Agentic-creator-os acos-meta
ACOS self-description and configuration skill. Documents how ACOS works, how to extend it, how to add new skills/commands/agents, and how to debug the hook system. Use when building new ACOS capabilities, understanding the system architecture, or onboarding to ACOS for the first time.
git clone https://github.com/frankxai/agentic-creator-os
T=$(mktemp -d) && git clone --depth=1 https://github.com/frankxai/agentic-creator-os "$T" && mkdir -p ~/.claude/skills && cp -r "$T/.claude/skills/acos-meta" ~/.claude/skills/frankxai-agentic-creator-os-acos-meta && rm -rf "$T"
.claude/skills/acos-meta/SKILL.mdACOS Meta
ACOS describes itself using the same primitives it uses to build everything else. This skill is ACOS about ACOS.
System Architecture
acos-intelligence-system/ ├── .claude-plugin/ │ └── plugin.json ← Plugin manifest (v11.0.0) ├── .mcp.json ← MCP server registry ├── CONNECTORS.md ← Connector category map ├── skills/ ← Domain expertise (subdirs, progressive disclosure) │ ├── [skill-name]/ │ │ ├── SKILL.md ← Lean main file (<3K words) │ │ └── references/ ← Deep content, fetched on demand ├── commands/ ← Slash commands (one .md per command) ├── hooks/ ← Lifecycle automation (SessionStart, Stop, etc.) ├── docs/ ← Strategy documents └── README.md ← Entry point
The 3 Design Principles (Drawn from knowledge-work-plugins)
1. Progressive Disclosure
SKILL.md contains the mental model and workflow skeleton. Details live in
references/. Claude loads the lean summary and fetches references only when needed. This keeps context efficient without sacrificing depth.
2. Connector Agnosticism
Skills reference
~~categories, not vendor names. The .mcp.json maps categories to specific tools. Swap tools without touching skill content.
3. Commands as Workflows
Commands are fully-specified workflows in markdown — trigger, input gathering, decision logic, output structure, follow-up options. No code. Claude interprets and executes.
How ACOS Auto-Routing Works
The
/acos command is the entry point. It routes based on keyword detection:
User request | ├── AI architecture keywords → Technical Architect agent ├── Content/writing keywords → Content Engine ├── Music keywords → Music Producer ├── Visual/image keywords → Visual Creation Council ├── Deploy/build keywords → DevOps Pipeline ├── Research keywords → Deep Research swarm └── Complex/multi-file → Full swarm (5+ agents)
The hook system (
hooks/skill-activation-prompt.sh) enhances routing with pattern matching before Claude processes the request.
Adding a New Skill
- Create
skills/[skill-name]/SKILL.md - Add YAML frontmatter:
,name
(include trigger phrases)description - Write lean main content (<3K words) covering: overview, core concepts, workflow, key principles
- Create
for detailed contentskills/[skill-name]/references/ - Register in
if using activation matchingskills/skill-rules.json - Test: mention trigger phrases and verify activation in session
SKILL.md frontmatter template:
--- name: skill-name description: "One-sentence description. Include trigger phrases like: what actions activate this skill, what topics it covers." ---
Adding a New Command
- Create
commands/[command-name].md - Use the standard command structure:
- YAML frontmatter:
,description
(optional)argument-hint
reference> See CONNECTORS.md
with numbered steps## Workflow- Input gathering, tool use, output format, follow-up options
- YAML frontmatter:
- No code logic — pure markdown workflow
- Test: run
and verify execution/[command-name]
Command frontmatter template:
--- description: What this command does in one sentence argument-hint: "<optional argument description>" ---
Adding a New Agent
- Create
.claude/agents/[agent-name].md - Define: role, capabilities, tools, escalation path
- Reference from orchestration commands or swarm topology
- Test via Task tool:
Task(subagent_type="[agent-name]", prompt="...")
Debugging the Hook System
ACOS has 15 hooks across 6 lifecycle events. When hooks behave unexpectedly:
- Check audit trail:
cat .claude-flow/audit.jsonl | tail -20 - Check circuit breaker state:
cat .claude-flow/circuit-breaker.json - View learning metrics:
cat .claude-flow/metrics/learning-status.json - Run monitor:
(real-time hook dashboard)npm run monitor
Hook event map:
SessionStart → session-start.js + starlight-bridge + todo-continuation restore UserPromptSubmit → skill-activation-prompt.sh PreToolUse → quality-gate + circuit-breaker PostToolUse → post-tool-track.js + audit-trail Stop → stop-finalize.js + todo-continuation save + learning-hooks PreCompact → context preservation
Intelligence Score System
ACOS tracks its own intelligence score across sessions:
| Component | Weight | Measured by |
|---|---|---|
| Skill activation accuracy | 25% | Trajectory success rates |
| Pattern extraction quality | 25% | n-gram count in patterns.json |
| Memory utilization | 20% | Context recovery on session start |
| Hook reliability | 15% | Zero circuit breaker breaks |
| Self-modify safety | 15% | Score delta tracking |
View with
/acos-score.
ACOS × knowledge-work-plugins
ACOS v11 integrates patterns from the knowledge-work-plugins ecosystem:
| Pattern | Source | Applied in ACOS |
|---|---|---|
| Progressive disclosure | knowledge-work-plugins | All new skills use SKILL.md + references/ |
| Plugin manifest | knowledge-work-plugins | |
| Connector agnosticism | knowledge-work-plugins | with placeholders |
| Command workflow format | knowledge-work-plugins | Standardized command structure with input gathering |
| Two-tier memory | productivity plugin | creator-productivity skill |
| Brand voice framework | marketing plugin | brand-voice skill |
ACOS contributes back to knowledge-work-plugins:
plugin — creator-specific domain (content, visual, music)creator/- Pattern: quality gates in visual and content creation
- Pattern: music prompt engineering pipeline
See
references/v11-architecture-decisions.md for the full integration rationale.