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-model-extraction-attacks/references/api-reference.md

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+# Model Extraction Detection — API / Library Reference
+
+## Libraries
+
+| Library | Install | Purpose |
+|---------|---------|---------|
+| adversarial-robustness-toolbox | `pip install adversarial-robustness-toolbox` | Extraction, inversion, and membership-inference attacks + defenses |
+| scikit-learn | `pip install scikit-learn` | Surrogate / attack model training |
+| numpy | `pip install numpy` | Confidence-vector math, perturbation |
+
+## ART Extraction Attacks (`art.attacks.extraction`)
+
+| Class | Key params | Purpose |
+|-------|-----------|---------|
+| `KnockoffNets` | `nb_stolen`, `batch_size_query`, `nb_epochs`, `sampling_strategy` | Train surrogate from black-box queries (Knockoff Nets) |
+| `CopycatCNN` | `nb_stolen`, `batch_size_fit`, `batch_size_query` | Copycat surrogate extraction for neural nets |
+| `attack.extract(x, thief_classifier=...)` | — | Run extraction; returns trained surrogate classifier |
+
+## ART Inference Attacks (`art.attacks.inference.membership_inference`)
+
+| Class | Key methods | Purpose |
+|-------|-------------|---------|
+| `MembershipInferenceBlackBox` | `.fit(...)`, `.infer(x, y)` | Black-box membership inference (AML.T0024.000) |
+| `MembershipInferenceBlackBoxRuleBased` | `.infer(x, y)` | Rule-based MIA baseline (no shadow training) |
+
+## ART Defenses (postprocessors)
+
+| Class | Purpose |
+|-------|---------|
+| `art.defences.postprocessor.ReverseSigmoid` | Perturb output probabilities to hinder extraction |
+| `art.defences.postprocessor.Rounded` | Round confidence values to reduce leaked precision |
+| `art.defences.postprocessor.HighConfidence` | Suppress low-confidence outputs |
+
+## Estimator Wrappers
+
+| Class | Purpose |
+|-------|---------|
+| `art.estimators.classification.SklearnClassifier` | Wrap a scikit-learn model as an ART victim |
+| `art.estimators.classification.KerasClassifier` / `PyTorchClassifier` | Wrap DL models |
+
+## Detection Signals (custom)
+
+| Signal | Heuristic |
+|--------|-----------|
+| Query volume | Queries/principal/window above baseline |
+| Unique-input ratio | `unique(input_hash)/queries` → ~1.0 |
+| Confidence-request ratio | Fraction of calls demanding full probability vectors |
+
+## External References
+
+- ART docs: https://adversarial-robustness-toolbox.readthedocs.io/
+- MITRE ATLAS AML.T0024: https://atlas.mitre.org/techniques/AML.T0024

skills/detecting-model-extraction-attacks/references/standards.md

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+# Standards and References — Detecting Model Extraction Attacks
+
+## MITRE ATLAS Techniques
+
+| ID | Name | Tactic | Rationale |
+|----|------|--------|-----------|
+| AML.T0024 | Exfiltration via AI Inference API | Exfiltration | Parent technique: abusing the inference API to steal model value or training data. |
+| AML.T0024.000 | Infer Training Data Membership | Exfiltration | Membership inference — determine if a record was in the training set (privacy leak). |
+| AML.T0024.001 | Invert AI Model | Exfiltration | Model inversion — reconstruct training inputs from confidence scores. |
+| AML.T0024.002 | Extract ML Model | Exfiltration | Model stealing — train a surrogate from query/response pairs to clone the model. |
+
+## NIST AI RMF
+
+| ID | Function | Rationale |
+|----|----------|-----------|
+| MEASURE-2.6 | AI system security and resilience are evaluated and documented | Extraction/inference testing measures and documents the model's resilience to inference-API abuse. |
+
+## Official Resources
+
+- MITRE ATLAS AML.T0024: https://atlas.mitre.org/techniques/AML.T0024
+- MITRE ATLAS Matrix: https://atlas.mitre.org/matrices/ATLAS
+- Adversarial Robustness Toolbox (Trusted-AI): https://github.com/Trusted-AI/adversarial-robustness-toolbox
+- NIST AI Risk Management Framework: https://www.nist.gov/itl/ai-risk-management-framework
+
+## Key Research
+
+- Tramèr et al., "Stealing Machine Learning Models via Prediction APIs" (USENIX Security 2016)
+- Shokri et al., "Membership Inference Attacks Against Machine Learning Models" (IEEE S&P 2017)
+- Orekondy et al., "Knockoff Nets: Stealing Functionality of Black-Box Models" (CVPR 2019)