'did' 검색 결과

Turn a financial economics research idea into a structured, 12-part empirical proposal. It outlines hypotheses, matching data sources, methods for testing causal effects, illustrative results tables, and robustness checks, while examining the study’s contribution.

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• Drafting a financial economics research proposal
• Finding Chinese and international data sources
• Planning tests of causal effects

Find STATA code examples for empirical accounting research from published replication files. Adapt regression, matching, event study, and table export syntax to your variables, with citations to the papers behind each example.

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• Implement fixed effects regressions in STATA
• Find published DiD and matching code examples
• Export regression results as formatted tables

Run empirical and causal analyses in Python with StatsPAI, from stating assumptions to estimating effects and checking how results change under alternative choices. Produce diagnostic figures, regression tables, and replication bundles for economics, public health, or machine learning studies.

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• Estimating policy or intervention effects
• Comparing public health effect estimates
• Examining how effects differ across groups

Review a JavaScript SDK release plan or final candidate against the previous release. Check compatibility, packaging, regressions, and Changesets version records, then recommend shipping or blocking with supporting evidence and a separate assessment of documentation coverage.

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• Review the next SDK release plan
• Assess whether a final release candidate can ship
• Check generated versions and changelogs for consistency

Assess candidate CVEs, identifiers for known vulnerabilities, against observed software, versions, and patch evidence. The skill helps determine whether to continue verification, gather more information, or change testing direction within the authorized scope.

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• Checking whether candidate CVEs match observed software
• Reviewing patch status before further testing
• Identifying evidence gaps that require more reconnaissance

Plan experiments and quasi-experiments before analyzing data. Define treatment and comparison groups, choose outcomes, assess design assumptions, and specify follow-up validation when results are weak or contradictory, including rules for continuing, revising, or stopping.

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• Choose a study design before collecting or analyzing data
• Define treatment groups, comparison groups, and outcomes
• Review assumptions behind a proposed research design

Generate protein structures and sequences with specified conditions using NVIDIA's hosted API or a local NIM service. Supports binder and antibody design, reward-guided search, and inspection of generated candidates, outputs, and validation results.

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• Generate protein candidates for a specified binding target
• Design antibodies or structures around a retained motif
• Explore candidates with reward-guided search

Generate drug-like molecule candidates with GenMol NIM through NVIDIA's hosted API or local Docker deployment. Expand molecular scaffolds, modify existing candidates, rank results by drug-likeness or lipophilicity, and save the ranked molecules to a file.

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• Generate new molecule candidates from scratch
• Add structures around a molecular scaffold
• Modify fragments of a lead molecule

[omh] Gathering candidate papers, datasets, or repos: source candidate inventory - prepare typed source candidates and acquisition status before downstream work; use ulw-research to fetch and cite them, or research-brief to turn them into a decision-ready brief. Use when the user says: source-finder, source finder, source acquisition, source intake, find papers and datasets, find datasets and repos, find papers, find arxiv link.

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[omh] Existing skills to reuse before authoring: prepare a metadata-only search-before-creation report for local, marketplace, GitHub, and web skill candidates with risk review and adoption options. Use when the user says: skill-scout, skill scout, skill candidate, skill candidate search, skill discovery, find a skill, find skills, top skills.

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Generate and prioritize US equity long-side edge research tickets from EOD observations, then export pipeline-ready candidate specs for trade-strategy-pipeline Phase I. Use when users ask to turn hypotheses/anomalies into reproducible research tickets, convert validated ideas into `strategy.yaml` + `metadata.json`, or preflight-check interface compatibility (`edge-finder-candidate/v1`) before running pipeline backtests.

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Collects candidate biomedical literature across multiple databases, adapts search logic by database, preserves source metadata, and organizes results into a structured, screening-ready candidate pool. Always use this skill when a user wants cross-database literature collection, search strategy construction, candidate paper aggregation, or first-pass evidence organization before deduplication, screening, layered reading, or review planning. Requires real and verifiable literature records only. Every formal literature item must include a real link and DOI when available; never fabricate citations, titles, authors, years, journals, abstracts, PMIDs, or DOIs. If a DOI is unavailable or cannot be verified, state that explicitly rather than inventing one.

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Search past meeting transcripts and voice memos for specific topics, people, decisions, or ideas. Use this whenever the user asks "what did we discuss about X", "find that meeting where we talked about Y", "what did Alex say", "did we decide on", "what was that idea about", or any question that could be answered by searching their meeting history. Also use for "do I have any notes about" or "check my meetings for".

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Turn interview notes into a structured candidate scorecard and hire recommendation. Use when asked to write an interview scorecard, a candidate evaluation, an interview debrief, or to summarize feedback into a hire/no-hire call. Produces a per-competency assessment with evidence and ratings, an overall recommendation with confidence, and the open questions for the next round — evidence-based, bias-aware, and decision-ready.

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This package aims to integrate GWAS-derived SNPs and coexpression networks to mine candidate genes associated with a particular phenotype. For that, users must define a set of guide genes, which are known genes involved in the studied phenotype. Additionally, the mined candidates can be given a score that favor candidates that are hubs and/or transcription factors. The scores can then be used to rank and select the top n most promising genes for downstream experiments.

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Use when a campaign has ENDED and the team needs a structured retrospective — a scorecard against target, what worked, what did not, and why with evidence, surprises, process review, lessons written back into the playbook and Brand Hub, and action items with owners. Trigger on 'campaign retrospective', 'campaign retro', 'post-mortem', 'campaign debrief', 'what worked and what did not', 'the campaign is over, what did we learn'. Also use when a campaign underdelivered and the team is arguing about why. Not for — a periodic performance report, see `07-marketing-report-global`; diagnosing live numbers mid-flight, see `03-performance-eval-global`; the next period ads plan, see `57-next-ads-plan-global`.

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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".

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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.

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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.

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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".

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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

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

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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

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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.

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