Add folder anatomy (scripts/agent.py + references/api-reference.md) for 648 cybersecurity skills

Complete skill folder anatomy across all cybersecurity skills:
- scripts/agent.py: 80-150 line Python agents using real libraries (impacket,
  boto3, azure-mgmt-*, kubernetes, pefile, yara, scapy, shodan, stix2, etc.)
- references/api-reference.md: real API documentation with method signatures
- LICENSE: MIT license for all skill folders

skills/generating-threat-intelligence-reports/references/api-reference.md

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+# API Reference: Threat Intelligence Report Generator Agent
+
+## Dependencies
+
+| Library | Version | Purpose |
+|---------|---------|---------|
+| jinja2 | >=3.1 | Template rendering for report generation |
+
+## CLI Usage
+
+```bash
+python scripts/agent.py \
+  --type operational \
+  --data /cases/intel_data.json \
+  --output-dir /cases/reports/ \
+  --output report_meta.json
+```
+
+## Report Types
+
+| Type | Audience | Length | Frequency |
+|------|----------|--------|-----------|
+| strategic | C-suite, board, risk committee | 1-3 pages | Monthly/Quarterly |
+| operational | CISO, security directors, IR leads | 3-8 pages | Weekly |
+| tactical | SOC analysts, threat hunters | 1-2 pages | Daily/as-needed |
+| flash | All security staff | 1 page max | Urgent/as-needed |
+
+## Functions
+
+### `confidence_label(level) -> str`
+Maps confidence levels to ICD 203 language: "high" -> "We assess with high confidence", "medium" -> "We assess", "low" -> "Evidence suggests".
+
+### `render_report(report_type, data) -> str`
+Renders a Jinja2 template with the provided data dict. Sets defaults for date, org, tlp.
+
+### `validate_report_data(report_type, data) -> list`
+Validates required fields per report type. Returns list of error strings.
+
+### `quality_check(rendered) -> list`
+Checks rendered report for: minimum length, TLP marker presence, unqualified confidence statements.
+
+### `generate_report(report_type, data_path, output_dir) -> dict`
+Full pipeline: load JSON data, validate, render template, run quality checks, save Markdown output.
+
+## TLP Levels
+
+| Level | Sharing Scope |
+|-------|---------------|
+| RED | Named recipients only |
+| AMBER+STRICT | Organization only |
+| AMBER | Organization and trusted partners |
+| GREEN | Community-wide (ISAC, sector peers) |
+| CLEAR | Public distribution |
+
+## Input Data Schema (Operational Example)
+
+```json
+{
+  "title": "APT29 Campaign Targeting Financial Sector",
+  "tlp": "AMBER",
+  "org": "Security Operations Center",
+  "executive_summary": ["APT29 actively targeting financial institutions..."],
+  "adversary": {
+    "name": "APT29 / Cozy Bear",
+    "motivation": "Espionage",
+    "sophistication": "Advanced",
+    "target_sectors": ["Financial", "Government"]
+  },
+  "ttps": [{"tactic": "Initial Access", "technique_id": "T1566.001", "name": "Spearphishing", "observed": "2025-03-01"}],
+  "key_judgments": [{"confidence": "high", "statement": "APT29 will continue targeting...", "evidence": "..."}],
+  "recommendations": [{"priority": "Critical", "description": "...", "owner": "SOC", "timeframe": "24h", "details": "..."}],
+  "iocs": [{"type": "domain", "value": "evil[.]com", "context": "C2", "confidence": "high"}]
+}
+```
+
+## Output
+
+The agent produces two files:
+1. `{type}_report_{date}.md` - Rendered Markdown report with TLP headers
+2. `report_meta.json` - Metadata including validation errors and quality issues