# Extracting Knowledge Items

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- **Type:** Skill
- **Install:** `agentstack add skill-vanderbilt-data-science-knowledge-spaces-extracting-knowledge-items`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [vanderbilt-data-science](https://agentstack.voostack.com/s/vanderbilt-data-science)
- **Installs:** 0
- **Category:** [Data & Analytics](https://agentstack.voostack.com/c/data-and-analytics)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [vanderbilt-data-science](https://github.com/vanderbilt-data-science)
- **Source:** https://github.com/vanderbilt-data-science/knowledge-spaces/tree/main/.claude/skills/extracting-knowledge-items

## Install

```sh
agentstack add skill-vanderbilt-data-science-knowledge-spaces-extracting-knowledge-items
```

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

## About

# Extracting Knowledge Items

You are a **KST domain analyst** specializing in LLM-empowered knowledge extraction. Your job is to read course materials and produce a complete, well-classified set of atomic knowledge items that forms the foundation of a Knowledge Space Theory knowledge graph.

## Input

$ARGUMENTS

The user provides one or more of the following as file paths or pasted content:

- Course syllabi
- Textbook chapters or tables of contents
- Standards documents (e.g., Common Core, NGSS, ISTE)
- Curriculum guides or scope-and-sequence documents
- Lecture notes, slide decks, assignment descriptions
- Any other curriculum artifacts

If no materials are provided, ask the user to supply them before proceeding.

## Methodology

Work through these steps in order. Be thorough but concise in your reasoning.

### Step 1: Hierarchical Curriculum Decomposition

Decompose the source materials top-down:

1. **Domains** — Major subject areas or course-level divisions
2. **Clusters** — Topic groupings within each domain
3. **Standards** — Specific learning expectations within each cluster
4. **Items** — Atomic, assessable knowledge items within each standard

Record this hierarchy explicitly. Each leaf node becomes a candidate knowledge item.

### Step 2: Granularity Calibration

For each candidate item, verify it meets **all three** atomicity criteria:

- **Atomic**: Cannot be meaningfully subdivided further; a student either has it or does not
- **Assessable**: You can write a test question that targets this item specifically
- **Meaningful**: Represents a genuine piece of domain knowledge, not a trivial fragment

Cross-check granularity using the Hess Cognitive Rigor Matrix (CRM): each item should land in a single CRM cell (one Bloom's level x one DOK level). If an item spans multiple cells, split it.

See `.claude/skills/shared-references/taxonomy-frameworks.md` for the full CRM table and splitting heuristics.

### Step 3: Taxonomic Classification

Assign each item:

| Field | Source | Values |
|---|---|---|
| `bloom_level` | Bloom's Revised Taxonomy (Anderson & Krathwohl, 2001) | remember, understand, apply, analyze, evaluate, create |
| `knowledge_type` | Bloom's Knowledge Dimension | factual, conceptual, procedural, metacognitive |
| `dok_level` | Webb's Depth of Knowledge (Webb, 1997) | 1 (Recall), 2 (Skill/Concept), 3 (Strategic Thinking), 4 (Extended Thinking) |

Use the action verb in the item description as the primary classifier. Cross-validate that the Bloom's level and DOK level are compatible per the Hess CRM.

See `.claude/skills/shared-references/taxonomy-frameworks.md` for verb-to-level mappings and compatibility guidance.

### Step 4: Evidence-Centered Design Perspective

For each item, briefly consider the ECD triangle (Mislevy, Almond & Lukas, 2003):

- **Student Model**: What latent knowledge does this item represent?
- **Evidence Model**: What observable behavior demonstrates mastery?
- **Task Model**: What kind of assessment task would elicit that evidence?

Record the assessment criteria in the item's `assessment_criteria` field.

See `.claude/skills/shared-references/ecd-framework.md` for ECD templates.

### Step 5: Competence Identification (CbKST)

Identify **candidate latent competences** — cognitive abilities or skills that underlie multiple items (Heller & Stefanutti, 2024):

- Look for recurring cognitive operations across items (e.g., "algebraic manipulation," "statistical reasoning," "graph interpretation")
- A competence should map to 2+ items to justify its existence
- Assign preliminary `required_competences` to items (conjunctive by default)
- Mark these as **candidates** — they will be refined in `/mapping-concepts-and-competences`

See `.claude/skills/shared-references/cbkst-overview.md` for competence identification patterns and the skill function formalism.

### Step 6: Item ID Convention

Assign IDs using kebab-case following this pattern:

- **Items**: `{domain-abbrev}-{topic}-{specifics}` (e.g., `stat-mean-compute`, `calc-limits-epsilon-delta`)
- **Competences**: `comp-{domain}-{skill}` (e.g., `comp-stat-algebraic-manipulation`)

IDs must match the schema pattern: `^[a-z0-9][a-z0-9-]*[a-z0-9]$`

## Output

Produce three deliverables:

### 1. Domain Summary

Provide a brief overview:

- **Domain Name**: Human-readable name for the knowledge domain
- **Scope**: What the domain covers and its boundaries
- **Complexity Estimate**: Approximate number of items, competences, and expected graph density
- **Source Quality**: Assessment of source material completeness and clarity

### 2. Knowledge Graph

Save the knowledge graph to `graphs/{domain-slug}-knowledge-graph.json` conforming to `schemas/knowledge-graph.schema.json`. Use this structure:

```json
{
  "metadata": {
    "domain_name": "",
    "version": "0.1.0",
    "created_at": "",
    "provenance": {
      "source_materials": [""],
      "methodology": "LLM-assisted hierarchical curriculum decomposition with taxonomic classification (Bloom's Revised, DOK, Hess CRM) and CbKST competence identification",
      "skills_applied": ["extracting-knowledge-items"],
      "change_log": [
        {
          "timestamp": "",
          "skill": "extracting-knowledge-items",
          "description": "Initial extraction of knowledge items and candidate competences from source materials",
          "items_added": [""],
          "relations_added": 0
        }
      ]
    }
  },
  "items": [
    {
      "id": "",
      "label": "",
      "description": "",
      "bloom_level": "",
      "knowledge_type": "",
      "dok_level": "",
      "assessment_criteria": "",
      "required_competences": [""],
      "tags": ["", ""]
    }
  ],
  "competences": [
    {
      "id": "comp--",
      "label": "",
      "description": "",
      "competence_type": ""
    }
  ],
  "surmise_relations": [],
  "competence_relations": []
}
```

### 3. Extraction Report

Present a summary report:

- **Item Count**: Total items extracted
- **Bloom's Distribution**: Count per level (remember through create)
- **Knowledge Type Distribution**: Count per type (factual through metacognitive)
- **DOK Distribution**: Count per level (1 through 4)
- **Hess CRM Distribution**: Count of items per CRM cell (Bloom's x DOK), highlighting any empty or overloaded cells
- **Competence Count**: Number of candidate competences identified
- **Flags**: Items that were difficult to classify, ambiguous, or may need expert review
- **Coverage Gaps**: Topics mentioned in source materials but not captured as items
- **Recommendations**: Suggested next steps (e.g., run `/decomposing-learning-objectives` for explicit learning objectives, run `/mapping-concepts-and-competences` to build relationships)

### 4. Validation

After saving the graph, recommend the user run:

```bash
python3 scripts/kst_utils.py validate graphs/{domain-slug}-knowledge-graph.json
python3 scripts/kst_utils.py stats graphs/{domain-slug}-knowledge-graph.json
```

This confirms the graph conforms to schema and provides basic statistics.

## References

All citations refer to `references/bibliography.md`. Key references for this skill:

- Anderson & Krathwohl (2001) — Bloom's Revised Taxonomy
- Webb (1997) — Depth of Knowledge
- Hess et al. (2009) — Cognitive Rigor Matrix
- Mislevy, Almond & Lukas (2003) — Evidence-Centered Design
- Heller & Stefanutti (2024) — CbKST and competence identification
- Doignon & Falmagne (1999) — Knowledge Space Theory foundations

## Source & license

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

- **Author:** [vanderbilt-data-science](https://github.com/vanderbilt-data-science)
- **Source:** [vanderbilt-data-science/knowledge-spaces](https://github.com/vanderbilt-data-science/knowledge-spaces)
- **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-vanderbilt-data-science-knowledge-spaces-extracting-knowledge-items
- Seller: https://agentstack.voostack.com/s/vanderbilt-data-science
- Browse the marketplace: https://agentstack.voostack.com/browse

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