Add 55 new skills across 3 new domains + 6 undercovered areas (762 -> 817)

Demand-driven expansion targeting the fastest-growing 2025-2026 threat and
skills categories (ISC2/WEF/CrowdStrike/Mandiant signals):

- AI Security (NEW domain, 12 skills): LLM red-teaming with garak/PyRIT,
  prompt injection (direct/indirect/RAG), MCP tool-poisoning, agentic tool
  invocation, guardrails, model/data poisoning, system-prompt leakage,
  embedding/vector weaknesses, model extraction, continuous red-teaming
- Supply Chain Security (NEW domain, 5 skills): SBOMs, dependency confusion,
  malicious-npm triage, typosquatting, SLSA/Sigstore provenance
- Hardware & Firmware Security (NEW domain, 4 skills): CHIPSEC/UEFI audit,
  Secure Boot bypass, TPM measured-boot attestation, ESP bootkit hunting
- Identity (10): Entra ID/ROADtools, GraphRunner, AADInternals, ADCS/Certipy,
  shadow credentials, coercion, BloodHound CE, device-code phishing, SSO abuse
- Cloud-native (8): Stratus, Pacu, CloudFox, container escape, K8s RBAC,
  Falco, Trivy, kube-bench
- Offensive C2 (6): Sliver, Havoc, NetExec, DPAPI, NTLM relay ESC8, redirectors
- DFIR (6): Hayabusa, Chainsaw, KAPE, Velociraptor, EZ Tools, Plaso
- Backfill (4): OpenCTI, MISP, honeytokens, post-quantum crypto migration

Each skill follows the repo taxonomy (SKILL.md + references/{standards,api-reference}.md
+ scripts/agent.py + LICENSE), with researched real tool commands (no placeholders),
complete frontmatter, and ATT&CK/ATLAS + NIST CSF mappings. Updates README domain
table, skill count, and index.json.

skills/detecting-indirect-prompt-injection/references/api-reference.md

@@ -0,0 +1,50 @@
+# API Reference — Indirect Prompt Injection Detection
+
+## LLM Guard
+
+Install: `pip install llm-guard`
+
+| API | Description |
+|-----|-------------|
+| `from llm_guard.input_scanners import PromptInjection` | Import the injection scanner |
+| `PromptInjection(threshold=0.5, match_type=MatchType.FULL)` | Construct scanner (FULL or SENTENCE match) |
+| `scanner.scan(text)` | Returns `(sanitized_text, is_valid, risk_score)` |
+| `from llm_guard import scan_prompt` | Run multiple scanners over a prompt |
+
+`is_valid == False` indicates an injection was detected.
+
+## Transformers detector models
+
+Install: `pip install transformers torch`
+
+| API | Description |
+|-----|-------------|
+| `pipeline("text-classification", model=...)` | Load a classifier pipeline |
+| `protectai/deberta-v3-base-prompt-injection-v2` | Open prompt-injection classifier (labels: SAFE / INJECTION) |
+| `meta-llama/Llama-Prompt-Guard-2-86M` | Meta jailbreak/injection classifier (gated license) |
+
+## Content extraction
+
+| API | Description |
+|-----|-------------|
+| `BeautifulSoup(html, "html.parser")` | Parse HTML |
+| `soup.find_all(string=lambda t: isinstance(t, Comment))` | Extract HTML comments |
+| `pypdf.PdfReader(path).pages[i].extract_text()` | Extract PDF text |
+| `pytesseract.image_to_string(Image.open(path))` | OCR text from an image |
+| `PIL.Image.open(path)._getexif()` | Read EXIF metadata |
+
+## Normalization helpers
+
+| Technique | Method |
+|-----------|--------|
+| Strip zero-width chars | `str.translate` over U+200B..U+FEFF |
+| Strip Unicode tag chars | filter ord in range 0xE0000-0xE007F |
+| Canonicalize | `unicodedata.normalize("NFKC", text)` |
+| Decode Base64 | `base64.b64decode(token)` |
+| Decode ROT13 | `codecs.decode(text, "rot_13")` |
+
+## External References
+
+- LLM Guard PromptInjection docs: https://llm-guard.com/input_scanners/prompt_injection/
+- Hugging Face transformers pipelines: https://huggingface.co/docs/transformers/main_classes/pipelines
+- pytesseract: https://github.com/madmaze/pytesseract