TL;DR: An agentic personal operating system built to automate high-leverage workflows across Claude Code, Codex, Pi, OpenClaw, and other coding agents/runtime platforms.
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Clone this repo
git clone https://github.com/itseffi/personal-os.git cd personal-os -
Run setup
chmod +x setup.sh ./setup.sh
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Start using This automates high-leverage execution end-to-end: it converts raw backlog into prioritized, goal-aligned, verification-enforced action plans.
Open this repo in your agent and run: 1) "Process my backlog from BACKLOG.md into Tasks/**/*.md using AGENTS.md rules." 2) "Show my P0/P1 unblocked tasks aligned to GOALS.md." 3) "Propose today’s top 3 with required verification evidence and commands."
Build Your Personal OS · Workflows · Canonical Skills · Evals · Tutorials (index)
flowchart TD
U["User Prompt"] --> A["Agent Runtime<br/>Claude Code | Codex | Pi | OpenClaw"]
A --> I["Instructions<br/>AGENTS.md + wrappers"]
A --> S["Skills<br/>.agents/skills/*/SKILL.md"]
A --> W["Workflows<br/>Workflows/*.md"]
A --> F["State + Context<br/>Tasks, GOALS, BACKLOG, Knowledge, Resources"]
A -. optional .-> M["MCP Integrations<br/>System/mcp + external services"]
A -. optional .-> D["Subagents<br/>runtime-dependent delegation"]
A --> E["Evals<br/>Evals/ + Evals/skills + scripts/run_skill_evals.py"]
classDef core fill:#ff9891,stroke:#2b2b2b,color:#111111,stroke-width:1.2px;
classDef optional fill:#ffd4d0,stroke:#2b2b2b,color:#111111,stroke-width:1.2px,stroke-dasharray: 4 3;
class U,A,I,S,W,F,E core;
class M,D optional;
Personal OS is designed to work with Claude Code, Codex, Pi, OpenClaw, and similar coding agent runtimes.
- Shared behavior:
AGENTS.md - Claude wrapper:
CLAUDE.md - Codex wrapper:
CODEX.md - Pi wrapper:
PI.md - OpenClaw wrapper:
OPENCLAW.md - Canonical runtime skills:
.agents/skills/*/SKILL.md - Skills in this repo follow the Agent Skills open standard.
- This repo uses skills with progressive disclosure to manage context efficiently: agents begin with each skill's metadata (
name,description, file path, plusagents/openai.yaml), and load fullSKILL.mdinstructions only when a skill is selected. Canonical skills live in.agents/skills/, with bridge paths for Claude, Pi, and OpenClaw. - Optional subagents are supported when the runtime provides agent delegation features (not required for core repo operation).
- Claude bridge path:
.claude/skills -> ../.agents/skills(symlink) - Pi bridge: configure Pi to point to this repo and use
.agents/skills/as its skill source - OpenClaw bridge: create
skills -> .agents/skillssymlink (or load.agents/skillsvia OpenClaw config)
Bridge bootstrap (run once from repo root):
mkdir -p .claude
ln -sfn ../.agents/skills .claude/skills
ln -sfn .agents/skills skillsFor Codex/OpenAI-style routing metadata, this repo includes:
.agents/skills/<skill>/agents/openai.yaml(Claude, Pi, and OpenClaw primarily useSKILL.mdand do not require this file format.)
You can run Personal OS with Pi using a local/offline model backend (for example llama.cpp) or a hosted endpoint. For full setup instructions (server launch, ~/.pi/agent/models.json, and runtime configuration), see Pi Agent Setup.
personal-os/
├── AGENTS.md # AI agent instructions (the brain)
├── GOALS.md # Your goals and priorities
├── BACKLOG.md # Quick capture inbox
├── Tasks/ # Your active work
├── Knowledge/ # Your notes and docs
├── Resources/ # Voice samples, templates, references
├── Workflows/ # Daily + Product & Strategy workflows
├── .agents/skills/ # Canonical Codex/OpenAI skill packs
├── Evals/ # Session reviews
├── Tutorials/ # Learning guides
└── System/ # MCP server, templates, integrations
Semantics by location: Tasks/**/*.md = actionable work, Knowledge/**/*.md = reference context.
AGENTS.md → Instructions layer (how AI behaves)
GOALS.md → Priority layer (what matters)
Tasks/**/*.md → State layer (current work)
Knowledge/**/*.md → Context layer (reference)
.agents/skills/* → Capability layer (how the agent executes specialized workflows)
Personal operating data stays local (gitignored):
Tasks/- your workKnowledge/- your notesResources/- your samplesBACKLOG.md- your inbox
Some top-level configuration files (AGENTS.md, GOALS.md, CLAUDE.md, CODEX.md, PI.md, OPENCLAW.md, docs) are version controlled by design. Treat GOALS.md as potentially sensitive and review content before publishing a public repo.
This repo includes structural, behavioral, routing, and memory-impact evals.
Run:
python scripts/validate_skills.py
python scripts/validate_skill_eval_cases.py
python scripts/run_skill_evals.py --provider fixture
python scripts/run_routing_evals.py
python scripts/run_memory_impact_evals.pyOptional live-model run (OpenAI-compatible endpoint, local or remote):
python scripts/run_skill_evals.py --provider openai --model your-model-idOutputs are written to:
Evals/skills/results/Evals/memory/results/
Use these evals as a regression gate when updating .agents/skills/.
Personal OS follows four operating patterns:
- Skills: versioned procedures in
.agents/skills/*/SKILL.md - Shell execution: run real tasks in terminal environments and produce artifacts
- Compaction-aware workflows: structure long runs to preserve continuity
- Verification-first completion: require fresh evidence before claiming work is done
Security defaults:
- Keep network access minimal and allowlist-based
- Treat tool output as untrusted input
- Use explicit review boundaries for generated artifacts
- File Format: Markdown with YAML frontmatter
- Agent Runtimes: Claude Code, Codex, Pi, OpenClaw, Cursor, and similar coding agent runtimes
- Optional Integrations: MCP (Slack, Linear, Google Calendar, Atlassian, Granola)
- Version Control: Git
Issues and PRs welcome.