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Select, run, and report proportional Proto UI validation for a completed change. Use before review, handoff, commit, or pull request updates, and after resolving validation failures. Cover focused evidence first, then affected graph, types, docs, packaging, consumers, release, or repository checks according to risk.

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Establish Proto UI repository state, live contributor authority, Agent comprehension scope, task risk, and authorization before work begins. Use at the start of a development session, before autonomous task selection, after a material repository update, or when permission or assessment state may have 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。

Use when the operator asks what Odylith can do for a repo, requests an advisory repo demo, or asks for the clean scenario-aware show-me output.

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。

Fix silent incorrect results in RFDB Datalog queries that share a variable between two edge atoms on opposite ends (e.g. self-join patterns like `edge(M, C, "T1"), edge(C, M, "T2")`). Use when: (1) a Datalog rule returns rows that look structurally wrong (e.g. a self-loop rule reports hits but pinning M to each reported id refutes the edge existence), (2) Cypher `MATCH (m)-[:R1]->(c)-[:R2]->(m)` returns the same bogus rows (engine-level bug, not Datalog-specific), (3) results change when you bind one variable explicitly vs. leave it free. Root cause: hash- join fast path in `eval_edge_hash_join` / `eval_incoming_hash_join` unconditionally rebinds the non-key Term::Var without checking whether the variable is already bound in the current row.

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Create standardized charts and visual assets from analytics query results. Prefer dependency-free plain HTML/CSS/JavaScript/SVG for interactive exploratory charts; use local plotting tools such as matplotlib, seaborn, or Plotly when static/report-ready exports or specialized charting libraries are more appropriate. Use for trends, comparisons, distributions, report assets, CSV-to-chart work, and presentation-ready data visuals.

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 an explorable HTML report of Claude Code session usage (tokens, cache, subagents, skills, expensive prompts) from ~/.claude/projects transcripts.

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。

Report, list, or manage bug reports as GitHub issues

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 standardized charts and visual assets from analytics query results. Prefer dependency-free plain HTML/CSS/JavaScript/SVG for interactive exploratory charts; use local plotting tools such as matplotlib, seaborn, or Plotly when static/report-ready exports or specialized charting libraries are more appropriate. Use for trends, comparisons, distributions, report assets, CSV-to-chart work, and presentation-ready data visuals.

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。

Clone, create, fork or release a GitHub repository.

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。

Install and verify the msmodeling development environment. Use when the user explicitly asks to install msmodeling dependencies, set up this repository, create `myenv` with `uv`, install this repository's `requirements.txt`, set project `PYTHONPATH`, or configure `HF_ENDPOINT`; if the user only says to install an environment, ask whether they mean msmodeling dependencies before proceeding.

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。

分析 AscendC 算子中自实现的软同步代码，判断其所属同步语义（核间 barrier/set-wait、卡间 barrier/signal），并接入 mssanitizer 的 Sanitizer 接口（sanitizer_report.h）上报软同步语义，避免 racecheck 对软同步误报。触发：算子存在自实现软同步（GM 地址轮询、flag 计数、跨核/跨卡信号等）需要配合 mssanitizer 检测、需要上报软同步语义、需要判断某段同步代码属于哪种同步语义时。

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 a report about a video

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 modern, version-aware C# for .NET repositories. Use when choosing language features across C# versions, especially C# 13 and C# 14, while staying compatible with the repo's target framework and `LangVersion`.

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 a repo-root `.editorconfig` to configure free .NET analyzer and style rules. Use when a .NET repo needs rule severity, code-style options, section layout, or analyzer ownership made explicit. Nested `.editorconfig` files are allowed when they serve a clear subtree-specific purpose.

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Run cross-experiment competition strategy synthesis. Invokes the competition-strategist agent which loads daily_reports/* + all SESSION_NOTES.md / claudeSummary.md / submit/SUBMISSIONS.md / KAGGLE_DIRECTION.md in parallel and proposes next moves. Use weekly or at inflection points.

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。

Efficiently accumulate and retrieve competition knowledge in the knowledge/ stock layer (atomic pages + INDEX). Distill reusable insights from the daily-report flow and promote them, deduplicating and keeping things current. Use when an insight emerges, for weekly cleanup, or to recall past knowledge.

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。

Git-native issue tracking (beads), coding guidelines, knowledge injection, and spec-driven planning for AI agents. Drop-in replacement for bd/Beads with simpler architecture. Use for: tracking issues/beads with dependencies, creating bugs/features/tasks, planning specs, implementing features from specs, code reviews, committing code, creating PRs, loading coding guidelines (TypeScript, Python, TDD, golden testing, Convex, monorepo patterns), code cleanup, research briefs, architecture docs, agent handoffs, and checking out third-party library source code. Invoke when user mentions: tbd, beads, bd, shortcuts, issues, bugs, tasks, features, epics, todo, tracking, specs, planning, implementation, validation, guidelines, templates, commit, PR, pull request, code review, testing, TDD, test-driven, golden testing, snapshot testing, TypeScript, Python, Convex, monorepo, cleanup, dead code, refactor, handoff, research, architecture, labels, search, checkout library, source code review, or any workflow shortcut.

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。

Automate a professional LinkedIn presence end-to-end using Claude: daily posting with pillar calendar, engagement with anti-detection rules, DM triage, reporting, and growth tracking. Use when the user wants to automate LinkedIn, manage a profile with AI, create a content strategy, or set up engagement automation. Triggers on: "automate my LinkedIn", "LinkedIn automation", "automated LinkedIn", "LinkedIn bot", "LinkedIn scheduling", "LinkedIn AI management", "LinkedIn content calendar", "LinkedIn engagement strategy", "build a LinkedIn presence", "can Claude manage my LinkedIn", "how do I automate my LinkedIn", or any request involving systematic LinkedIn content creation, audience growth, or profile management. Covers: identity/TOV setup, pillar calendar, post creation, engagement rules, anti-detection, scheduled task creation, and weekly reporting.

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 the changes since a fixed point (commit, branch, tag, or merge-base) along two axes — Standards (does the code follow this repo's documented coding standards?) and Spec (does the code match what the originating issue/spec asked for?). Runs both reviews in parallel sub-agents and reports them side by side. Use when the user wants to review a branch, a PR, work-in-progress changes, or asks to "review since X".

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。

The Arbor executor workflow: clone the repo from the git bundle your brief hands you, implement and evaluate exactly ONE hypothesis with the file tools, then report structured results via worker_complete. Preloaded automatically on arbor-executor task workers. Never touch the held-out test split.

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。

Intake for an autonomous research run (Arbor). Use when the user wants to optimize a metric in a repo over many isolated experiments — 'optimize this benchmark', 'improve the model F1 overnight', 'beat the leaderboard'. Discovers the repo/eval/splits, measures the baseline, confirms a Research Contract, then emits a ready-to-send /auto-research command. Does NOT start the run itself.

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Review a pull request or contribution deeply, explain it tutorial-style for a maintainer, and produce a polished report artifact such as HTML or Markdown. Use when asked to analyze a PR, explain a contributor's design decisions, compare it with similar systems, or prepare a merge recommendation.

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。

Investigates phishing email incidents from initial user report through header analysis, URL/attachment detonation, impacted user identification, and containment actions using SOC tools like Splunk, Microsoft Defender, and sandbox analysis platforms. Use when a reported phishing email requires full incident investigation to determine scope and impact.

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 need to know where a piece of reporting actually came from — the user gives an article URL and asks "where does this come from?", "who is the original source?", "is this just a rewrite?", or another skill needs the provenance chain before grading or citing a report. Follows an article's links and named attributions back to the originating vendor, government, victim or actor document and returns the chain, the primary's stated access and confidence language, publication dates, and every hop it could not resolve. Deterministic — a script or the Liberty91 platform resolves the chain; the model does not reconstruct it from memory. Invoked by /quality-of-information-check, /threat-actor-profiling, /campaign-tracking and /intelligence-writing. Works on any URL with no API key.

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。