os-init

name	os-init
plugin	agent-agentic-os
description	Trigger: "set up agentic OS", "initialize agent harness", "init my project for AI agents", "where do I put CLAUDE.md", "create my agent environment", "set up persistent memory". Guides users through an interview to understand their use case, then scaffolds the right Agentic OS structure. Use even when the user just asks WHERE to put files.
allowed-tools	Bash, Read, Write

Dependencies

This skill requires Python 3.8+ and standard library only. No external packages needed.

To install this skill's dependencies:

pip-compile ./requirements.in
pip install -r ./requirements.txt

See ./requirements.txt for the dependency lockfile (currently empty — standard library only).

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Agentic OS Init

Bootstrap the Agentic OS / Agent Harness structure into any project. The setup is not one-size-fits-all -- a solo developer using Claude for marketing strategy needs a very different environment than a team using agents to document a legacy system. The interview phase exists to get that right the first time.

There is no official Anthropic "agentic OS" reference implementation. This pattern synthesizes Anthropic's documented features (CLAUDE.md hierarchy, /loop, sub-agents, hooks) with community conventions for persistent memory and context management. Official Anthropic docs:

• CLAUDE.md and memory: https://docs.anthropic.com/en/docs/claude-code/memory
• /loop scheduled tasks: https://docs.anthropic.com/en/docs/claude-code/loop
• Hooks (automation): https://docs.anthropic.com/en/docs/claude-code/hooks
• Sub-agents: https://docs.anthropic.com/en/docs/claude-code/sub-agents
• Claude Code overview: https://docs.anthropic.com/en/docs/claude-code/overview

Execution Flow

Execute these phases in order. Do not skip phases.

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Phase 1: Discovery Interview

Do not assume defaults. Ask the user enough to make smart decisions. Pull answers from the conversation if they are already there -- do not repeat questions already answered.

Ask only what is unclear. Group questions to minimize back-and-forth. Adapt your language to the user's technical confidence level (a plumber opened a terminal for the first time is different from a senior engineer).

Core Questions (always ask these if not already answered)

1. What is this project? What kind of work are you doing here?

   • Is this a software project, a research/writing project, a business workflow?

2. Who is using it? Just you, or a team?

   • Solo: keep it simple. Team: shared CLAUDE.md matters more.

3. What is your main use case for the agent? Pick the closest:

   • Writing / content / marketing / strategy
   • Software development (coding, debugging, reviewing)
   • Research / analysis / documentation
   • Business process automation (workflows, legacy system analysis, etc.)
   • Something else (describe it)

4. What sub-tasks or specialized areas do you need the agent to handle? Examples: "analyze screenshots of legacy screens", "draft blog posts", "review PRs", "document business rules", "manage project status".

5. Do you need scheduled/autonomous work? (e.g., nightly summaries, daily standups, background analysis while you sleep) -- this determines whether to set up /loop and heartbeat.md.

6. How much context do you expect to persist? (light = just a few facts, heavy = full project history, business rules, entity glossary, etc.)

Use-Case-Specific Follow-Ups

After getting the core answers, ask targeted follow-ups based on the use case:

If: legacy system documentation / analysis

• How many screens/modules are you documenting?
• What outputs do you need: business rules, workflow diagrams, requirements docs?
• Are multiple people contributing or is this solo?
• Recommendation: set up sub-agents for screen analysis, business rules capture, and workflow documentation. Add an entities/ folder for business terms glossary.

If: marketing / strategy / communications

• What content types? (blogs, social, strategy docs, competitor analysis?)
• Do you have brand voice guidelines to give the agent?
• Recommendation: set up context/soul.md with brand voice, a content/ folder for work-in-progress, and session logs for capturing working decisions.

If: software development

• What stack? (the agent needs to know your build/test commands)
• Do you use GitHub PRs, CI/CD? Should the agent know about them?
• Recommendation: lean CLAUDE.md with build commands, optional hook for pre-commit.

If: research / analysis

• What is the source material? (papers, URLs, files, screenshots?)
• What is the deliverable? (report, synthesis, presentation?)
• Recommendation: set up a research/ folder, context/memory.md for findings.

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Phase 2: Component Planning

Based on the discovery answers, propose a component plan before running the script. Show the user a simple table of what will be created and what will be skipped.

Example output format:

Here is what I plan to set up for your [use case]:

| Component              | Include? | Why |
|------------------------|----------|-----|
| CLAUDE.md (project)    | YES      | Core kernel for your project conventions |
| context/soul.md        | YES      | Agent personality for your brand voice |
| context/user.md        | YES      | Your working style preferences |
| context/memory.md      | YES      | Persistent facts across sessions |
| context/memory/ logs   | YES      | Dated session logs |
| START_HERE.md          | YES      | Bootstrap prompt for new sessions |
| heartbeat.md + /loop   | NO       | You didn't mention scheduled tasks |
| .claude/agents/        | YES      | Sub-agents for [specific tasks] |
| ~/.claude/CLAUDE.md    | OPTIONAL | Global kernel if you want this across all projects |

Additional folders I recommend for your use case:
- entities/   -- glossary of business terms (legacy docs use case)
- research/   -- source material and findings (research use case)

Get confirmation or adjustments before proceeding to Phase 3.

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Phase 3: Execution

Run the setup script with flags derived from the plan:

# Fallback to current directory if not running inside the plugin manager
PLUGIN_DIR="${CLAUDE_PLUGIN_ROOT:-$(pwd)}"

python "${PLUGIN_DIR}/skills/os-init/scripts/init_agentic_os.py" \
  --target <path> \
  [--global] \
  [--dry-run] \
  [--force]

Flags:

• --target PATH : Project root (default: current directory)
• --global : Also scaffold ~/.claude/CLAUDE.md for the global kernel
• --dry-run : Preview what would be created without writing anything
• --force : Overwrite existing files (show this option only for existing projects)

For existing projects: always run --dry-run first and show the user the preview.

After the script runs, create any use-case-specific additional folders that the interview surfaced (entities/, research/, content/, etc.) but the script does not create automatically.

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Phase 4: Post-Init Guidance

After creating the structure, walk the user through what to fill in, in priority order. Do not just dump a list -- explain WHY each file matters.

Priority 1: CLAUDE.md (the kernel -- this is everything)

CLAUDE.md is the single file that Claude reads at the start of every session. It is your project's kernel. Do not let the user leave it blank.

Prompt them with specific questions based on their use case:

• "What are the most important things Claude should always know about this project?"
• "What commands does Claude need to know to build, test, or run things?"
• "Are there conventions or rules Claude should always follow?"

Priority 2: context/soul.md (identity and tone)

This only matters if the agent needs a persona. For a coding project it is less critical. For marketing/communications it is essential. Ask: "Do you want Claude to have a specific voice or personality in this project?"

Priority 3: context/user.md (your working style)

Help the user fill this in with a few targeted questions:

• "Do you prefer concise responses or detailed explanations?"
• "Are there workflows or shortcuts Claude should know about how you work?"

Priority 4: .gitignore recommendations

Remind the user:

# Add to .gitignore:
CLAUDE.local.md
context/memory/
context/events.jsonl
context/os-state.json
context/.locks/
.claude/
context/memory.md
context/status.md

Keep in git (shared with team):

CLAUDE.md
context/soul.md
context/user.md
context/kernel.py
context/agents.json
heartbeat.md
START_HERE.md

Priority 4b: context/agents.json (kernel permit list)

agents.json controls which agent names are allowed to emit events to the kernel bus. Any agent or hook that calls kernel.py emit_event must be listed here — unlisted agents are rejected (fail-closed). When a new plugin is installed that fires kernel events from hooks, its hook agent name must be added to permitted_agents.

Standard entries (from the runtime template):

{
  "schema_version": "1.0",
  "permitted_agents": [
    "agentic-os-setup",
    "Triple-Loop Retrospective",
    "os-memory-manager",
    "os-health-check",
    "os-clean-locks",
    "os-eval-runner",
    "system"
  ]
}

When installing a plugin that fires hook events, check the plugin's hooks/ directory for the --agent value passed to emit_event and add it to the list. Missing entries cause silent event rejection (or, on unpatched kernels, fail-open — a security risk).

Priority 5: Install skills (optional)

If more specialized skills are required for this environment, see the central installation guide:

👉 INSTALL.md

[!TIP] Avoid File Duplication: When installing local/development plugins, ensure they are linked as Symbolic Links rather than deep copied (verify if the skill installer does this, or use ln -s).

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Phase 5: Self-Assessment Survey (MANDATORY)

After every init run, complete the Post-Run Self-Assessment Survey (references/memory/post_run_survey.md). Init sessions reveal what is confusing about onboarding — this is critical signal for improving the skill and the OS.

Count-Based Signals: How many times did the user need clarification? How many interview questions needed rephrasing? How many setup steps required re-explanation?

Qualitative Friction:

1. Which interview question generated the most confusion or unexpected answers?
2. Which component (CLAUDE.md, agents.json, context/, hooks) was hardest to explain?
3. Which decision (include/exclude) was hardest to justify to the user?
4. What one addition to the init templates would reduce friction next time?

Improvement Recommendation: What one change to the init skill, interview flow, or scaffold templates should be tested before the next run?

Save to: ${CLAUDE_PROJECT_DIR}/context/memory/retrospectives/survey_[YYYYMMDD]_[HHMM]_os-init.md

Emit survey completion:

python context/kernel.py emit_event --agent agentic-os-setup \
  --type learning --action survey_completed \
  --summary "retrospectives/survey_[DATE]_[TIME]_os-init.md"

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Reference Materials

For the agent running this skill:

• Full Agentic OS pattern explained -> os-guide skill
• Memory write/promote/archive decisions -> os-memory-manager skill
• Project setup reference -> references/operations/project-setup-guide.md