「did」の検索結果

Use @aleabitoreddit ("Serenity")'s full mention archive (built by the follow-aleabito skill) to anticipate where her attention is moving and generate candidate ideas in her style. Two modes — (1) RADAR reads the live mention data for attention momentum (which tickers she is heating up on, new entrants, conviction core, theme rotation) via scripts/radar.js; (2) GENERATOR applies her empirically-mined patterns (theme-rotation logic, selection signature, catalyst playbook) to propose her likely next focus. Every candidate is gated through the serenity-method checklist. This is a CANDIDATE GENERATOR + CHECKLIST, never an oracle or buy/sell signal. Trigger on "what is Serenity ramping on / her next pick / aleabito radar / predict her next move / generate ideas like her / 她下一个可能看什么".

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 candidate CVs on a 100-point scale against a Job Description. Use this skill when the user wants to evaluate, score, rank, or screen candidate CVs/resumes against a JD. Also trigger when the user mentions 'review CV', 'screen resume', 'rate candidates', 'shortlist', or any context involving matching resumes to job 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。

Inspect and explain bmad-loop run artifacts under `.bmad-loop/runs/`. Use for live health checks ("is it stuck?", "is the loop alive?", "what is the agent doing?") and post-run forensics ("what happened?", "why did story X fail?", "where did the tokens go?"). Also triggers on "monitor/check/summarize the loop", "why did the run pause?", Vietnamese "theo dõi bmad loop", "phân tích run", "đọc log của run", and equivalent requests in other languages. Use only for bmad-loop's own run artifacts, not application runtime logs or CI-provider build logs.

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。

Pick among candidate outputs (code, configs, plans) by running them on diverse inputs and clustering by behavioural fingerprint, rather than by textual aggregation or log-probability. Activates when an executor returns multiple plausible candidates that need disambiguation, when output-majority voting would be the default choice, or when reviewing generated code that has not yet been validated. The 2026 evidence (Semantic Voting, arxiv 2605.08680v1) is that any execution-based selector dominates output-majority voting by 19-52pp; sketch-generated inputs beat random fuzz by 11.3pp. Triggers: "pick the best candidate", "majority vote on code", "select from N samples", "validate the generated output", "behavioural verification".

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。

Batch Resume Screening — score and rank candidates 0-100 against job requirements, flag red flags (job hopping, gaps, skill mismatches), output Pass/Phone Screen/Skip recommendation per candidate

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。

Deep Single-Candidate Scoring — evaluate one candidate across 5 dimensions (skills match, experience relevance, culture fit signals, growth potential, red flags) with final 0-100 score and hire/no-hire signal

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。

Head-to-Head Candidate Comparison — two candidates side-by-side across skills, experience, culture fit, salary expectations, references, hire-or-no-hire 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。

Show recent Claude Code sessions across all projects, so the user can re-enter work after a reboot or context switch. Lists per session the absolute project path, a short summary, where the session stopped, last activity, turn count, git branch, model, and a ready-to-paste resume command; can search sessions by what was said in them, flag projects with uncommitted or unpushed work, and re-open every session at once in new terminal tabs. Use when the user asks "what was I working on", "which projects did I touch recently", "where did I leave off", "list my recent sessions", "how do I get back into that session", "open all my sessions again", "which session did I fix X in", "where do I still have uncommitted work", or invokes /recap. Reads only local files; the default run is instant and offline.

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。

"Apply Difference-in-Differences (DID) to estimate causal treatment effects by comparing changes in outcomes between treatment and control groups. Use this skill when the user evaluates policy interventions, natural experiments, or regulatory changes, needs to test parallel trends, or when they ask 'did this policy work', 'how do I identify causal effects without randomization', or 'what is the treatment effect'.".

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。

Skippable Phase 0 of the Gaia evidence verification pipeline. Use only on declared need for higher-quality Stage-2 evidence: a promotion candidate, or a skill that gaia-meta-sweep flagged as under-evidenced. Given a skill id, source repo/context, and needed evidence types (benchmark-result, arxiv, peer-review, social-signal), it runs Firecrawl searches, scrapes top hits to markdown, and appends discovered sources into Phase 1 inputs. Missing benchmark evidence is recorded as benchmark-source candidates first, not direct scoring rows. Trigger phrases: "discover evidence", "find new evidence", "Phase 0", "run ev-discovery", "search for benchmarks/papers", "under-evidenced", "promotion candidate needs stronger proof". This is the ONLY ev-* skill that searches the web for NEW evidence; other ev-* skills aggregate or verify known sources. Requires Firecrawl to be usable (`firecrawl --status` authenticated OR FIRECRAWL_API_KEY set); skips gracefully when neither holds.

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。

Construct a compelling job offer. Use when the user says "write an offer letter", "help me make an offer", "how should I structure this offer", "what should we include in the offer", "how do I explain the equity", "the candidate is negotiating", or wants to convert a candidate from interested to accepted - even if they don't explicitly say "offer letter". Also use when a recruiter needs to explain comp philosophy or total compensation to a candidate.

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。

Structure and run a post-interview candidate debrief. Use when the user says "run the debrief", "how do we decide on this candidate", "let's calibrate", "we need to make a hire/no-hire call", "prep the debrief meeting", or wants to structure the decision-making process after interviews are complete - even if they don't explicitly say "debrief". Also use when a recruiter wants to prevent groupthink or HiPPO effect (highest paid person's opinion) from dominating the decision.

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。

Turn Caveman's exact report-only repository observations into an operator-chosen optimization candidate with a paired baseline/candidate evaluation. Use when the user asks to inspect an optimization observation, evaluate a candidate change, or act on the current Caveman optimization report. Require a logged-in Caveman CLI connection and explicit approval; never infer money or actuation from a profile.

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。

Fetch external sources (web / arXiv / GitHub) and turn them into scanner-gated wiki CANDIDATES — never canonical pages. Every fetched byte is DATA; url-guarded, size-capped, redirect-re-validated, injection-scanned, and secret-scrubbed before a candidate is even created. Requires network. NOT for capturing local knowledge (see mk:wiki); NOT for one-shot page-to-markdown (see mk:web-to-markdown).

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。

Evaluates candidate profiles against a job description and hiring criteria. Use when the user provides candidate profiles plus a role definition and wants consistent screening, matched requirements, and an interview 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。

The case-law chronicle of this repo's major failures — symptom, root cause, evidence, status, and the rule each battle minted — so nobody re-fights a settled battle or re-proposes a rejected fix. Use when a bug/incident looks familiar ("didn't this happen before?"), when you're about to propose an approach that may have been tried and rejected (e.g. shipping a migration from a package), when someone asks "why does rule X exist" / "why is done-by-lgtm a thing" / "what was incident 988", or when a bug-archaeology run hits the shallow-clone wall and needs the pre-June-2026 history. Trigger phrases: "has this broken before", "why can't packages ship migrations", "why did graduation fail", "why is the green-build lesson a rule", "what did we learn from incident NN". (For a live "build is green but the app is broken" symptom, start at crouton-diagnostics-index. NB keep this description free of any space-then-hash sequence — an unquoted YAML scalar treats space-hash as a comment start and silently truncates.)

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。

LLM-judge stage of an alpha-mining pipeline: take a miner's deterministic candidate list, keep only real signal, drop noise, and propose additions to that miner's curated note. Use after any zero-token detector run. Reads only the candidate digest and the home note, never the corpus. Triggers: "/alpha-judge <miner>", "judge the 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。

4-layer recall procedure (durable → semantic → PostgreSQL → daily notes) before claiming "no context". Invoke when user explicitly asks "do you remember", "what did we discuss about X", "search history". NOT a general assistant — only fires when recall is the explicit task. Drop the prior "use proactively" rule. Triggers: "do you remember", "what did we discuss", "when did", "find that conversation about".

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 and improve existing Ozor.ai video prompts for better output quality. Use this skill whenever the user has an Ozor prompt that produced mediocre results, wants to improve a video prompt, needs help debugging why a video didn't turn out well, or wants expert feedback on their prompt before running it. Trigger on phrases like 'improve this prompt', 'optimize my Ozor prompt', 'why did my video look bad', 'fix this prompt', 'make this prompt better', 'review my Ozor prompt', 'the video didn't turn out right', 'prompt feedback', 'better results from Ozor', or any request to refine, debug, or enhance an existing Ozor video prompt.

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。

OpenClawのリリース候補を人が試すために、隔離コピーなどの検証環境を準備し、テストの進行とフィードバック整理を支援します。公開前の報告確認も扱います。

• Gatewayの隔離コピーで検証したいとき
• 操作画面ごとに人がテストしたいとき
• 公開前に検証報告を確認したいとき

TenorやGiphyのGIFを検索・プレビュー・保存するスキル。端末上で候補を選び、指定時点の静止画や複数フレームを並べたPNG画像も作れます。

• 話題や反応に合うGIFを探したいとき
• GIF候補を確認して保存したいとき
• 指定時点の静止画を切り出したいとき

候補の探索や比較を繰り返し、試行の根拠と判断を追記型の台帳に残すスキル。前回との整合性や再検証の結果を確認し、実取引や本番操作へ進む条件を明確にします。

• 試行を重ねて候補を比較したいとき
• 判断の根拠を記録したいとき
• 新情報で前回の採用候補を見直したいとき

自社の強みや顧客像を基に、比較すべき競合と調査先を選びます。直接競合・周辺競合・目標となる企業に分類し、根拠付きの候補一覧を作って詳しい比較の準備を整えます。

• 競合比較の対象を決めたいとき
• 直接競合・周辺競合・目標企業の分類
• 市場調査レポートの候補を絞りたいとき

プロンプト、正規表現、SQL、小さなコードを、採点基準に沿って繰り返し改良します。AIが候補を作り、失敗例を手がかりによりよい案を探すスキルです。

• 正解例に沿ってプロンプトを改善したいとき
• 合格率で正規表現を調整したいとき
• テストでSQLやコードを改善したいとき