1,253 件 ・ 関連度順
概要と使いどころ
Run quokka's real-device E2E layer (QA strategy layer 5): drive the real qk binary against connected iPhone/Android devices through tmux, apply deterministic verifiers, and write a report. Use when the user asks to "run /qa", "run the device QA suite", "test against the real device", or before a release. Optionally finishes with an advisory exploratory UX pass (layer 6).
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。
Plan, art-direct, generate, edit, and quality-check tactile minimal-zine bitmap imagery from almost any subject: landscapes, portraits, objects, products, architecture, interiors, existing photographs, moods, article ideas, cultural essays, data, reviews, and social-media card series. Use for 小红书图片, Instagram carousels, poetic posters, editorial covers, photo restyling, visual essays, evidence-aware explainers, or any request for aged paper, photocopy, risograph, letterpress, sparse type, and restrained single-ink character. Routes the subject before choosing composition, preserves identity and factual content when editing, saves reproducible prompts, invokes the runtime-native image generator, and audits the real raster output. Do not use for glossy commercial ads, deterministic UI/vector assets, or exact diagram rendering.
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 writing Japanese novels with AI coding agents (Hermes, Claude Code, opencode, goose). File-based, agent-agnostic workflow: settings in TOML, deterministic context packs, validation scripts.
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。
Index a code repository into Cognee's code graph and query it deterministically (callers, impact analysis, paths, endpoints). Use for structural code questions, "index this repo", and checking what a re-index changed.
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。
Creates or improves an Agent Skill by defining a precise trigger boundary, concise procedural instructions, progressive-disclosure references, deterministic helpers, routing evals, and validation evidence. Use when authoring or reviewing SKILL.md, skill metadata, bundled references, or skill packaging. Not for ordinary project documentation or application code.
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。
Designs and runs reproducible evaluations for AI agents, prompts, tools, skills, and model-backed workflows using realistic datasets, isolated baselines, objective assertions, rubric grading, trajectory analysis, cost/latency tracking, and regression comparison. Use when measuring agent quality, optimizing skill triggering, comparing prompts or models, or gating an AI feature release. Not for ordinary deterministic unit tests.
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。
Handle small bureaucratic tasks — jury duty responses, parking tickets, passport renewals, DMV forms, permit applications, and other government or administrative paperwork.
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。
Detect non-deterministic (flaky) tests by reading CI run logs or test result history. Aggregates pass rates per test, identifies intermittent failures, recommends quarantine or fix, and maintains a flaky test registry. Best run during Polish phase or after multiple CI runs.
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。
Generate minimal, abstract, UI-inspired illustrations for software product features — as deterministic SVG. Use when asked for a feature illustration, product illustration, spot illustration, empty-state graphic, feature-card artwork, docs or developer-portal illustration, or a consistent illustration set/family for a product. Also use to turn a feature description or a product screenshot into an abstract UI illustration, to add a new illustration to an existing family, or to retheme an existing set.
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 a Meta-Harness-style optimization loop NATIVELY — automatically search over the scaffolding around a FIXED base model (memory, retrieval, context construction, prompt templates, summarization, tool-selection logic) by proposing candidate variants, scoring each on a cheap deterministic eval, and keeping a Pareto frontier of quality vs cost — using native Agent / Workflow / loop tools instead of a standalone Python harness. Use this whenever the user wants to optimize, evolve, tune, distill, or search over a harness, scaffold, prompt system, memory or retrieval policy, context-assembly code, or summarizer while keeping the model fixed; whenever they mention Meta-Harness, harness optimization, scaffold evolution, automatic prompt/memory optimization, an evolutionary or Pareto search over candidate implementations, or "make the harness/agent better without retraining"; and whenever the gain must come from the code AROUND the model rather than the model weights. Reproduces the Meta-Harness paper's method natively, with no claude_wrapper.py and no metered solver 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。
Codify project review rules as deterministic guards — ast-grep structural rules (with TDD-style rule tests) for mechanical checks, REVIEW_GUIDELINES.md for qualitative ones, wired into the project gate / wf verify. Use when the user wants to define review rules, turn a repeated review finding into a lint/guard, or set up ast-grep in a project.
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。
Review a change with an optional workflow contract using deterministic scope, adaptive cross-model reviewers, verified findings, fixes, and a gate. Use standalone or from wf-impl.
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 for insurance-claims operations with Stedi whenever the user asks to submit claims, validate claim payloads, check claim status, retrieve/interpret 277CA or 835 responses, troubleshoot payer/provider enrollment requirements, or build deterministic claims workflows. Trigger this skill even if the user does not say "Stedi" explicitly but mentions 837/835/277CA/ERA, payer IDs, claim rejection codes, claim resubmission, COB, or healthcare clearinghouse automation.
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。
Expert knowledge of Folketrygdloven (National Insurance Act) processing in melosys-api. Use when: (1) Understanding FTRL bestemmelser (§2-5 to §2-13), (2) Debugging yrkesaktiv/ikke-yrkesaktiv/pensjonist processing flows, (3) Understanding trygdedekning options and combinations, (4) Investigating vilkår for each bestemmelse, (5) Understanding medlemskapsperiode generation logic, (6) Debugging frivillig vs pliktig membership determination.
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。
Regulatory compliance guidelines for life insurance policy administration transactions. Use when processing address changes (state-change impacts), beneficiary changes (community property, ERISA, minors), or any transaction that may trigger compliance review. Covers NAIC model regulations, state-specific rules, OFAC screening, and suitability requirements.
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 165+ AI-powered skills for chiropractors and chiropractic office staff. Use when: user asks about patient intake, SOAP notes, insurance billing, HIPAA compliance, appointment scheduling, clinical documentation, practice marketing, or any chiropractic office administration 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。
Deterministic-first mobile policy for tool selection, verification, and escalation.
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。
Amazon Bedrock Automated Reasoning for mathematical verification of AI responses against formal policy rules with up to 99% accuracy. Use when validating healthcare protocols, financial compliance, legal regulations, insurance policies, or any domain requiring deterministic verification of AI-generated content.
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。
Inspect, schema, and fill true AcroForm PDFs using sciClaw's pdf_form_inspect, pdf_form_schema, and pdf_form_fill tools. Use when: (1) checking whether a PDF is a real fillable AcroForm, (2) listing form field names, types, and choices, (3) filling a supported form from structured values, (4) preparing reviewable insurance, legal, administrative, or clinical form drafts, (5) reporting skipped fields or unused input keys after a fill. Do not use for scanned PDFs, OCR tasks, or XFA-only forms.
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。
Schools of historical thought, methodology debates, and the philosophy of history. Covers the major historiographical traditions (Annales, Marxist, feminist, postcolonial, subaltern, world-systems), their methodological commitments, their contributions and limitations, and the enduring debates about objectivity, narrative, and the nature of historical knowledge. Use when evaluating how different historians approach the same subject, understanding why historical interpretations change over time, or assessing the theoretical framework underlying a historical argument.
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。
Understanding and integrating multiple perspectives in historical analysis. Covers historical empathy (reconstructing how past actors understood their world), analyzing events from multiple viewpoints, constructing inclusive narratives that recover marginalized voices, and avoiding presentism (the imposition of contemporary values on past societies). Use when interpreting historical actors' motivations, assessing whose stories are told and whose are silenced, or guarding against anachronistic judgment.
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。
Computational thinking as a problem-solving discipline independent of programming languages. Covers the four pillars (decomposition, pattern recognition, abstraction, algorithm design), computational problem-solving methodology (understand, decompose, generalize, formalize, verify), abstraction levels (from hardware through user interface), modeling and simulation, automata and formal languages (DFA, NFA, regular expressions, context-free grammars, Turing machines), computational complexity classes (P, NP, NP-complete, undecidable), and constructionist pedagogy (learning by building, Logo, Scratch, physical computing). Use when approaching unfamiliar problems, teaching problem-solving strategies, analyzing problem complexity, or bridging between domain knowledge and computational solutions.
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。
MLOps and the production ML lifecycle -- model packaging and serving, CI/CD for ML, experiment tracking, model registries, reproducibility, production monitoring for data and concept drift, retraining pipelines, A/B and shadow deployment, and rollback. Covers batch vs online/real-time inference, REST endpoints, feature stores, data and version pinning, deterministic pipelines, performance-decay detection, and retraining triggers. Use when deploying models to production, serving predictions, monitoring for data or concept drift, setting up ML CI/CD, tracking experiments, managing a model registry, or planning retraining, shadow rollout, and rollback.
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 you have captured session evidence — session-retro / session- observatory-live traces in .planning/patterns/, tool logs, correction records — and want to induce a reusable skill from it. Segments the traces into candidate skill units (an LLM judgment, not a deterministic parse) and decomposes each candidate into a four-part structured spec: workflow structure, execution semantics, and runtime attachments (verification, safety, rollback, state). It emits a spec object, NOT a finished SKILL.md, and hands that spec to skill-forge. It sits between skill-integration (upstream frequency detector) and skill-forge (downstream author). Backed by Agent-Trace-to-Skill Induction (arxiv 2606.06893v1). Triggers on inducing a skill from captured traces, turning a repeated pattern into a skill spec, and preparing evidence for skill-forge.
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。