361건 · 관련도순
Bei vorhandenem und ausdrücklich autorisiertem lokalem Entscheidungsprofil: wiederkehrende Entscheidungen aus belegtem Feedback vorhersagen, Konfidenz kalibrieren und Vorhersage, Entscheidung und Ausführung strikt trennen.
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Selbstjustierende Takt-Regelschleife für wiederkehrende Agenten-Scans. Nutzen, wenn ein geplanter Scan sein Intervall bei Aktivität schärfen und bei Stille abkühlen soll, ohne Operator-Eingriff.
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Dach-Skill für Cloud-gestützte Kommunikationsprotokolle zwischen Agenten auf unterschiedlichen Maschinen (Ping-Pong, agent-beam, Listener und künftige Protokolle). Nutzen, wenn Arbeit über Maschinen hinweg per gemeinsamem Sync-Ordner oder Message-Yard koordiniert wird.
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Automated software development pipeline. Scans projects, prioritizes tasks, analyzes code, and orchestrates development loops. Zero dependencies (Python stdlib only).
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Local, private capture of medical data: diagnoses, symptom histories and examination plans. No BACH origin — custom design with its own SQLite store. Strictly local, no cloud transfer.
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Build automated evaluation suites for AI agents using golden datasets, rubrics, and regression gates. Use when shipping agent features, validating prompt changes, or gating deployments on quality.
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Instrument AI agents with tracing, token metrics, latency, and cost visibility. Use for reliability and debugging.
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Schedule AI agent runs with cron, loops, or external clocks while avoiding unsafe tight autonomous timers.
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Use when working with Aws Control Tower — aWS Control Tower landing zone and account management. Covers landing zone status, enrolled accounts, guardrails (controls), organizational units, baseline configurations, and drift detection. Use when auditing landing zone health, reviewing guardrail compliance, inspecting account enrollment, or troubleshooting Control Tower drift.
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Busabase App-in-Skill dashboard visualizing a generated MOCK fleet of LLM agents running behind a shared AI gateway for a generic organization. Use when the user invokes $kelly-agent-observability or /kelly-agent-observability, wants to review agent health, call volume, latency, error rates, cost, or trace-level chain breaks across an agent fleet. This is a demo/reference dashboard over a generated mock fleet snapshot seeded into Busabase by a trusted script — never a live monitoring integration, never a real gateway or real agents.
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Deploying Cloudflare Access with Cloudflare Tunnel to provide zero trust access to self-hosted and private applications, configuring identity-aware access policies, device posture checks, and WARP client enrollment for VPN replacement.
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Focused Signals scout for PostHog projects using AI observability. Rotates through a set of lenses — cost, latency, errors, volume, eval performance, eval/enrichment config, clusters, and tool usage — watching each for trends and spikes sliced by the dimensions it discovers over time. Leans on the sandbox's bundled `exploring-llm-*` deep-dive skills for the actual queries. Emits findings only when they clear the confidence bar; otherwise writes durable memory and closes out empty. Self-contained peer in the signals-scout-* fleet — no dependencies on other scouts.
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Claude Code (code.claude.com) デプロイ・CI/CD リファレンス。 Amazon Bedrock / Google Cloud Agent Platform (Vertex AI) / Microsoft Foundry / Claude Platform on AWS、 Claude apps gateway、LLM gateway (protocol / rollout)、 GitHub Actions (claude-code-action) / GitLab CI/CD / GitHub Enterprise Server (GHES)、 Code Review (REVIEW.md・review level・@claude review always・コスト・トラブルシュート)、 ローカル /code-review、ultrareview (claude ultrareview)、 Slack (Claude Tag、旧 Claude Code in Slack、Pro/Max)、 headless mode、cloud environments、devcontainer、 self-hosted environments（runner 構成・identity 検証・本番デプロイ）。
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Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.
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Instrument a shipped product's AI agents with tracing and observability so you can see what they did, why outputs happened, and what each run cost. Plain-language primer plus free-tier-first backend selection (Langfuse, Phoenix, LangSmith, Braintrust) and OpenTelemetry/OpenInference instrumentation. Load when the user asks to add observability, add tracing, instrument my agents, see what my agent is doing in production, set up Langfuse or Phoenix or LangSmith, debug why my agent gave a bad answer, or track LLM cost per request. Also fires when agent-system-architecture or setup-evaluation requires an observability plan for an agent-chain product. NOT for tracing the coding agent itself — that is run-trace. Precondition for runtime-learning-loop.
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Prepare for Anthropic-specific technical interviews covering Constitutional AI, RLHF, interpretability, scaling laws, MCP, agentic systems, and AI safety. Activate on "anthropic interview", "constitutional AI prep", "alignment interview", "safety interview", "RLHF opinion", "interpretability prep", "anthropic technical". NOT for general ML interview prep, system design interviews, coding challenges, or behavioral interview practice.
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Recommend, create, preview, and attach Coval trace-based metrics from OpenTelemetry spans. Use when a user has Coval traces and wants custom trace metrics, trace-aware LLM judge metrics with include_traces, latency, token usage, provider failures, tool behavior, vertical-specific workflow signals, or production monitoring based on trace attributes.
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Improve Coval trace quality after basic ingestion works. Use when traces are sparse, missing useful STT/LLM/TTS/tool spans, missing attributes needed for Coval built-in metrics, or when a customer wants maximum debugging and observability value from agent traces.
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Security review and threat work — threat modeling, vulnerability assessment, penetration test, incident response, OWASP Top 10, LLM Top 10, API Top 10, CVE triage, STRIDE, NIST CSF, SBOM. Auto-loops on any task touching PII, auth, payments or regulated data.
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OpenInference semantic conventions and instrumentation for Phoenix AI observability. Use when implementing LLM tracing, creating custom spans, or deploying to production.
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INVOKE THIS SKILL when creating, reading, updating, or deleting Arize AI integrations. Covers listing integrations, creating integrations for any supported LLM provider (OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Vertex AI, Gemini, NVIDIA NIM, custom), updating credentials or metadata, and deleting integrations using the ax CLI.
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Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality
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Deploying Cloudflare Access with Cloudflare Tunnel to provide zero trust access to self-hosted and private applications, configuring identity-aware access policies, device posture checks, and WARP client enrollment for VPN replacement.
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Complete reference for 26 web2 bug classes with root causes, detection patterns, bypass tables, exploit techniques, and real paid examples. Covers IDOR, auth bypass, XSS, SSRF (11 IP bypass techniques), SQLi, business logic, race conditions, OAuth/OIDC, file upload (10 bypass techniques), GraphQL, LLM/AI (ASI01-ASI10 agentic framework), API misconfig (mass assignment, JWT attacks, prototype pollution, CORS), ATO taxonomy (9 paths), SSTI (Jinja2/Twig/Freemarker/ERB/Spring), subdomain takeover, cloud/infra misconfigs, HTTP smuggling (CL.TE/TE.CL/H2.CL), cache poisoning, MFA bypass (7 patterns), SAML attacks (XSW/comment injection/signature stripping), error disclosure / debug endpoints (stack trace regex per framework, chain templates), CSS injection (attribute-selector exfiltration, opacity clickjacking, @import). LFI / file inclusion -> RCE (php://filter source disclosure, iconv filter-chain RCE with no upload, log/environ poisoning, .user.ini/.htaccess auto_prepend, data:// + expect:// wrappers, session inclusion, traversal bypass table). Insecure deserialization (PHP __wakeup bypass / phar:// POP chains, Java ysoserial CommonsCollections gadgets + magic bytes, Python pickle __reduce__ + signed-cookie forgery, Node node-serialize). Dependency confusion / supply chain (internal package-name discovery, unclaimed-name confirmation, callback-only PoC, npm/pip/Maven/RubyGems variants). Use when hunting a specific vuln class or studying what makes bugs pay.
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