「how」の検索結果

Show skill-bus overview, status, and available commands. Quick reference for all skill-bus features.

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List all active skill-bus subscriptions across global and project scopes, showing merge status, insert-level and subscription-level conditions, effective condition stacking, and what would fire for each skill.

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Research what Chinese internet users actually said in the last 30 days across Weibo, Xiaohongshu (RED), Bilibili, Zhihu, Douyin, WeChat public accounts, Baidu and Toutiao: engagement-weighted ranking, cross-platform clustering, cited Markdown/JSON/HTML reports. Also shows today's cross-platform hot-search board (全网热榜, --hot) and can add Hacker News / GitHub / Reddit or an installed upstream last30days for X/YouTube/TikTok.

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Use when evaluating markets through Howard Marks-style second-level thinking, credit cycles, risk premiums, market psychology, and defensive versus aggressive posture.

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Redesign GitHub README homepages or create project-native pure SVG, hybrid SVG-composed PNG/WebP, and opt-in animated GIF assets. Use when a user asks to beautify, redesign, rebrand, visually upgrade, simplify, or audit a GitHub README; create only a hero, section headers, diagrams, badges, motion graphics, showcase modules, or other README assets; or turn a repository homepage into a cohesive visual story. If whole-README work versus asset-only work is unclear, ask which scope the user wants. For hero-like assets where pure SVG and generated raster material are both viable, explain the tradeoffs and confirm the implementation before creating the asset.

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Explore an existing PostgreSQL database before answering questions about its data or writing SQL. Use this skill whenever a user asks for a query or a data-backed answer against an unfamiliar schema (counts, missing or failed records, recent changes), asks where a business concept lives, or asks how tables, joins, views, routines, triggers, RLS, or extensions work. Find the relevant objects with read-only pg_catalog queries, then request approval before inspecting data-derived statistics or rows. Not a schema-design or migration guide.

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Standing rules for how any role in an OpenOPC company coordinates with its peers — work-item discipline, messaging, meetings, and blocking collaboration. Loaded only in company_mode.

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Teaches how to reason from a transaction protocol’s rules to a working model. Use when analyzing a concurrency-control paper, spec, or algorithm to identify state, metadata, invariants, operation rules, examples, counterexamples, guarantees, false aborts, unsafe commits, or tradeoffs before inspecting concrete traces.

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Calculate the relative contribution of different factors to a response variable using R² decomposition. Use when you need to quantify how much each factor explains the variance of an outcome.

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Detect long-term trends in time series data using parametric and non-parametric methods. Use when determining if a variable shows statistically significant increase or decrease over time.

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Diagnostic guide for RL-based post-training of language models (GRPO, PPO, REINFORCE, DPO). Use proactively when debugging a training pipeline that shows no improvement, loss anomalies, reward stagnation, NaN gradients, or other unexpected behavior during reinforcement learning fine-tuning. Work through all pipeline stages — reward, advantages, log-probs, loss, generation/decoding — before concluding the diagnosis is complete; stopping after one or two fixes is a common failure mode. Covers log-probability math, advantage estimation, numerical stability, reward processing, and generation/decoding pipeline issues.

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Reference for the TRL (Transformer Reinforcement Learning) library codebase. Use proactively before reading or editing any file under `trl/` so you have the intended contracts and invariants in mind, not just what the current code says. Covers trainer hierarchy (SFT, DPO, GRPO, KTO), shared utility functions (selective_log_softmax, decode_and_strip_padding, padding helpers), configuration system, model wrappers, and how data flows through any TRL trainer.

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Methodology for clause-by-clause review of a contract against a structured deviation policy ("playbook"). Covers how to walk a playbook, locate the matching provision in the contract, apply rule types (max-value, must-be-present, must-be-absent, acceptable-set, must-have-feature), classify the result (ok / risk / reject), choose the prescribed action, and ground each finding in a verbatim excerpt. Use whenever reviewing any contract — NDA, MSA, vendor DD questionnaire, lease, DPA — against a structured rules-based playbook.

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Reference for the standard clauses found in commercial non-disclosure agreements (mutual and one-way) — what each clause does, the surface forms it appears in, and how to recognise it in unfamiliar drafting. Use when reviewing, comparing, or extracting provisions from any confidentiality / NDA / mutual NDA / standstill-and-confidentiality agreement.

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Use when implementation is complete, all tests pass, and you need to decide how to integrate the work.

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Use when starting any conversation. Establishes how to find and use skills, requiring skill invocation before any response.

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For a repository in the babysitter-users catalog, locate its babysitter processes and any committed runs (.a5c/runs/<runId>/) and perform a retrospective on a chosen run -- what went well, what failed, process suggestions, quality of effect design, breakpoint patterns -- mirroring the /babysitter:retrospect workflow but applied to an external repo. Invoke when asked to "retrospect on repo X's run", "analyze how someone else used babysitter", or "review an external babysitter run".

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演讲与分享教练。基于Patrick Winston（MIT AI教授）的How to Speak方法论，帮助准备线下培训、技术分享、B站教程视频等演讲场景。当用户提到"演讲"、"分享"、"培训"、"讲课"、"PPT演讲"、"开场"、"结尾"、"如何讲"、"演讲结构"时使用此技能。

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Explain any topic, code, concept, or error tailored to a specific audience's level of understanding. Use this skill whenever the user says 'explain like I am', 'ELI5', 'explain this to my', 'break this down for', 'dumb it down', 'simplify this for', or asks you to explain something to a specific person or audience type (e.g., 'explain this to a manager', 'how would I explain this to my mom', 'make this understandable for a 5th grader'). Also trigger when the user mentions wanting to understand something at a particular level, or asks for an explanation targeting a non-technical audience. Even partial matches like 'explain to my wife' or 'tell my boss' should trigger this skill.

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。

Score an Instagram profile out of 100 against a 12-part rubric and rewrite the parts that lose points - name field, bio, link, highlights, pinned three, grid. Use when the user says "optimize my profile", "fix my bio", "score my Instagram", "why don't people follow me", or pastes their profile and asks how it reads.

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Write Instagram DMs that get replies - the keyword delivery, the first message to someone who engaged, the collab pitch, and the two follow-ups. Use when the user says "DM this person", "what do I send them", "outreach message", "how do I follow up", "pitch this brand", or is reaching out to someone specific.

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Strip the machine fingerprint out of any draft - em dashes, AI slop words, invisible watermark characters - and score it against a five-check detection panel before it goes out. Use whenever text needs to sound human, when the user says humanize, "does this sound like AI", "remove the em dashes", "de-slop this", "this sounds like ChatGPT", or before any caption, script, comment, reply or DM is shown to the user.

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Go and find the reels that are actually working right now in the user's niche, rank them by how far each beat its own account, name the hook formula each one used, and turn that into a swipe file they can shoot from. Use when the user says "find viral videos", "what's working right now", "what are people posting in my niche", "reverse engineer this account", "build me a swipe file", "why is this reel doing numbers", or asks what to make next and has no evidence to answer with.

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Case studies of American screenwriting (美国电影剧作案例) — Mei Feng's readings of the Hollywood Western (value pairs, four oppositions), screwball comedy (strong heroine, buddy vs adversary two-hander), Billy Wilder (disguise, Lubitsch touch, meet-cute), Hitchcock and his writers, Adaptation, Mildred Pierce (eight drafts, woman's film + noir), Thelma & Louise ("but" method, mirror pair), Fargo (Coen method), Good Will Hunting (five-part structure), American Beauty (POV strategy, genre mix), Eternal Sunshine (1:5:1, image motifs); Walter's American Graffiti; the Hollywood masterclass films (Bourne, Guardians, Zootopia, Thor); Hoxter's Winter's Bone and Jojo Rabbit; Snyder's Miss Congeniality; Field's Chinatown. Use when you want a worked example of how a specific American film's script is built, when analysing a film for structure, character or theme, or when looking for a model to imitate for a Western, screwball, noir, buddy, coming-of-age, black comedy or non-linear romance.

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。