acos-meta

ACOS 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

1. Create skills/[skill-name]/SKILL.md
2. Add YAML frontmatter: name, description (include trigger phrases)
3. Write lean main content (<3K words) covering: overview, core concepts, workflow, key principles
4. Create skills/[skill-name]/references/ for detailed content
5. Register in skills/skill-rules.json if using activation matching
6. 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

1. Create commands/[command-name].md
2. Use the standard command structure:
   • YAML frontmatter: description, argument-hint (optional)
   • > See CONNECTORS.md reference
   • ## Workflow with numbered steps
   • Input gathering, tool use, output format, follow-up options
3. No code logic — pure markdown workflow
4. Test: run /[command-name] and verify execution

Command frontmatter template:

---
description: What this command does in one sentence
argument-hint: "<optional argument description>"
---

Adding a New Agent

1. Create .claude/agents/[agent-name].md
2. Define: role, capabilities, tools, escalation path
3. Reference from orchestration commands or swarm topology
4. 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:

1. Check audit trail: cat .claude-flow/audit.jsonl | tail -20
2. Check circuit breaker state: cat .claude-flow/circuit-breaker.json
3. View learning metrics: cat .claude-flow/metrics/learning-status.json
4. Run monitor: npm run monitor (real-time hook dashboard)

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	.claude-plugin/plugin.json
Connector agnosticism	knowledge-work-plugins	CONNECTORS.md with ~~category 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:

• creator/ plugin — creator-specific domain (content, visual, music)
• Pattern: quality gates in visual and content creation
• Pattern: music prompt engineering pipeline

See references/v11-architecture-decisions.md for the full integration rationale.

Horizontal Substrates (composable into every pillar)

These are NOT pillars — they are reusable substrates that every pillar can consume. Each ships as its own OSS repo with its own contracts.

Substrate	OSS repo	Purpose	Composed by
Starlight Intelligence System (SIS)	frankxai/Starlight-Intelligence-System	9-layer governance protocol + sovereignty clause + file-contract attestation	Every pillar's substrate-tier decisions
Library OS	frankxai/library-os	Book intelligence: quotes, chapters, related-reading, JSON-LD per book hub	/library surface; Book pillar
Visual Intelligence System (VIS)	(FrankX-internal)	9-platform persona matrix; NB2 / Higgsfield / HyperFrames 3-tier asset stack	Visual pillar; Content Ops Pillar 1
Prompt Hub	frankxai/prompt-engine + frankxai/prompt-library	13-agent prompt-engineering team + Library-of-Alexandria corpus + IFS Psyche layer	Every pillar's system-prompt design + /po + /superintelligence
Second Brain OS	frankxai/second-brain-os	Local-first cross-session memory (parts, profiles, reflections); v0.1.0	Prompt Hub's Cartographer + Psychometrist for recall

How to extend with a new substrate

1. Author master spec at docs/superpowers/specs/YYYY-MM-DD-<name>-design.md.
2. Pressure-test with /starlight-board (per the board-before-tag invariant).
3. Build agent team + skill + command in .claude/.
4. Extract as standalone repo frankxai/<name> under MIT (mirror Library OS / Prompt Hub pattern).
5. Register here (add row to the table above).

Prompt Hub composition specifics

@prompt-conductor (Opus composer, top-level entry) routes to 12 specialists via 8 canonical flows:

• flow-design / flow-optimize / flow-evaluate — builds, refines, scores prompts
• flow-harvest / flow-curate — imports OSS patterns, maintains library
• flow-introspect / flow-profile — IFS Cartographer + psychometric instruments (maps not unburdens; crisis-routing-as-code at lib/prompt-hub/crisis-routing.ts)
• flow-knowledge-base — designs RAG ingestion + retrieval prompt pairs

Red Team gates every publish flow. Voice gate (lib/voice/frankx-voice.ts) checks every output before return.

Adding a new lab specialist when a new model family drops: author .claude/agents/prompt-<lab>-specialist.md, add to AgentName + Lane types in lib/prompt-hub/types.ts, add to Conductor's lab-specialist selection table, register <lab> row in repos/prompt-library/taxonomy/lanes.yaml. No new repo, no new command — the Hub absorbs new labs through specialist addition only.

Master spec: docs/superpowers/specs/2026-05-13-prompt-hub-design.md.