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Found 6,510 Skills
Use Probe to interact with Nexus via CLI commands. Agents use probe for Zenon Network wallet creation, auth, ideas, projects, tasks, claims, PR/issue linking, messaging, daemon liveness, and SQL inspection. Use when you need to (1) create or manage project artifacts (ideas, projects, tasks), (2) claim and execute tasks with PR delivery, (3) maintain online presence and heartbeats, or (4) inspect Nexus state via SQL.
Audit a design proposal or diff against Exarchos's architectural invariants — event-sourcing integrity (INV-1), facade equivalence over shared dispatch core (INV-2), basileus-forward (INV-3), platform-agnosticity (INV-4), and agent-first interface design (INV-5a input ergonomics, INV-5b spec-aligned output contract, INV-5c Aspire-inspired control-plane verbs, INV-5d action discriminator pattern). Pairs with /axiom:backend-quality — this skill is project-specific (axiom is generic). Triggers: 'check invariants', 'design conformance', 'check #1118 / #1109', or /design-invariants.
Design failing tests for complex features using Independent Evaluation — dispatches a context-free agent that sees only the requirement spec and code paths (not the implementation approach), then returns executable failing tests. Use when starting TDD for a non-trivial feature, when the requirement is ambiguous enough that biased tests are a risk, or when the user asks for independent test design.
INVOKE THIS SKILL when auditing an AI agent or LLM app for regulatory compliance. Covers EU AI Act, GPAI Code of Practice, GDPR, NIST AI RMF, Colorado AI Act, HIPAA, and ISO 42001. Scans the codebase for compliance gaps, cross-references Arize instrumentation for audit trail coverage, and produces an actionable remediation checklist tailored to the selected frameworks.
Comprehensive testing doctrine for software and AI systems — covers positive patterns, anti-patterns, gates for coding agents writing tests, CI discipline, and an LLM/agent evaluation primer. Use when authoring or reviewing tests, adding mocks, deciding test placement, generating tests via agents, debugging flaky CI, designing eval suites for LLM features, or rebuilding a brittle test suite. Contains 12 positive patterns (selector hierarchy, table-driven, builders, real-system gates), 25 anti-patterns across Brittleness, Flakiness, Mock-misuse, Process, and AI-specific families, 7 mandatory gates for agents writing tests, flaky-test taxonomy with quarantine workflow, contract / property / mutation testing patterns, and an oracle-ladder primer for LLM-as-judge and agent eval. Language-agnostic — pseudo-code only. Don't use for general code review, library-specific debugging unrelated to tests, non-testing CI pipeline design, or production observability.
Capture a full DevTools-protocol trace of any browser automation — CDP firehose, screenshots, and DOM dumps — then bisect the stream into per-page searchable buckets. Use when the user wants to debug a failed run, audit network/console/DOM activity, attach a trace to an in-progress session, or feed structured per-page summaries back into an agent loop so its next iteration learns from the last one.
Manage and monitor VSS alerts after the alerts profile is deployed. The deployment's mode (CV vs VLM real-time) is fixed at deploy time and determines the workflow — start/stop real-time alerts via the VSS Agent on a VLM deployment, onboard CV alerts by adding RTSP streams to VIOS on a CV deployment, query incidents, customize verifier prompts. Use when asked to start/stop a real-time alert, check or list alerts, add a camera, use a sample video for alerts, customize alert prompts, or view verdicts.
Implement Cisco's Foundry specification for agentic AI security evaluation systems with multi-agent architecture
Use when the user is doing AI/ML work in a scientific domain — biology, chemistry, physics, astronomy, climate, genomics, materials science, medicine, ecology, energy, conservation, engineering, mathematics, scientific reasoning, drug discovery, protein design, weather modeling, theorem proving, single-cell, PDE solving, or anything similar. Hugging Science (huggingscience.co) is a curated catalog of scientific datasets, models, blog posts, and interactive Spaces; the `hugging-science` org on Hugging Face hosts community datasets, models, and demo Spaces. This skill helps you discover the right resource AND actually use it — loading datasets via `datasets`, running models via `transformers` or the HF Inference API, calling Spaces like BoltzGen via `gradio_client`, and citing blog posts for methodology. Trigger this skill whenever a user mentions a scientific ML task, asks for "a dataset/model for X" where X is a scientific topic, wants to fine-tune on scientific data, asks about protein / molecule / genome / climate / materials / astronomy / pathology / weather ML, or needs AI tools for research — even if they never say "Hugging Science" explicitly. The catalog is purpose-built for LLM agents (it ships an `llms-full.txt`); prefer it over generic web search for these tasks.
Srcwalk is the agent's code navigator: one tree-sitter CLI for repo maps, token-aware large-file reads, symbol search, callers/callees, deps, impact checks, and precise drill-ins. Use it before raw reads or grep for code-structure work. Run `srcwalk guide` first. Must use! It is the installed binary's source of truth.
Generates a contextual onboarding document for a new contributor or agent joining the project. Summarizes project state, architecture, conventions, and current priorities relevant to the specified role or area.
Localization (i18n) across all CometChat UI Kit families — React, React Native, Angular, Android (V5/V6), iOS, Flutter (V5/V6). Covers CometChatLocalize.init signature differences (positional vs object), bundled languages, custom-language registration, RTL support, fallback to English, and cross-family drift risks. Cross-family — applies wherever the agent is configuring CometChat localization.