“did”的搜索结果

Use this skill to analyze an existing PostgreSQL database and identify which tables should be converted to Timescale/TimescaleDB hypertables. **Trigger when user asks to:** - Analyze database tables for hypertable conversion potential - Identify time-series or event tables in an existing schema - Evaluate if a table would benefit from Timescale/TimescaleDB - Audit PostgreSQL tables for migration to Timescale/TimescaleDB/TigerData - Score or rank tables for hypertable candidacy **Keywords:** hypertable candidate, table analysis, migration assessment, Timescale, TimescaleDB, time-series detection, insert-heavy tables, event logs, audit tables Provides SQL queries to analyze table statistics, index patterns, and query patterns. Includes scoring criteria (8+ points = good candidate) and pattern recognition for IoT, events, transactions, and sequential data.

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Builds a high-fidelity interactive legal assessment as a single self-contained HTML artifact. Output includes a live countdown timer, contract review tasks with hover-annotated problem clauses, candidate answer textareas, model answers hidden behind reveal blocks, scenario-based legal memo tasks, strategy and function-building questions, and a pre-submission checklist that encodes the marking criteria. Use when the user needs to (1) assess a legal candidate with a realistic timed exercise, (2) train or onboard junior lawyers using problem sets rather than doctrine, (3) help a candidate prepare for a real take-home assessment they are facing, (4) build educational materials for law students, in-house teams, or compliance training, or (5) produce scenario-based training modules on specific legal topics. Triggers on "legal test", "take-home", "mock exam", "contract redline exercise", "candidate assessment", "legal training exercise", "practice test", or similar phrasing even when informal.

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Screen job applications against requirements and score candidates objectively. Use when a user asks to review applications, evaluate candidates, screen resumes, rank applicants, assess qualifications against a job description, shortlist candidates, or build a hiring scorecard.

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Build a candidate sourcing strategy and outreach pipeline. Use when the user says "where do I find candidates for this role", "build a sourcing plan", "write a cold outreach message", "we're not getting enough applicants", "our pipeline is dry", "help me find passive candidates", "write a LinkedIn message", or wants to increase the volume or quality of candidates in the funnel - even if they don't explicitly say "sourcing strategy". Also use when a recruiter needs to go beyond job postings to find talent.

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Capture, gate, query, and render long-term project knowledge through the gated `mewkit wiki` subsystem. Use to create a wiki, propose/approve candidates (scanner-gated), hand off a skill's terminal artifact as a scanned candidate, recall context, search the FTS index, or list pages. Agents may only PROPOSE candidates; canonical pages are written only via human approve. NOT for short-lived JSON memory (see mk:memory); NOT for fetching external sources (see mk:wiki-research); NOT for HTML snapshots (see mk:wiki-render).

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Post-launch review of a shipped PRD against the brief's success metrics. Did we move the metric? What did the data actually show? Which assumptions held, which broke? Which accepted-gap decisions resolved (confirmed vs. invalidated)? What would we do differently? Triggers on "outcome review", "did it work", "post-launch review", "did we move the metric", "retro the feature", "was it worth it". Produces a Confluence outcome-review page and updates the brief + linked decisions.

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Debrief a finished Otameshi Tenshoku trial from the employer's side: set the observed facts against the completion criteria agreed before the start, keep expectations added afterwards, second-hand or off-the-job observations and the company's own late or missing support apart, settle the hours actually worked separately from any evaluation, and draft feedback to the candidate that states facts, support gaps and what happens next without promising a hire or continuation the company has not decided. Use when a hiring manager, recruiter or small-business owner wants to review a trial, decide what to tell the candidate, or work out the settlement after a shortfall; do not use to judge the candidate's aptitude or predict a hire, to cut pay or demand unpaid rework because of dissatisfaction, to compare candidates, or to send the feedback, pay, or update records on the user's behalf.

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在本地验证候选代码，与基准版本比较，分别报告阻断性失败、诊断性差异和证据不足，并明确列出尚未完成的CI检查。

• 维护者预先检查候选合并请求
• 将失败与准确的基准版本比较
• 区分阻断性失败与诊断性差异

Repair confirmed failures in CI, the automated build and test process, starting with the base branch. Then prepare affected pull requests individually, recording checks for the exact code candidate and unresolved issues for owner review.

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• Reproduce and repair CI failures on the base branch
• Investigate affected pull requests one at a time
• Verify required checks for the exact code candidate

Analyze candidate sites where sugars may attach to proteins, review experimental evidence, and prepare prediction or structural shielding workflows. Supports mutation planning, antibody glycan comparisons, and interpretation of site-specific measurements while distinguishing candidates from confirmed modifications.

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• Find candidate glycosylation sites in protein sequences
• Plan mutations and rescan altered sequences
• Compare antibody Fc glycoforms

Rank possible gene regulatory relationships from bulk or single-cell expression data using GRNBoost2 or GENIE3. The skill guides input preparation, compatible execution, method comparison, and stability checks to help prioritize candidates for further biological investigation.

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• Rank candidate regulator–target gene relationships
• Compare GRNBoost2 and GENIE3 results
• Check network stability across seeds and resampled data

Find opportunities to simplify a codebase by hiding complex behavior behind smaller interfaces. Compare candidates in a visual HTML report, then explore the chosen option through questions about constraints, dependencies, and testing.

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• Finding structural improvements in frequently changed code
• Comparing refactoring candidates visually
• Exploring constraints and testing for a chosen candidate

Generate independent options for design, implementation, explanations, or debugging, then compare them using one shared rubric. Code candidates use separate working copies and undergo independent verification before a winner is presented for review.

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• Comparing several plausible design approaches
• Evaluating independent implementations of the same task
• Comparing explanations or debugging approaches

Turn work history into clear accomplishment statements for performance reviews, promotion cases, and weekly updates. Use Copilot CLI sessions, git commits, and GitHub pull requests to connect each action with its result and supporting evidence.

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• Recovering work accomplishments missing from a personal log
• Preparing evidence for a performance review
• Organizing achievements for a promotion case

Run repeated code improvement experiments with multiple agents. It plans and reviews candidate changes, compares benchmark results, merges candidates that pass rechecking, and records progress until a target, iteration limit, or another stopping condition is reached.

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• Comparing several code improvement ideas with benchmarks
• Running repeated experiments toward a measurable target
• Tracking candidate scores and experiment history

Find LinkedIn prospects, candidates, companies, posts, jobs, services, and groups through Curviate CLI, a command-line tool. Guides filter setup and multi-page retrieval so searches use the intended criteria and incomplete results are identified.

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• Find sales prospects and recruiting candidates
• Identify companies by size and search their job postings
• Find relevant posts and service providers

Turn applications into a ranked shortlist using only the job’s stated criteria, with evidence for each score. Prepare candidate replies, coordinate interview times, and create an onboarding checklist while leaving hiring decisions to the owner.

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• Shortlisting applicants against the job’s stated criteria
• Drafting interview invitations and rejection replies
• Coordinating interview times

Draft an offer letter with salary, equity, bonuses, employment terms, and benefits. Organize first-year total compensation and prepare negotiation notes for the hiring manager, using candidate and compensation data from connected HR systems when available.

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• Draft an offer letter for a hiring candidate
• Summarize salary, equity, and bonuses
• Organize start date, work location, and benefits

Compare a Python SDK release plan or candidate with the previous release to assess compatibility, versioning, and proven regressions. Produce a ship-or-block recommendation, documentation handoff, and key changes for minor releases.

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• Plan the next patch or minor SDK release
• Assess whether a release candidate can ship
• Check compatibility and migration paths

Build a custom branded Qwen Code desktop package from its Tauri shell using a brand identifier and local logo. Apply app naming, icons, and startup screen changes, then report generated installers and their verification hashes.

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• Create a desktop client with a company name and logo
• Build an installer for a supported operating system
• Configure a separate update feed for a branded app

Find unused components, files, translation keys, and other unnecessary code across a codebase. Document every consumer and removal impact in a tracking issue, then turn one maintainer-approved candidate into a focused pull request.

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• Periodically checking for accumulated unused code
• Finding orphaned translation keys
• Recording removal candidates and their impact in an issue

Coordinate a game release from candidate preparation through testing, release approval, and deployment. Assign work to role-based agents and collect quality results, patch notes, launch messaging, and a post-release monitoring plan.

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• Prepare a release candidate and checklist
• Collect testing and release readiness results
• Decide whether a release should proceed

Compare multiple NemoClaw pull requests addressing the same issue. It checks merge eligibility, correctness, quality, and attribution, then produces an evidence-based recommendation for merging or choosing a candidate for further repair.

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• Comparing competing pull requests for one issue
• Checking merge eligibility before ranking proposals
• Choosing a candidate to repair when none is ready

Find information you previously saw on screen and reconstruct past work from locally recorded screenshots, recognized text, and app usage. Summarize research, measure recorded screen time, and provide references that reopen the relevant moments.

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• Finding a page or passage you previously saw
• Reviewing the sequence of work from a particular day
• Checking recorded time spent in apps or websites