Results for “ai-maturity”

Expert CMMC 2.0 (Cybersecurity Maturity Model Certification) advisor for US defense contractors and subcontractors in the Defense Industrial Base (DIB). Use this skill whenever a user asks about CMMC 2.0, CMMC Level 1, Level 2, or Level 3, DoD cybersecurity compliance, NIST SP 800-171, CUI (Controlled Unclassified Information) protection, System Security Plan (SSP), Plan of Action & Milestones (POA&M), C3PAO assessments, DIBCAC audits, self-assessment, SPRS score, or any requirement under DFARS 252.204-7012 or 7021. Also trigger for: "CMMC gap analysis", "CMMC readiness", "FCI protection", "CUI scoping", "CMMC practices", "DoD contract cybersecurity", "defense supply chain security", or "prime contractor flow-down requirements".

Run an AI-readiness assessment on the current repository and generate a static HTML dashboard at reports/index.html.

Implements AI safety guardrails including input validation, output filtering, robustness testing, human oversight, and monitoring to prevent harmful outputs and ensure system reliability.

Implements AI transparency requirements under EU AI Act Arts. 13-14 and GDPR Arts. 13-14. Covers user notification of AI interaction, system capability disclosure, limitation documentation, and meaningful information about automated logic. Keywords: AI transparency, EU AI Act, GDPR notification, explainability, automated decision.

Map which elements of an assignment benefit from AI assistance vs. which AI use undermines. Use when redesigning tasks for AI-age classrooms or setting defensible AI use policies for specific assignments.

Turns a mature product plan into a rough implementation plan with high-level steps, maturity and verification marks, and a risks section.

After any AI-generated explanation, require the learner to identify one place it could be wrong, one thing to check, and one source to consult. Builds epistemic vigilance — treats AI output as a claim to evaluate, not truth to absorb.

Classifies AI systems under the EU AI Act's risk tiers and maps compliance obligations for conformity, transparency, and human oversight.

AI safety and responsible AI practices

Assess AI/ML systems for prompt injection, jailbreak vulnerabilities, model inversion risk, data poisoning exposure, and agent tool abuse, with MITRE ATLAS mapping and guardrail recommendations.

当个人希望提升自身在职业或社会中的不可替代性，以应对环境变化和不确定性时

Expert strategy advisor for products built on LLMs or agents — not general product strategy (see `product-strategy` for that). Use this — proactively and without waiting to be asked — whenever choosing where to apply AI in a product, deciding between RAG and fine-tuning, designing how much autonomy an AI feature should have, evaluating whether an AI feature is actually defensible, or deciding whether to add AI to a feature at all. Also triggers for: "should this be an agent or a simple LLM call", "how much autonomy should this feature have", "RAG vs fine-tuning", "is this AI feature defensible", "our AI feature keeps hallucinating and users don't trust it", "should we build this with AI or just ship it deterministic", "AI product wedge", "what happens to this feature when the models get better", "human-in-the-loop design for AI features". Produces a decision-focused brief: the wedge, the architecture choice, the autonomy level, and the defensibility bet — each with an explicit trade-off.

Manages AI model retention and machine unlearning requirements. Covers training data deletion verification, model versioning for compliance, machine unlearning techniques (SISA, gradient-based), and retraining triggers. Keywords: AI retention, machine unlearning, model versioning, training data deletion, retraining, storage limitation.

Analyzes game AI systems in a codebase, covering behavior trees, finite state machines, GOAP, utility AI, pathfinding, steering, perception, difficulty adaptation, NPC dialogue, and AI debugging tools for Unity, Unreal, and Godot projects.

For analysis/task/review agents using a Professional Skill on models, RAG, agents, evaluation, or safety; not for work without AI decision impact.

Audit a codebase, UI, or copy for machine-generated tells across prose, visual/UI, code, and structure/IA, then produce a phased de-slop burndown. Use when the user says "feels AI-generated", "looks like AI slop", "reads like ChatGPT", "feels generic/soulless", or wants an authenticity/voice pass before launch.

Data-Centric Artificial Intelligence: A Survey

Design a Funhouse Mirror activity where students use their own domain expertise to detect AI distortions, omissions, and overconfidence. Use when students know a subject well enough to evaluate AI claims about it.

Benchmark AI skill effectiveness by measuring implementation quality against legacy constraints.

AI Manipulation Defense System with self-learning prompt injection detection and adaptive mitigation

Guides the implementation of ethical AI principles, including fairness auditing, bias mitigation, explainability, accountability, and privacy protection in machine learning systems.

AI Safety Specialist IA — Expert en sécurité IA (alignment, guardrails, filtrage de contenu, test de biais, red teaming)

Assesses codebase maturity using Trail of Bits' 9-category framework, producing a professional scorecard with evidence-based ratings and actionable recommendations.

ai-module-development

Covers AI, machine learning, LLMs, prompt engineering, and blockchain development with code examples and best practices for building AI applications and smart contracts.

Plan, execute, document, and retest authorized security assessments of AI agents and multi-agent workflows using safe adversarial cases, synthetic identities, canaries, and evidence-based findings. Use when defining red-team rules of engagement, assessing prompt injection or excessive agency, testing tool and identity boundaries, evaluating memory or cross-agent attacks, scoring a campaign, or verifying remediation in an approved environment.

Generates optimized prompts for AI video and image generation across multiple platforms, using cinematic styles and behavioral frameworks for commercial outcomes.

Train AI models to be harmless through self-critique and AI feedback using a set of constitutional principles, without requiring human labels for harmful outputs.

Generates high-quality prompts for AI image, video, and music generation platforms, with optional browser automation to submit them to target sites.

Prevents AI-introduced regressions with sandbox-mode API testing, automated bug-check workflows, and patterns that catch blind spots where the same model writes and reviews code.

Execute and analyze AI Data Poisoning attacks. By subtly injecting malicious or targeted misinformation into an LLM's training or fine-tuning dataset, an attacker can covertly manipulate the model's future outputs, implant backdoors, or enforce biases without altering the model architecture.

Mathematical AI interpretability with sheaf cohomology, spectral analysis, causal inference, and hallucination prevention

Expert en médias avancé (content strategy, distribution, monetization, AI generation, DZ context)

Conduct a maturity assessment against the NIST Cybersecurity Framework (CSF) 2.0, using Implementation Tiers to measure organizational cybersecurity posture and create improvement roadmaps.

Guides AI agents to apply advanced mathematical and probabilistic techniques (Bloom filters, HyperLogLog, FFT, etc.) for large-scale data problems where classical algorithms are optimal but math offers better asymptotic bounds.