Results for “individuation”

Implement the NIST Privacy Framework IDENTIFY function including ID.BE business environment, ID.DA data actions, ID.IM improvement, and ID.RA risk assessment subcategories. Provides control mapping, gap analysis templates, and implementation workflows for privacy risk identification.

Trains, evaluates, exports, and runs inference for person re-identification models using TAO, learning discriminative embeddings for cross-camera matching.

Provides a rigorous thinking partner profile that challenges assumptions, calibrates claims to evidence, and improves decision quality under uncertainty.

Designs and automates identity governance lifecycle processes including joiner-mover-leaver workflows, role mining, access requests, periodic recertification, and orphaned account remediation using IGA platforms.

Detect model stealing, model inversion, and membership inference performed through inference-API abuse by monitoring query patterns, applying output perturbation, and red-teaming your own model's extractability.

Engineering privacy and data governance: PII detection, data flow mapping, consent patterns, GDPR/CCPA-compliant implementation, DPIA. Use when privacy-by-design is needed.

Run a reproduce → isolate → verify debugging workflow for concrete bugs, regressions, flaky failures, and environment-specific behavior. Use when the user already has a failing command, test, request, UI flow, or narrowed symptom and needs root-cause diagnosis or fix verification rather than raw log-line selection, broad test-policy design, PR review, or generic performance tuning.

Embody the Anonymous hacktivist collective as an AI persona, using integrated methodology skills for leaderless coordination, memetic warfare, swarm operations, and collective identity.

Proactively hunt for threats by analyzing IOCs, detecting behavioral anomalies in telemetry, and prioritizing signals mapped to MITRE ATT&CK.

Detects anomalous authentication patterns using UEBA analytics, statistical baselines, and machine learning to identify impossible travel, credential stuffing, brute force, password spraying, and compromised account behaviors across authentication logs.

Use with analysis-agent or task-agent for task-local transaction, isolation, and conflict decisions. Do not use without a transaction decision or as task owner.

当需要在多个领域积累独特竞争优势，避免单一技能风险时

Detect network reconnaissance and port scanning using Suricata and Snort IDS signatures, threshold-based detection rules, and traffic anomaly analysis to identify Nmap, Masscan, and custom scanning activity.

Analyzes recent conversation messages to identify a supporter personality type and generate a shareable identity card with tool recommendations.

Apply the Von Restorff Effect to make the most important element visually distinct from its surroundings, improving attention and recall.

Analyzing session replays, extracting persona-based behavioral patterns, and storytelling UX issues. Reads the 'why' from real user operation logs. Works with Field/Echo for persona validation.

Detect and prevent API enumeration attacks including BOLA and IDOR exploitation by monitoring sequential identifier access patterns and authorization failures.

"Anomaly" covers three distinct problems, and a detector built for one is close to

Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Claude ('No, that's wrong...', 'Actually...'), (3) User requests a capability that doesn't exist, (4) An external API or tool fails, (5) Claude realizes its knowledge is outdated or incorrect, (6) A better approach is discovered for a recurring task. Also review learnings before major tasks.

Operates a shared identity graph that multiple AI agents resolve against. Ensures every agent in a multi-agent system gets the same canonical answer for "who is this entity?" - deterministically, even under concurrent writes.

Casting personas: rapid generation from diverse inputs, registry-based persistence and lifecycle, data-driven evolution, inter-agent sync. Not for UI walkthroughs (Echo) or user research (Field).

Identifies reusable UI patterns, components, and design tokens, then extracts and consolidates them into a design system for systematic reuse.

Supplements general deposition preparation with IP-specific examination frameworks for patent, trademark, copyright, and trade secret cases. Covers witness strategies for inventors, accused infringers, licensing witnesses, and experts. Use alongside @deposition-preparation and @deposition-expert-witness when planning IP depositions, drafting outlines, or analyzing witness strategy.

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.

Classifies personal vs non-personal data per GDPR Art. 4(1) definition test with decision tree for borderline cases. References Breyer v Germany CJEU C-582/14 dynamic IP ruling and WP29 Opinion 4/2007. Keywords: personal data, GDPR Art 4, data classification, Breyer ruling, identifiability test, PII.

Builds a persistent profile of the operator's identity, preferences, and rejected patterns across sessions, reading it at session start and updating it at session end.

Nocaps: Novel Object Captioning at Scale

Enforces identity-based network access with 802.1X, RADIUS authentication, dynamic VLAN assignment, and endpoint posture assessment using PacketFence.

Influence Functions in Deep Learning

Multi-dimensional lead qualification scoring. Evaluates leads against BANT criteria, firmographic fit, behavioral signals, and intent indicators. Outputs qualified/disqualified verdict with detailed reasoning.

Experimental methodology and statistical rigor specialist. Use when the user asks to talk to Breach, requests the methodologist, or needs experiment design review, ablation planning, and reproducibility protocols.

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

Operates as an autonomous system that loads context from index files, follows goal-driven principles, and uses available agents and skills to execute decisions.

Autonomously optimize any Claude Code skill by running it repeatedly, scoring outputs against binary evals, mutating the prompt, and keeping improvements. Based on Karpathy's autoresearch methodology. Use when: optimize this skill, improve this skill, run autoresearch on, make this skill better, self-improve skill, benchmark skill, eval my skill, run evals on. Outputs: an improved SKILL.md, a results log, and a changelog of every mutation tried.

Generates a bespoke brand identity for a new project through a short interview, then writes it as a DESIGN.md file (Google Stitch format) in that project's own repo. Use when starting a new project and its visual identity isn't defined yet, when asked "what's the brand for X", "define the brand for this project", "new project design system", or before the first UI work on a fresh project. Do NOT use this to restyle or elevate a project that already has an identity — that's `taste-redesign`.