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

Create skills/[skill-name]/SKILL.md
Add YAML frontmatter: name, description (include trigger phrases)
Write lean main content (<3K words) covering: overview, core concepts, workflow, key principles
Create skills/[skill-name]/references/ for detailed content
Register in skills/skill-rules.json if using activation matching
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, argument-hint (optional)
- > See CONNECTORS.md reference
- ## Workflow with numbered steps
- Input gathering, tool use, output format, follow-up options
No code logic — pure markdown workflow
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

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: 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

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