「skill discovery」の検索結果

Runs several language models as independent coders on the same labeling or discovery task and reads their disagreement as data — panel assembly for model diversity, keeping votes independent, consensus rules, agreement statistics (Cohen's and Fleiss kappa, Krippendorff's alpha), the correlated-errors caveat that stops agreement being mistaken for validity, human validation beyond the panel, and reporting. Use when the user asks about a council, panel, ensemble, or jury of models, asks how to combine several models' labels, or asks what kappa or alpha to report for model coders. Single-model codebook and validation work goes to text-classification, per-item confidence to llm-calibration-logprobs.

日本語の概要は準備中です。原文の説明を表示しています。

Create a shared ServiceDefaults project for Aspire applications. Centralizes OpenTelemetry, health checks, resilience, and service discovery configuration across all services.

日本語の概要は準備中です。原文の説明を表示しています。

Write integration tests using .NET Aspire's testing facilities with xUnit. Covers test fixtures, distributed application setup, endpoint discovery, and patterns for testing ASP.NET Core apps with real dependencies.

日本語の概要は準備中です。原文の説明を表示しています。

Configure Aspire AppHost to emit explicit app config via environment variables; keep app code free of Aspire clients and service discovery.

日本語の概要は準備中です。原文の説明を表示しています。

Akka.Management for cluster bootstrapping, service discovery (Kubernetes, Azure, Config), health checks, and dynamic cluster formation without static seed nodes.

日本語の概要は準備中です。原文の説明を表示しています。

Advises on setting up and validating topic models for survey and experimental text. Covers the discovery-versus-measurement question, choosing STM, LDA, or BERTopic, preprocessing consequences, prevalence and content formulas, topic-count selection across coherence, exclusivity and FREX, held-out likelihood, and residuals, validation of topics against human judgment, robustness, and DA-RT-compliant reporting. Use when the user has open-ended responses or another corpus and asks what topics are present, how many topics to use, about STM, searchK, coherence, or FREX, or wants topic prevalence compared across treatment arms or countries. Fixed-codebook coding goes to text-classification.

日本語の概要は準備中です。原文の説明を表示しています。

Advises on setting up LLM-based text classification as a validated measurement design. Covers codebook construction, learning-regime and model choice, prompt construction, validation against human coding with kappa and F1, hybrid human-LLM review, downstream measurement error, and reporting, plus a short resumable batch pattern with a rule-based baseline for repeated free text against a closed codebook. Use when the user wants to classify, code, or label text at scale. Category discovery goes to topic-modeling.

日本語の概要は準備中です。原文の説明を表示しています。

Advises on setting up and validating LLM text classification: codebook, learning regime and model choice, prompts, validation against human coding (kappa, F1), hybrid review, measurement error, and reporting, plus a short resumable batch pattern with a rule-based baseline. Use to classify, code, or label text at scale; category discovery goes to topic-modeling.

日本語の概要は準備中です。原文の説明を表示しています。

Advises on setting up and validating topic models for survey and experimental text: discovery vs. measurement, STM/LDA/BERTopic choice, preprocessing, covariate formulas, choosing K across coherence, FREX, held-out likelihood, and residuals, validating topics against human coding, robustness, and reporting. Coding against a fixed codebook goes to text-classification.

日本語の概要は準備中です。原文の説明を表示しています。

Use when an existing repository contains agents, prompts, workflows, playbooks, commands, or Skills and you need to identify which capabilities are worth converting into a ChatGPT/Codex Plugin.

日本語の概要は準備中です。原文の説明を表示しています。

Dogfooding discovery agent — establish human-approved project baseline from public docs without code inspection

日本語の概要は準備中です。原文の説明を表示しています。

Discovers all Claude Code agents in the system including built-in, plugin, project, and user-level agents. Use when you need to find which agents are available, understand the agent ecosystem, or prepare agents for Actoris registration.

日本語の概要は準備中です。原文の説明を表示しています。

Use whenever the user wants to find, rank, or shortlist directories and registries where they can submit or list an MCP server (Model Context Protocol server) — to get backlinks, referral traffic, and discovery by agent builders. Triggers on "where do I list my MCP server", "best MCP directories", "MCP registries to submit to", "get my MCP server discovered", or "pull submission details for these MCP-directory domains", even when described indirectly (we built an MCP server, where do we publish it). Drives the ServiceGraph API (api.servicegraph.co) — a catalog of 1,000+ product directories enriched with Domain Rating, backlinks, and organic traffic. Defer to find-ai-directories for general AI-tool / AI-agent / agent-skill listings, and to find-product-directories for general SaaS/software launches. Skip building an MCP server or asking how MCP works (DIY), finding a firm to build one (use find-ai-consultancy / find-software-developer), and MCP link-building *services*.

日本語の概要は準備中です。原文の説明を表示しています。

Strategic sales leadership guidance for B2B SaaS and enterprise software companies. Covers sales strategy, team building, pipeline management, enterprise selling, discovery calls, demos, proposals, negotiations, and sales operations. Use when building sales teams, designing sales processes, improving win rates, or scaling revenue operations. Use for "sales strategy", "sales process", "pipeline review", "deal strategy", "sales hiring", "quota planning".

日本語の概要は準備中です。原文の説明を表示しています。

HTTP/HTTPS application-layer reconnaissance after a web service is identified: fingerprinting, content/API discovery, JS/source maps, proxy/cache topology, WAF, CMS, and known CVE/PoC candidates. Recon only — do not exploit, write files, or obtain a shell. Hand CVE candidates and attack surface to the operator, who may select /web-attack. Non-HTTP ports belong to /recon.

日本語の概要は準備中です。原文の説明を表示しています。

Credential operations: secret discovery, classification, extraction, conversion, offline cracking of hashes the operator already has as a credential job, policy-aware spraying, NetNTLM capture, generic SMB relay, and Windows/Linux harvest. Do not hijack an in-progress /ad-attack Kerberoast/AS-REP chain (that module cracks and uses the ticket itself). Do not DCSync, read LAPS LDAP, or escalate cloud IAM. Usable credentials are recorded; the operator chooses the next module.

日本語の概要は準備中です。原文の説明を表示しています。

Audit and rewrite a Threads (Meta) profile end-to-end for 2026: profile photo, name with a searchable keyword, @handle, bio (150 chars, warm and specific), link, pinned post, and the connected-Instagram cross-pull that feeds Threads discovery. Triggers on "review my Threads profile", "fix my Threads bio", "what to pin on Threads", and "profile audit". Turns a default profile into one that converts visitors into followers. Not for writing posts (use threads-post-writer).

日本語の概要は準備中です。原文の説明を表示しています。

Autonomous AI Sales Development Rep for B2B export — handles lead discovery, BANT qualification, multi-channel outreach (WhatsApp/Email/Telegram), CRM management, and deal pipeline tracking. Built for cross-border trade.

日本語の概要は準備中です。原文の説明を表示しています。

Fuzz directories, files, and parameters using ffuf, feroxbuster, arjun, paramspider, and x8. Use when the user has live hosts and needs to find hidden routes, backup files, exposed configs, and untracked URL parameters.

日本語の概要は準備中です。原文の説明を表示しています。

Academic research skill for Biblical Hebrew, Semitic linguistics, cuneiform studies, and comparative Ancient Near Eastern research. Provides Sefaria API for Hebrew Bible, CDLI/ORACC for cuneiform databases, and web discovery via Omnisearch, Exa, Firecrawl, and Obscura for finding scholarly sources across JSTOR, Perseus, Persée, Google Scholar, and academia.edu. Triggers on Hebrew quotes, cuneiform, Sefaria, ANE research, Minoan, search for scholarship, find papers, literature review, scholarly search, academic search, Genesis/Tehom, Ugaritic, Talmudic sources, extract from PDF, OCR academic.

日本語の概要は準備中です。原文の説明を表示しています。

Use this skill when tool outputs exceed 2000 tokens, tasks span multiple conversation turns, sub-agents need to share state, context window is bloating, plans need to persist across summarization, terminal/log output needs selective querying, or when user mentions "offload context", "dynamic context discovery", "filesystem memory", "scratch pad", "reduce context bloat", or "just-in-time context loading".

日本語の概要は準備中です。原文の説明を表示しています。

Generates a complete, professional client onboarding package for AI consultants, AI auditors, and solopreneurs working in the AI space (AI strategy, automation, AI readiness audits, implementation, training, etc.). Use this skill whenever a user asks to onboard a new client, set up a client folder, write a welcome email, generate a discovery questionnaire, draft a service agreement, or create a project brief — especially in an AI consulting context. Also trigger when the user says things like "I have a new client", "prepare onboarding docs", "create a client package", or "set up a new project". Produces four ready-to-use deliverables: intake questionnaire, welcome email, service agreement, and project brief.

日本語の概要は準備中です。原文の説明を表示しています。

Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For ready-made ADMET numbers without training a model use admet-prediction; for benchmark datasets and task-aware splits use pytdc.

日本語の概要は準備中です。原文の説明を表示しています。

Access a collection of open-source molecular design and structural biology tools on the Tamarind Bio platform, via its REST API or MCP server — no local GPUs required. Tamarind bundles popular open-source models for structure prediction (AlphaFold, Boltz, Chai, ESMFold), protein, binder, and de novo design (RFdiffusion, ProteinMPNN, BoltzGen), antibody and nanobody design and developability, protein-ligand docking (DiffDock, Autodock Vina), binding-affinity prediction, MSA generation, and molecular dynamics. Use when the user mentions Tamarind or tamarind.bio, wants to run any of these open-source tools in the cloud, references app.tamarind.bio/api or the x-api-key header, or needs to submit batches of sequences for structural or biophysical characterization.

日本語の概要は準備中です。原文の説明を表示しています。