# Ancient Costume Mural Reconstruction

> Ancient Costume Reconstruction & Archaeological Textile History harness — production-grade evidence-backed analysis with real-time data aggregation, recognized domain methods, academic research integration, and continuous self-improvement via knowledge crawl pipeline. Use for ANY ancient costume reconstruction, textile archaeology, iconographic analysis, period construction recovery, materials/dy…

- **Type:** Skill
- **Install:** `agentstack add skill-dungnotnull-ancient-costume-mural-reconstruction-agent-skill-ancient-costume-mural-reconstruction-agent-skill`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [dungnotnull](https://agentstack.voostack.com/s/dungnotnull)
- **Installs:** 0
- **Category:** [Web & Browser](https://agentstack.voostack.com/c/web-and-browser)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [dungnotnull](https://github.com/dungnotnull)
- **Source:** https://github.com/dungnotnull/ancient-costume-mural-reconstruction-agent-skill

## Install

```sh
agentstack add skill-dungnotnull-ancient-costume-mural-reconstruction-agent-skill-ancient-costume-mural-reconstruction-agent-skill
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# SKILL.md — Skill Registry & Architecture

## What This Skill Does

This skill transforms Claude into a **Senior Ancient Costume Reconstruction & Archaeological Textile History Specialist**. When users ask about reconstructing garments from murals/statues, identifying historical textiles, analyzing period construction, recovering ancient dye recipes, or producing 3D reconstructions of archaeological dress, this skill:

1. **Clarifies requirements** (object, scope, timeframe, inputs, audience, language)
2. **Fetches authoritative evidence** (museum records, extant textiles, academic sources)
3. **Applies domain methods** (iconography → construction → materials → 3D reconstruction)
4. **Surfaces academic evidence** (Tier-labelled citations from SECOND-KNOWLEDGE-BRAIN)
5. **Delivers risk-disclosed conclusions** (evidence-graded verdict with full disclosure)

**Key capability**: This is NOT just costume design — it's archaeologically-grounded reconstruction that respects evidence hierarchies (extant textiles > iconography > textual > ethnographic) and always discloses limitations before conclusions.

---

## When This Skill Triggers

This skill should trigger for **ANY** user query involving:

### Direct Reconstruction Tasks
- "Reconstruct the [garment] from [mural/cave/tomb]"
- "What did [historical figure] wear in [painting/relief]?"
- "Build a 3D model of [period] costume based on [statue]"

### Analysis & Identification
- "Analyze this textile fragment"
- "Identify the dye used in [archaeological find]"
- "What materials would this [period] garment be made of?"
- "Examine the construction of [garment in artwork]"

### Comparative & Historical
- "Compare Han and Tang dynasty court robes"
- "How did [garment type] change from [period A] to [period B]?"
- "What's the evidence for [costume element] in [culture]?"

### Museum & Collection Queries
- "Find museum records for [accession number]"
- "What extant [garment type] examples survive from [period]?"
- "Show me comparable textiles to [artifact]"

### Materials & Techniques
- "What dyes were available in [period/region]?"
- "How was [fabric type] made in [culture]?"
- "Reconstruct the weave structure of [textile]"

**Even if the user doesn't say "reconstruct"** — if they're asking about historical garments, archaeological textiles, period dress, or museum costume collections, this skill should trigger.

---

## Skill Registry Architecture

### Registration Model

Skills are registered in `assets/skill_manifest.json` with:

```json
{
  "version": "2.0.0",
  "skills": [
    {
      "name": "skill-name",
      "kind": "orchestrator|router|sub-agent",
      "path": "skills/skill-name.md",
      "description": "One-line summary",
      "step": N,
      "inputs_schema": "assets/schemas/xxx.schema.json",
      "outputs_schema": "assets/schemas/yyy.schema.json",
      "tools": ["Tool1", "Tool2"],
      "quality_gates": ["U1", "G1"],
      "routing": {...},
      "fallback_skill": "fallback-name"
    }
  ]
}
```

### Skill Kinds

| Kind | Purpose | Examples |
|------|---------|----------|
| `orchestrator` | Top-level harness, manages workflow, quality gates | `main.md` |
| `router` | Chain-of-thought routing to specialized agents | `sub-router.md` |
| `sub-agent` | Domain-specialized analysis agent | All sub-*.md files |

### Skill Resolution Pipeline

```
User invokes /ancient-costume-mural-reconstruction
    ↓
Load SKILL.md (this file) → skill_manifest.json
    ↓
Resolve execution chain via sub-router
    ↓
Sequential invocation:
  1. sub-gather-requirements (intake)
  2. sub-evidence-collector (data)
  3. sub-router (routing decision)
  4. [Specialized agents per routing plan]
  5. sub-knowledge-updater (academic evidence)
  6. sub-advisor (synthesis → verdict)
    ↓
Quality gate evaluation (U1-U6, G1-G4)
    ↓
Graceful degradation if gates fail
    ↓
Render final report via render_report tool
    ↓
Deliver to user
```

---

## Input/Output JSON Schemas

### Requirements Schema (inputs to Steps 1-2)

```json
{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "title": "RequirementsBundle",
  "type": "object",
  "required": ["object", "period", "scope"],
  "properties": {
    "object": {
      "type": "string",
      "description": "The mural/statue/relief/site or garment being analyzed"
    },
    "period": {
      "type": "string",
      "description": "Historical period/culture (e.g., 'Tang Dynasty', 'Han China')"
    },
    "scope": {
      "type": "string",
      "enum": ["iconography", "construction", "materials", "3d", "comparison", "combined"],
      "description": "Analysis type focus"
    },
    "timeframe": {
      "type": "string",
      "description": "Urgency or deadline constraints"
    },
    "available_inputs": {
      "type": "array",
      "items": {"type": "string"},
      "description": "Available sources (images, documents, artifacts)"
    },
    "target_audience": {
      "type": "string",
      "description": "Who will use this analysis"
    },
    "language": {
      "type": "string",
      "enum": ["en", "vi"],
      "description": "Output language"
    },
    "analysis_type": {
      "type": "string",
      "description": "Specific analysis type if different from scope"
    }
  }
}
```

### Evidence Bundle Schema (outputs from Step 2)

```json
{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "title": "EvidenceBundle",
  "type": "object",
  "properties": {
    "current_collection_records": {
      "type": "array",
      "items": {"$ref": "https://example.org/evidence_item.schema.json"}
    },
    "extant_parallels": {
      "type": "array",
      "items": {"$ref": "https://example.org/evidence_item.schema.json"}
    },
    "authoritative_docs": {
      "type": "array",
      "items": {"$ref": "https://example.org/evidence_item.schema.json"}
    },
    "recent_developments": {
      "type": "array",
      "items": {"$ref": "https://example.org/evidence_item.schema.json"}
    },
    "reference_benchmarks": {
      "type": "object",
      "description": "Period-specific benchmarks from SECOND-KNOWLEDGE-BRAIN"
    }
  }
}
```

### Reconstruction Schema (outputs from Step 4)

```json
{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "title": "ReconstructionReport",
  "type": "object",
  "required": ["iconographic_analysis", "construction_recovery", "materials_analysis", "evidence_hierarchy_applied", "3d_reconstruction"],
  "properties": {
    "iconographic_analysis": {
      "type": "object",
      "description": "Garment form, drape, layering, accessories, status markers"
    },
    "construction_recovery": {
      "type": "object",
      "description": "Pattern, seaming, draping, period techniques"
    },
    "materials_analysis": {
      "type": "object",
      "description": "Fiber identification, dye analysis, textile technology"
    },
    "evidence_hierarchy_applied": {
      "type": "boolean",
      "description": "Whether evidence hierarchy was explicitly applied"
    },
    "3d_reconstruction": {
      "type": "object",
      "description": "Layered 3D model with confidence levels and scenarios"
    },
    "cultural_context": {
      "type": "object",
      "description": "Social/cultural significance of the garment"
    }
  }
}
```

### Verdict Schema (final output)

```json
{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "title": "ReconstructionVerdict",
  "type": "object",
  "required": ["verdict", "evidence_chain", "key_risks", "disclosure"],
  "properties": {
    "verdict": {
      "type": "string",
      "enum": [
        "Evidence-Based Reconstruction",
        "Plausible (interpretive)",
        "Speculative",
        "Inconclusive"
      ]
    },
    "scenarios": {
      "type": "array",
      "description": "Best/base/worst case scenarios"
    },
    "key_risks": {
      "type": "array",
      "minItems": 3,
      "items": {
        "type": "object",
        "properties": {
          "description": {"type": "string"},
          "probability": {"type": "string"},
          "impact": {"type": "string"}
        }
      }
    },
    "evidence_chain": {
      "type": "object",
      "description": "Full evidence chain from sources to conclusion"
    },
    "recommended_actions": {
      "type": "array",
      "items": {"type": "string"}
    },
    "disclosure": {
      "type": "string",
      "description": "MUST appear before the verdict in output"
    }
  }
}
```

---

## Tool Definitions & Execution Handlers

### Tool Registry

All tools are defined in `assets/tool_definitions.json` with:

- `name`: Tool identifier
- `description`: What the tool does
- `category`: "knowledge" | "retrieval" | "analysis" | "validation" | "io" | "utility"
- `input_schema`: Path to JSON schema for inputs
- `output_schema`: Path to JSON schema for outputs
- `handler`: Python function path in `tools/agent_tools.py`
- `timeout_seconds`: Maximum execution time
- `idempotent`: Whether repeated calls produce same result
- `requires_network`: Whether tool needs internet access

### Tool Execution Flow

```
Agent invokes tool by name
    ↓
Load tool definition from tool_definitions.json
    ↓
Validate inputs against input_schema
    ↓
Call handler function in tools/agent_tools.py
    ↓
Enforce timeout (raises TimeoutError if exceeded)
    ↓
Validate outputs against output_schema
    ↓
Return structured result
```

### Available Tools

| Tool | Handler | Category | Description |
|------|---------|----------|-------------|
| `search_knowledge_base` | `search_knowledge_base()` | knowledge | Search SECOND-KNOWLEDGE-BRAIN for Tier-labelled citations |
| `fetch_museum_record` | `fetch_museum_record()` | retrieval | Fetch museum collection record by accession number |
| `build_3d_layer` | `build_3d_layer()` | analysis | Build 3D reconstruction layer with material assignment |
| `queue_crawl_gap` | `queue_crawl_gap()` | knowledge | Queue crawl gap for knowledge pipeline |
| `run_quality_gate` | `run_quality_gate()` | validation | Evaluate quality gate (U1-U6, G1-G4) |
| `validate_verdict` | `validate_verdict()` | validation | Validate verdict against schema |
| `render_report` | `render_report()` | io | Render final report from components |
| `emit_event` | `emit_event()` | utility | Emit lifecycle event to hooks bus |

---

## Quality Gates (Validation Rules)

### Universal Gates (U1-U6)

Apply to **ALL** harness outputs:

| Gate | Check | Auto-Fix |
|------|-------|----------|
| U1 | ≥3 sources cited, ≥1 Tier 1 (academic/authoritative) | Add 2+ sources, include Tier 1 |
| U2 | Disclosure BEFORE recommendation | Move disclosure to appear first |
| U3 | Evidence hierarchy stated per source | Add Tier labels (Tier 1-4) |
| U4 | Language matches user preference | Ensure all text in declared language |
| U5 | Output follows template | Add missing sections |
| U6 | All claims traceable to source OR flagged as judgment | Add citations or flag as judgment |

### Domain Gates (G1-G4)

Apply to ancient costume reconstruction specifically:

| Gate | Check | Auto-Fix |
|------|-------|----------|
| G1 | Iconographic analysis completed | Perform visual source analysis |
| G2 | Construction & materials/dyes recovered | Add period construction & materials |
| G3 | Evidence hierarchy applied per claim | State hierarchy (extant > iconographic > textual > ethnographic) |
| G4 | 3D reconstruction produced with confidence | Build layered 3D model with H/M/L confidence |

### Gate Enforcement Logic

```
For each gate:
  1. Run gate check against payload
  2. If PASS → continue
  3. If FAIL → apply auto-fix (if enabled) → retry
  4. If still FAIL after 2 retries → flag limitation → continue
  5. If enforcing mode (default) → fail the step → escalate degradation level
```

---

## Graceful Degradation Strategy

### Degradation Levels

| Level | Condition | Behavior |
|-------|-----------|----------|
| 0 | All gates pass | Full execution, no limitations |
| 1 | Non-critical gates fail | Add limitation banner, continue |
| 2 | Critical gates fail, fallback available | Use fallback agent, add limitation |
| 3 | Major limitations | Best-effort analysis, heavy disclosure |
| 4 | Severe failures | Minimum viable output, Inconclusive verdict |

### Degradation Banners

When degradation occurs, output MUST include:

```markdown
---
**LIMITATION**: This analysis has [data/method] limitations.
[Specific limitation description]
Confidence: [Low/Moderate/High]
Verdict may be affected: [Yes/No]
---
```

---

## Hooks System (Lifecycle Events)

### Event Types

Events are emitted via `emit_event` tool:

| Event Type | When | Payload |
|------------|------|---------|
| `routing_decision` | After sub-router completes | execution_chain, rationale, fallback |
| `quality_gate_pass` | After each gate passes | gate_id, payload |
| `quality_gate_fail` | After gate fails (with auto-fix) | gate_id, failure_reason, auto_fix |
| `degradation` | When degradation level increases | old_level, new_level, reason |
| `step_complete` | After each sub-skill completes | skill_name, outputs, duration_ms |
| `analysis_complete` | After full harness completes | verdict, total_duration_ms |

### Hook Handlers

Hooks are registered in `assets/hooks.json` and can:

- Log structured events
- Trigger notifications
- Update metrics
- Call external services
- Modify execution flow (rare, requires explicit enable)

---

## Error Handling & Recovery

### Error Categories

| Error Type | Recovery Strategy |
|------------|------------------|
| `ValidationError` | Return validation error, don't proceed |
| `TimeoutError` | Return timeout, suggest simplification |
| `ToolError` | Log error, try fallback, add limitation |
| `AgentError` (LLM failure) | Retry with exponential backoff (max 3) |
| `ConfigError` | Return configuration error, don't proceed |

### Retry Policy

```
Retry attempt 1: immediate
Retry attempt 2: 1.5s delay
Retry attempt 3: 3s delay
After 3 failures: flag as limitation, continue or fail
```

---

## Configuration Management

### Config Resolution Order

1. Explicit keyword arguments to `load_config()`
2. Environment variables (`ACMR_*` prefix)
3. Config file (`config/config.json`, `config/config.yaml`, or `$ACMR_CONFIG`)
4. Built-in defaults (dataclass defaults)

### Key Config Sections

- `llm`: Model parameters, temperature, token budgets, retry policy
- `harness`: Language policy, gate enforcement, degradation behavior
- `knowledge_pipeline`: Crawl keywords, sources, schedules, limits
- `features`: Feature flags for incremental rollout
- `logging`: Log level, output targets, rotation settings

### Environment Variables

```bash
ACMR_CONFIG=/path/to/config.yaml
ACMR_LLM__MODEL=claude-sonnet-4-5
ACMR_HARNESS__DEFAULT_LANGUAGE=vi
ACMR_FEATURES__ENABLE_KNOWLEDGE_CRAWL=true
```

---

## Knowledge Pipeline Integration

### SECOND-KNOWLEDGE-BRAIN.md Structure

```markdown
# Core Methods
[Domain methodology entries]

# Key Papers & References
[Tier 1/2 academic papers with DOIs]

# State of the Art
[Current research directions]

# Data Sources
[Museum collections, databases]

# Self-Update Protocol
[Crawl configuration, last updated]

# Update Log
[Append-only log of new entries]
```

### Crawl Pipeline (`tools/knowledge_updater.py`)

- **Sources**: ArXiv (cs.GR, cs.CV, cs.AI), Semantic Scholar, Crossref, RSS feeds
- **Dedup**: SHA-256 of normalized DOI
- **Scoring**: Recency (0.4) + Keyword relevance (0.4) + Citation count (0.2)
- **Schedule**: Weekly academic (Mondays 8:00), Daily news (Daily 7:00)
- **Safety**: Backup-before-write, idempotent append, graceful degradation

---

## Language Support (English/Vietnamese)

### Language Detection

Pre-flight step detects language from input:
- **Vietnamese**: Diacritics (à, á, ả, ã, ạ, ă, â, đ, è, é, ê, ì, í, ò, ó, ô, ơ, ù, ú, ư, ý)
- **English**: Default
- **Other**: Default to English, ask user to confirm

### Translation Table

All output templates support both languages:

| English | Tiếng Việt |
|---------|--

…

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [dungnotnull](https://github.com/dungnotnull)
- **Source:** [dungnotnull/ancient-costume-mural-reconstruction-agent-skill](https://github.com/dungnotnull/ancient-costume-mural-reconstruction-agent-skill)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-dungnotnull-ancient-costume-mural-reconstruction-agent-skill-ancient-costume-mural-reconstruction-agent-skill
- Seller: https://agentstack.voostack.com/s/dungnotnull
- Browse the marketplace: https://agentstack.voostack.com/browse

---
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