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/assessing-vector-and-embedding-weaknesses/references/api-reference.md

@@ -0,0 +1,51 @@
+# API and Command Reference
+
+## sentence-transformers (embedding generation)
+| Call | Purpose |
+|------|---------|
+| `SentenceTransformer("all-MiniLM-L6-v2")` | Load an embedding model (384-dim) |
+| `model.encode([texts])` | Return numpy array of embeddings |
+| `model.encode(text, normalize_embeddings=True)` | L2-normalized vectors (for cosine) |
+
+## scikit-learn similarity
+| Call | Purpose |
+|------|---------|
+| `cosine_similarity(a, b)` | Pairwise cosine similarity matrix |
+
+## Qdrant client (qdrant-client)
+| Call | Purpose |
+|------|---------|
+| `QdrantClient(url="http://localhost:6333")` | Connect |
+| `client.get_collection(name)` | Inspect vector size + distance metric |
+| `client.count(name)` | Corpus size |
+| `client.search(collection_name, query_vector, limit, query_filter)` | k-NN search with optional filter |
+| `client.upsert(name, points=[PointStruct(id, vector, payload)])` | Insert/update points |
+| `Filter(must=[FieldCondition(key, match=MatchValue(value))])` | Metadata filter (tenant isolation) |
+
+## Chroma (chromadb)
+| Call | Purpose |
+|------|---------|
+| `chromadb.Client()` / `PersistentClient(path)` | Connect |
+| `collection.query(query_embeddings=[...], n_results=k, where={...})` | k-NN with metadata filter |
+| `collection.add(ids, embeddings, metadatas, documents)` | Insert |
+
+## Pinecone (pinecone-client)
+| Call | Purpose |
+|------|---------|
+| `Pinecone(api_key=...)` | Connect |
+| `index.query(vector=..., top_k=k, namespace="tenant", filter={...})` | k-NN; namespace = tenant boundary |
+| `index.upsert(vectors=[(id, vec, meta)], namespace=...)` | Insert |
+
+## Assessment metrics
+| Metric | Meaning |
+|--------|---------|
+| Inversion cosine | Similarity between reconstructed candidate and target vector; high = recoverable. |
+| Membership delta | top-1 score(in-corpus query) − top-1 score(control query); large positive = membership leak. |
+| Poison dominance | Fraction of unrelated queries returning the poison chunk in top_k. |
+| Cross-tenant count | Number of foreign-tenant rows returned to a tenant query (should be 0). |
+
+## vec2text (research baseline)
+| Call | Purpose |
+|------|---------|
+| `vec2text.load_pretrained_corrector("gtr-base")` | Load inversion corrector for compatible embedder |
+| `vec2text.invert_embeddings(embeddings, corrector)` | Reconstruct text from embeddings |