「bi」の検索結果

Query patent bibliographic (catalog) information from the Nexscope patent database by patent ID or publication number. Triggered when users mention patent bibliographic info queries, patent catalog information, patent applicant queries, patent inventor queries, patent classification codes, patent abstract retrieval, patent citation analysis, patent priority claims, patent application citations, patent examiner information, patent bibliographic data, inventor lookup, applicant lookup, patent classification, patent metadata, Nexscope, or patent citations. Even if the user does not explicitly mention "bibliographic info," this skill should be triggered whenever their need involves querying detailed metadata for a specific patent by patent ID or publication number.

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。

Learns the words a user habitually writes to their coding agent that make its answers worse ("be 100% sure", "don't assume", "full review", "are you sure?", "all fine, right?"), from what they actually typed in their recent Claude Code sessions. Shows what each one does, what went wrong after it, and what to say instead, then writes the habit words block that maps each word to the checks it should trigger, so the checks run without the words. Use when asked to learn, read, refresh or review the user's habit words or prompts, "which of my words make you worse", from setup-first-pass's profile step, or when the start-of-session check says the habit words are due.

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。

Marketingbilder rechtebewusst planen, erzeugen, bearbeiten, optimieren und prüfen. Nutze diesen Skill für Bildbriefings, Produktscreenshots und Mock-ups, generative Bilder, Stock- und Lizenzrouten, Bildbearbeitung, Formate und Komprimierung sowie deutsche Alt-Texte. Klärt Herkunft und Nutzungsrechte vor der Produktion, beachtet Persönlichkeits-, Marken- und Urheberrecht, verlangt Kennzeichnung wo nötig und schließt erfundene Produktansichten und unbelegte Bildaussagen aus. Nicht gedacht für Logos, Design-Systeme und Layoutarbeit.

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。

Reading a habitat — identifying biome, plant community, food web structure, successional stage, and the keystone species that hold the system together. Covers the elevational and latitudinal gradients that Humboldt first mapped, the structural vocabulary for describing a habitat, the core concepts of food-web and trophic analysis, and the practice of diagnosing habitat health from indicator species. Use when the task is to understand a place rather than a species.

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。

Interact with Bitbucket repositories and pull requests using the BITBUCKET_TOKEN environment variable. Use when working with code hosted on Bitbucket or managing Bitbucket resources via API.

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。

Interleave layer bridging the BigQuery cluster to plurigrid/asi. Routes BigQuery queries through asi's DuckDB stack, wires patent search into asi knowledge graph, connects Looker Studio dashboards to CatColab, and feeds BigQuery ML into the lolita physics emulation pipeline.

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 biomedical AI agent framework for executing complex research tasks across genomics, drug discovery, molecular biology, and clinical analysis. Use this skill when conducting multi-step biomedical research including CRISPR screening design, single-cell RNA-seq analysis, ADMET prediction, GWAS interpretation, rare disease diagnosis, or lab protocol optimization. Leverages LLM reasoning with code execution and integrated biomedical databases.

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。

Runs the description-drift experiment — spawns all Claude Code agents simultaneously to collect self-reported capabilities, then compares them against static frontmatter descriptions to reveal how reliable orchestrator routing based on descriptions actually is. Use when measuring description drift across the agent fleet, re-running the capability collection experiment, analyzing a specific agent's self-reported capabilities, or auditing whether frontmatter descriptions accurately reflect agent behavior.

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。

Run SQL against Google BigQuery and browse its catalog — submit queries (sync or async), poll job status, page through results, list datasets/tables, and read table schemas. Use this whenever the user wants to query a BigQuery table, ask "what's in this dataset", check a BigQuery job's status, or mentions bigquery.googleapis.com or a `project.dataset.table` path. Always start from this skill when interacting with this service — its bundled scripts and recipes are the fastest path.

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。

Evaluate tabulated subcontractor bids against specs and drawings — scope gap analysis, exclusion risk scoring, award recommendation. Triggers: 'evaluate bids', 'bid evaluation', 'lowest responsible bidder'.

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。

Cofold protein-ligand, protein-protein, and nucleic-acid complexes with Boltz-2, and predict binding affinity with its trained affinity head. Use this skill to build Boltz input YAML, run structure prediction with MSAs, pocket constraints, templates, and modified residues, screen compound libraries by cofolding, and interpret confidence scores (pLDDT, pTM, ipTM, PDE) and affinity output (binder probability and log10 IC50). Also trigger on Boltz, Boltz-1, Boltz-2, cofolding, boltz predict, affinity_pred_value, affinity_probability_binary, ipTM, or open-weights AlphaFold3 alternatives.

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。

Fit hidden Markov models using the Baum-Welch (EM) algorithm with model selection, Viterbi decoding for state sequences, and forward-backward probabilities. Use when observations are generated by unobservable latent states, you need to segment a time series into latent regimes (market regimes, speech phonemes, biological sequences), compute sequence probabilities, decode the most likely hidden state path, or compare models with different numbers of hidden states.

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。

Design an A2A Agent Card (.well-known/agent.json) manifest describing agent capabilities, skills, authentication requirements, and supported content types. Use when building an agent that must be discoverable by other A2A-compliant agents, exposing capabilities for multi-agent orchestration, migrating an existing agent to the A2A protocol, defining the public contract for an agent before implementation, or integrating with agent registries that consume Agent Cards.

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。

Analyze a tensegrity system by identifying compression struts and tension cables, classifying type (class 1/2, biological/architectural), computing prestress equilibrium, verifying stability via Maxwell's rigidity criterion, and mapping biological tensegrity (microtubules, actin, intermediate filaments). Use when evaluating tensegrity in architecture, robotics, cell biology, or any system with isolated compression in continuous tension.

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。

FastAPI-specific delta on top of python-foundation:sqlalchemy-patterns: async engine and AsyncSession lifecycle, get_db dependency injection, async-safe lazy loading rules, async Alembic env.py wiring. Used by fastapi-architect (model definitions) and alembic-specialist (column type finalization and migration generation). Activated automatically by fastapi-plugin/stack.md. Use this skill to: - Manage async database sessions with AsyncSession and async_sessionmaker. - Inject sessions into routes via the get_db() dependency. - Apply async-safe lazy loading (lazy="selectin" or "raise"; never sync lazy loads). - Integrate async Alembic env.py for migration autogeneration. Do NOT use this skill for: - Framework-agnostic model, column, relationship, and querying rules — see python-foundation:sqlalchemy-patterns (load it first). - FastAPI routing and Pydantic schemas — see fastapi-plugin:fastapi-conventions. - Alembic migration execution (that's alembic-specialist's job) — this skill covers definitions.

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。

Agent Skillのusability testを行うスキル。 下位tierのmodelのsubagentに、検証したいskillだけを頼りにゴールを与え、手を貸さずに観察し、終了後にインタビューとtranscriptの監査で判断の根拠を確かめる。 ユーザーが「skillのusability testをして」「skillをE2Eテストして」「手順書だけで完遂できるか試して」と言った時に使用する。 手順の追加や組み替え、新しいコマンドの組み込み、ガイドラインの判断基準の変更のように、skillのワークフロー構成や判断基準に影響する変更をした後に、実行をユーザーに提案する。

Extract a DDD-style ubiquitous language glossary from the current conversation, flagging ambiguities and proposing canonical terms. Saves to UBIQUITOUS_LANGUAGE.md. Use when user wants to define domain terms, build a glossary, harden terminology, create a ubiquitous language, or mentions "domain model" or "DDD".

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。

Set up or operate a business operations workspace for activity reports, tasks, and customer routing; workIQ is optional. Use when initializing BizOps, processing ongoing business updates, reviewing reports or tasks, or preparing an operations handoff. Triggers: business-operations-workspace, BizOps, 業務管理ワークスペース, レポート管理, タスク管理, 業務引き継ぎ.

"Apply AI ethics frameworks (fairness, accountability, transparency, privacy) to evaluate AI systems for algorithmic bias, explainability gaps, and value alignment failures. Use this skill when the user needs to audit an AI system for ethical risks, design fairness constraints, assess explainability requirements, or when they ask 'is this AI system fair', 'how do we detect algorithmic bias', 'what are the ethical implications of this AI deployment', or 'how do we make this model explainable to stakeholders'.".

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。

Builds and audits a concept-ontology for a regelrecht corpus to decide and verify reference types — cross-law source-binding vs intra-law source-binding vs open-norm leaf vs external-fact leaf — and to catch concept conflation across laws. Core principle: a value's reference type is fixed by WHO derives it, and a binding is sound only when producer and consumer denote the SAME concept; the lexical surface (the word in the text) is not the concept. Detects false friends (one term, different concepts — e.g. a register-based vs a factual "naar de omstandigheden"-based residence/ingezetene concept), orphan plain-params (a value a regulation actually derives, modeled as a brute input), negation/complement re-entry (¬X modeled as an independent leaf next to X), and duplication (one concept re-entered as several independent leafs, losing the single-source-of-truth invariant). Produces a law-level glossary and a stelsel-level concept map with sound same-concept binding edges and flagged lexical-match-but-concept-mismatch edges. Use when deciding whether a value should be cross-law/intra-law/leaf, when the same term means different things across laws, or when a binding might silently conflate concepts.

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。

Identify and exploit misconfigured Linux Capabilities. This skill covers how attackers escalate privileges to root without relying on SUID binaries or kernel exploits by abusing excessive capabilities like cap_dac_read_search, cap_sys_ptrace, or cap_setuid assigned to ordinary files.

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。

Writes professional bug bounty reports for HackerOne, Bugcrowd, and Intigriti with CVSS 4.0 scoring, business impact, working exploits, and remediation. Runs 5-check Pre-Report Verification first: hallucination detection, AI writing patterns, PoC reproducibility, duplicate detection, and impact plausibility. Use when user describes a vulnerability, shares HTTP logs, HAR files, recon output, or screenshots; says 'write a bug report', 'format my finding', 'draft a vuln report', 'is this valid', 'rate my vulnerability', 'verify my report', or any variant. Trigger for partial or messy input — raw notes, one-liners, or full writeups all work. Do not wait for perfect input.

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。

Bug bounty findings secretary, tracker, and workspace initializer for the bb-huge portal. Use this skill for web security research, vulnerability hunting, and hunt workspace setup. Triggers on: "log finding", "save finding", "add to bb-huge", "record vulnerability", "update finding", "show findings", "bb-huge stats", "mark as confirmed", "mark as rewarded", "setup workspace", "pull evidence", "continue working on finding", "dump attachments", "list my skills", "what skills do I have", "/bb-huge init", "init hunt workspace", "new bug bounty program", "start hunting", "mobile bug bounty", "binary analysis", "source code review", "white-box audit", "h1-brain", "hack program", "hack()", "search disclosed", "fetch rewarded". Also auto-activates whenever a vulnerability is discovered during any recon, fuzzing, or manual testing session — do not wait to be asked.

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。

Work with BioReason-Pro predictions and reasoning traces for protein function. Use when: comparing BioReason outputs against curated reviews, converting web exports to YAML, reviewing reasoning trace quality, or running the SFT/RL comparison. Triggers: "bioreason", "GO-GPT predictions", "reasoning trace review", "bioreason comparison", "SFT vs RL".

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。