1,192 件 ・ 関連度順
概要と使いどころ
Scientific research engine v6.0 NEXUS — adversarial review (Reviewer 2), 32 quality gates, tree search, serendipity tracking, confounder harness, cross-session learning. Use for ANY scientific analysis, hypothesis testing, data validation, literature review, or task where correctness > speed.
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。
Scientific research engine for hypothesis testing, literature gap analysis, experimental validation, and data-driven discovery. Enforces adversarial review (Reviewer 2), 32 quality gates, tree search over hypotheses, confounder harness for quantitative claims, and serendipity detection. TRIGGER when: user asks to analyze scientific data, test hypotheses, validate findings, search for research gaps, design experiments, or investigate results. DO NOT TRIGGER when: pure code review, documentation writing, devops tasks, general conversation, or non-scientific data transformation.
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。
6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.
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。
Root-causes one specific mistake the agent just made, such as not committing despite instructions to, by checking it against the loaded AGENTS.md, CLAUDE.md, skills, and Beads memories, delegating the investigation to a separate agent where the harness supports one; proposes concrete context improvements, and lets the user choose which to apply and how to remediate. Use when the user invokes `/learn` or `$learn`, with or without a description of the mistake, including `/learn /qs`-style shorthand naming the corrective command. For a broad end-of-session documentation sweep, use `documentation-updates` (`/reflect`) instead.
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。
Experimental/manual harness canary procedure. It records benchmark manifests and scores but does not provide a fully automated runner.
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 negotiating a file-based sprint contract between the generator (developer) and evaluator before code is written. Translates a product-level spec into testable acceptance criteria with rubric tie-ins. Triggers on /mk:sprint-contract, "draft a sprint contract", "negotiate scope for sprint", or before any harness-driven sprint kicks off.
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。
Load a skill profile to switch active plugin set. Use when user wants to focus on a specific workflow (web-app/data-eng/harness-dev/minimal) and reduce skill enumeration block size per
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。
On explicit /homework invocation, analyze the current and linked previous sessions, extract mistakes (찐빠), and report them via omcustom-feedback with a confirmation gate. Auto-activation on session cleanup/session-end signals is OPT-IN (default OFF) — requires an explicit project/user directive. Use when explicitly auditing recent work for harness gaps.
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。
hada.io RSS feed monitoring for AI agent/harness articles with automated /scout analysis
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。
Quantitative agent evaluation using 4-metric framework (correctness/step_ratio/tool_call_ratio/latency_ratio) with ideal trajectory annotation and capability-categorical taxonomy. Use when measuring agent efficiency, comparing agent variants, or gating new agents through correctness→efficiency phases. Complements harness-eval (SE benchmarks) and evaluator-optimizer (qualitative rubric).
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。
6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.
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。
How to test the peoplereadme CLI end-to-end, including the fidelity harness (eval/calibrate) with a synthetic persona fixture and live LLM calls.
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 GenAI and agentic apps with genai-tk (tclatos/genai-tk), a YAML-driven IoC toolkit over LangChain, DeepAgents, DeerFlow, and Prefect. Use this skill whenever the user mentions genai-tk, genai_tk, `cli init`, `cli agents run`, `LangchainAgent`, `create_harness`, `get_llm`, `global_config()`, `model_id@provider`, `agents:`/`agent_defaults:` profiles, `RetrieverFactory`, `cli workflow`, or BAML extraction. Also trigger for app_conf.yaml, GENAITK_PROFILE, CodeAct, OpenSandbox, decision models, or "the toolkit".
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。
Update Codex's pinned `v8` / `rusty_v8` versions, validate the release-candidate path, and investigate failed V8 canary or artifact builds. Use when asked to bump V8, update `rusty_v8` artifacts, prepare or validate a V8 release candidate, check `v8-canary`, or diagnose why a V8 version update no longer builds.
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。
Breaking changes
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。
Diagnose GitHub bug reports in openai/codex. Use when given a GitHub issue URL from openai/codex and asked to decide next steps such as verifying against the repo, requesting more info, or explaining why it is not a bug; follow any additional user-provided instructions.
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。
Guide for testing Codex TUI interactively
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。
Pushing GitHub Actions changes, resolving push rejection, requesting upload exceptions.
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。
Babysit a GitHub pull request after creation by continuously polling review comments, CI checks/workflow runs, and mergeability state until the PR is merged/closed or user help is required. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and keep watching open PRs so fresh review feedback is surfaced promptly. Use when the user asks Codex to monitor a PR, watch CI, handle review comments, or keep an eye on failures and feedback on an open PR.
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。
Test authoring guidance
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。
Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations.
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 how to build with OpenAI products or APIs, asks about Codex itself or choosing Codex surfaces, needs up-to-date official documentation with citations, help choosing the latest model for a use case, or model upgrade and prompt-upgrade guidance; use OpenAI docs MCP tools for non-Codex docs questions, use the Codex manual helper first for broad Codex self-knowledge, and restrict fallback browsing to official OpenAI domains.
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 and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugin ordering and availability metadata, or update an existing local plugin during development with the CLI-driven cachebuster and reinstall flow.
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 final code review on a pull request
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。