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State Enforcement (v2.0):
- .state file as single source of truth for task phase
- Approval gates for research, decomposition, design, test_design
- status.py --transition refuses illegal phase transitions
- status.py --validate-folder detects out-of-order artifacts
- status.py --audit checks all tasks for violations
- status.py --create-task is the only valid way to create tasks
- Pre-v2.0 tasks without .state are UNTRACKED -- all commands refuse them
- New --upgrade command bootstraps .state files for existing tasks
Project Scoping:
- --project flag added to all status.py commands across 16+ files
- _find_project_dir errors instead of silently falling back to ~/.automaton/
- --scope-check marks framework files OUT_OF_SCOPE when working on a project
- Dashboard handlers use stored project_root instead of re-detecting from CWD
- Prompts reference ~/.automaton/scripts/vram_detect.py (not {project}/.automaton/)
Harness Integration:
- status.py --can-edit now supports project-level checks (no --task required)
- --can-edit --file checks file scope without --task
- --json output for machine-readable harness integration
- opencode plugin (plugins/automaton-guard/plugin.ts) intercepts edit/write
- Git pre-commit hook (scripts/git-hooks/pre-commit) blocks commits without task
- Formal integration contract (contracts/harness-integration.md)
Other:
- upgrade.sh delegates to status.py --upgrade instead of manual heuristics
- Phase prompts reference --project {project} for multi-project scoping
- 200 tests passing (14 new)
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Framework Configuration
This file contains global framework settings that apply across all projects.
VRAM Configuration
Settings for task decomposition based on available VRAM.
- Auto-detect: Yes # Detect GPU VRAM, RAM, and model context window automatically
- Target context: 16k tokens # Override auto-detect if needed
- Headroom: 25% # Leave headroom for code, context, and reasoning
- Max peak context per sub-task: 12k tokens # Max context for any single sub-task
Auto-detection
When Auto-detect: Yes, the framework probes your system to detect:
- GPU VRAM (via
nvidia-smiorlspci) - System RAM (via
/proc/meminfoorsysctl) - Model context window (via API config or model name lookup)
- Framework overhead (by reading all loaded prompt files)
To disable auto-detection and use manual values:
## VRAM Configuration
- **Auto-detect**: No
- **Target context**: 8k
- **Headroom**: 30%
- **Max peak context per sub-task**: 5.6k
Model Configuration
Settings for the LLM model being used.
- Model: auto # Use auto-detection from API config, or specify explicitly (e.g., gpt-4o, claude-3-5-sonnet)
- Override context window: auto # Override auto-detection, or specify (e.g., 128k, 200k)
Auto-detection
When Model: auto, the framework detects the model name from:
.agent.mdin the project (if specified there)- API config files (
.env,config.yaml,config.json, etc.) - Model name lookup by name (e.g., gpt-4o → 128k, claude-3-5-sonnet → 200k)
To disable auto-detection and use manual values:
## Model Configuration
- **Model**: gpt-4o
- **Override context window**: 128k
System Requirements
Requirements for the environment the framework runs in.
- nvidia-smi: Required if NVIDIA GPU (for VRAM detection)
- lspci: Fallback if NVIDIA GPU not available (for AMD GPU VRAM detection)
- /proc/meminfo: Required for RAM detection (Linux)
- sysctl: Fallback for RAM detection (macOS)
Framework Version
- Version: 2.0
- State enforcement: enabled (
.statefile +status.py)