi';
const project = await GSDProject.load('/path/to/project');
// Check current state
const state = await project.getState();
console.log(state.currentMilestone, state.currentSlice);
// Run a single slice programmatically
const runner = new AutoRunner(project, {
budget: 5.00,
onUnitComplete: (unit, cost) => {
console.log(`Completed ${unit.id}, cost: $${cost.toFixed(3)}`);
},
onStuck: (unit, attempts) => {
console.error(`Stuck on ${unit.id} after ${attempts} attempts`);
process.exit(1);
}
});
await runner.runSlice('M1/S4');
Custom Dispatch Hooks
Inject custom context into any dispatch prompt:
// .gsd/hooks.ts
import type { DispatchHook } from 'gsd-pi';
export const beforeTaskDispatch: DispatchHook = async (ctx) => {
// Append custom context to every task dispatch
return {
...ctx,
extraContext: `
## Live API Docs
${await fetchInternalAPIDocs()}
`
};
};
Register in .gsd/config.json:
{
"hooks": "./hooks.ts"
}
Roadmap Reassessment
After each slice completes, GSD runs a reassessment pass that may:
- Re-order upcoming slices based on discovered dependencies
- Split a slice that turned out larger than expected
- Mark a slice as no longer needed
- Add a new slice for discovered work
The LLM edits ROADMAP.md in place. You can review diffs with:
git diff ROADMAP.md
To disable reassessment:
{
"reassessment": false
}
Troubleshooting
Auto mode stops immediately with "no pending slices"
All slices in ROADMAP.md are marked [x]. Reset a slice: remove [x] from its entry and delete .gsd/milestones/M1/slices/S3/SUMMARY.md.
LLM keeps failing must-haves
Check .gsd/sessions/ for the last session log. Common causes: must-have references wrong file path, or test command needs environment variable. Adjust must-haves in the task's PLAN.md and re-run with /gsd run --task M1/S3/T2.
Cost ceiling hit unexpectedly
The research phase on large codebases can be expensive. Set researchModel to a cheaper model in config, or reduce codebase index depth.
Lock file left after clean exit
rm .gsd/LOCK
/gsd auto
Git worktree conflicts
git worktree list # see active worktrees
git worktree remove .gsd/worktrees/M1 --force
/gsd auto # recreates cleanly
Session file too large for recovery
If .gsd/sessions/ grows large, GSD compresses sessions older than 24h automatically. Manual cleanup:
/gsd cleanup --sessions --older-than 7d
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