9,783 件 ・ 関連度順
Dynamically discover and load ACSet reference skills in dependency-optimal order using GitHub interactome analysis. Use when loading the acsets skill and needing its validation, generation, and coordination partners resolved automatically with GF(3) conservation guarantees.
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。
Retroductive hypothesis sampling via MCMC: given observations, importance-sample explanations weighted by P(observation|hypothesis) using Gay.jl colored particles as hypothesis candidates.
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。
Build an interactive account org chart, leadership map, buying committee, or manager/report neighborhood around a person. Use for decision makers, reporting-line candidates, stakeholder mapping, and warm intro paths. Skip standalone email lookup and unrelated cross-company prospecting.
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。
House rules and craft for LaTeX Beamer slide decks (lectures, seminars, talks), drawn from the author's own Overleaf edits and published slide-design guidance. Covers storyboarding, frame titles, slide prose, word and frame budgets, layout without overfull boxes, XeLaTeX fonts and Korean (xeCJK), color, figures and screenshots, Overleaf source hygiene, speaker notes, and a build-and-QA loop with a contact sheet. Use when writing, editing, reviewing, or building a Beamer deck, theme, or storyboard. A course skill supplies course facts and the frame skeleton; this skill supplies the craft. Not for HTML or PowerPoint decks, posters, or papers.
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 end-stage QA on a finished figure and table set. Checks callouts, numbering, and cross-references, whether numbers in the text match the exhibits, self-contained captions and statistical notes, accessibility and production quality, and links to the SI and replication package. Values that could only be read off an image are marked for author verification rather than estimated. Use when preparing a submission. Design guidance during drafting belongs to figures and tables.
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。
Launches and configures the standalone presubmit CLI, an API-driven adversarial peer-review pipeline with 30-plus stages, including Red Team finders, Blue Team defence, verification cascade, legal pass, and copyedit, producing one consolidated report on disk. Verifies installation and API key, chooses the output location and smoke, standard, or custom mode, launches the run, and reports the result path. Use when the user asks to run presubmit, wants a deep unattended pre-submission audit, or needs a math or replication-code audit. Everyday self-audits go to paper-review-lite, and external refereeing to journal-review.
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。
Reads model uncertainty from token log-probabilities. Covers collecting logprobs and aggregating multi-token labels, confidence tiers and margins for triage, calibration assessment with ECE, Brier scores, and reliability diagrams against human labels, responsible downstream use of confidence, and reproducibility archives. Use when the user asks about classifier confidence by item, logprobs, top-k tokens, calibration, ECE, Brier, reliability diagrams, or routing low-confidence cases to human review. Codebook and validation design go 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。
Reads a model's own uncertainty from token log-probabilities: collecting and aggregating label logprobs, confidence tiers and margins for triage, calibration against human labels (ECE, Brier, reliability diagrams), downstream use, and reproducibility. Use for per-item classifier confidence or routing low-confidence cases to human review; codebook and validation design go 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。
Resolve LinkedIn profile URLs from name + company with strict identity validation to avoid false positives.
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。
Convert a Clay table configuration into local Deepline scripts. Handles extraction (MCP or script), documentation, action mapping, script generation, and parity validation against Clay ground truth.
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。
GTM prospecting, enrichment, outreach, and Deepline Play work/audits. Providers: adyntel,affinity,ai_ark,akta,allegrow,amplemarket,apify,attention,attio,aviato,bettercontact,bigquery,bluesky,bounceban,brave,browserbase,builtwith,cbinsights,clay,clickhouse,cloudflare,contactout,contextdev,crunchbase,crustdata-v3,customer_db,databricks,dataforseo,datagma,dealroom,deepline_ip_to_company,deepline_native,deeplineagent,discolike,dropleads,edges,emailbison,emailguard,enformion,enigma,exa,findymail,firecrawl,fireflies,firmable,forager,fullenrich,fundable,generic_http,getleads,gong,google_ads_audiences,google_workspace,govfiles,grain,hackernews,harvestapi,heyreach,hubspot,hunter,icypeas,instantly,intercom,ipqs,kaspr,kernel,leadmagic,lemlist,limadata,linkedin_ads_audiences,luma,lusha,meta_audiences,millionverifier,nooks,notion,openmart,opensosdata,openwebninja,outreach,parallel,peopledatalabs,pitchbook,podscan,polymarket,postgres,predictleads,prospeo,quickenrich,rapidapi,redshift,rocketreach,salesforce,salesforge,….
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。
This skill should be used when validate-chunk reports test failures specifically, when the user says "tests are failing", "figure out what's wrong with the tests", "fix the failing tests", or when test assertions don't match expected output after chunk implementation. Triages whether the test or the implementation is wrong and fixes the correct one.
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。
Assess review findings against the target's governing contract, evidence, and consequence without imposing a lens-specific verdict, schema, or gate. Use when classifying a candidate concern in a code or change review.
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 the primary outcome is authoring, rewriting, summarizing, or validating user-facing documentation without implementation comparison or required source attribution, including READMEs, tutorials, API docs, GitLab Markdown, source-faithful summaries, and audience-specific prose.
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。
Single-pass SOLID review — dispatches one reviewer over the complete ruleset, emitting the fixed candidate schema.
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 the user asks what to do next, asks for a plan under uncertainty, challenges stalled or low-trust work, requests multiple possible approaches before action, or needs an evidence-first loop that uses RT-ICA-style prerequisite checks, adversarial review, subagent coordination, real validation, and durable concern tracking.
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 working with uv — this skill's uv policy and required overrides to Astral's official guidance. Pair with `astral:uv` (if installed) for general usage.
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。
Validates and corrects Python shebangs and PEP 723 inline script metadata by applying four shebang-selection rules. Use when auditing or fixing shebangs in Python files — choosing between plain python3 and the uv shebang for standalone scripts with external dependencies, adding or removing PEP 723 metadata blocks to match actual import requirements, checking execute bit presence, or avoiding redundant transitive dependencies when typer is declared.
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。
Specialist skill for Python data engineering — pandas, polars, DuckDB, numpy, ETL pipelines, tabular data ingestion, and notebook-to-module extraction. Use when working with dataframes, data validation at ingress boundaries, merge/join operations, typed column contracts, or choosing between pandas vs polars vs DuckDB for a data task.
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, improve, or represent processes across a file or directory by delegating to process-siren. Uses the same semantic model and validation loop as single-process work; Mermaid is produced when it is the useful concise representation.
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 debugging software, investigating incidents, diagnosing flaky tests, or analyzing performance regressions — enforces structured observation recording with evidence IDs, causality validation, and verification gates to prevent correlation-causation pollution. Use when an agent might otherwise summarize or speculate instead of reporting observed evidence.
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。
Provides Hatchling build backend guidance for Python packaging — use when configuring pyproject.toml metadata, build targets (wheel, sdist, binary), file selection with git-style globs, build hooks, metadata hooks, version management (code/regex/env sources), editable installs, the hatch-vcs plugin, plugin development, build environment setup (UV/pip/Cython), setuptools migration, or troubleshooting Hatchling errors. Covers PEP 517/518/621/660 standards and context variable interpolation.
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 building CLI applications with Typer — creating commands, defining arguments and options with enum restrictions, path validation, date and UUID types, composing subcommands, testing with CliRunner, or using advanced features like colored output, progress bars, shell autocompletion, and version callbacks.
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 working with ty — this skill's ty policy and required overrides to Astral's official guidance. Pair with `astral:ty` (if installed) for general usage.
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。