「did」の検索結果

Biohub ESMFold2 / ESMFold2-Fast all-atom co-folding (Candido et al. 2026, github.com/Biohub/esm). Single-sequence and MSA modes; protein, DNA, RNA, ligand (CCD/SMILES), modified residues. FoldBench Ab-Ag 50-55%, PPI 70-77% DockQ-pass. Also covers the ESMC-{300M,600M,6B} protein language models from the same release: masked-LM logits, hidden states, mutation scoring, contact prediction, and the SAE interpretability head. MIT-licensed weights on HuggingFace org `biohub`. Use this skill when: (1) Predicting complex structures with single-sequence input, (2) Validating designed binders with ESMFold2-Fast, (3) Running ESMFold2 with MSA input, (4) Getting ESMC embeddings or per-residue mutation scores, (5) Choosing kernel backend and sampling-step settings for paper-faithful throughput.

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Compute the EXACT sub-transaction impact of a rollup rule in TRES Finance — every filter applied — and flag genuine problems (zero matches, overlap, config mistakes) before the rule is created. Two modes. Mode A: a pre-create quality gate invoked by the tres-rollup-rules skill to vet proposed candidate rules. Mode B: standalone review of a rule someone submitted or of existing PENDING rules. Trigger this skill when the user says "review this rollup rule", "what would this rollup rule actually match", "preview the rollup impact", "how many transactions would this rule catch", "is this rollup rule valid", "validate my rollup rule", "check my pending rollup rules", or "review the rollup requests". Do NOT use this to design or propose new rollups — that is tres-rollup-rules. This skill only measures impact and flags issues; it never executes mutations.

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Causal inference design audit for the sewage-house-prices project. Runs a 4-phase review (claim identification, design validity, inference, polish) covering hedonic pricing, repeat sales, long difference, DiD/event studies, upstream/downstream, and dry spill strategies. This skill should be used when asked to "check the econometrics", "audit the identification", "review the strategy", or when verifying that code matches the stated design.

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End-to-end data analysis workflow in R or Python — from exploration through regression to publication-ready tables and figures. Make sure to use this skill whenever the user wants to run any empirical analysis, write analysis code, or produce output from data. Triggers include: "analyze this data", "run a regression", "write R code for this", "write Python code for this", "I have a dataset", "help me with this regression", "run a DiD", "run an RDD", "event study", "IV regression", "fit a model", "produce a table", "make a figure", "explore my data", or any request involving a dataset path or empirical estimation.

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Find and assess datasets for a research question. Dispatches Explorer agents to search across data source categories, then Explorer-Critic to stress-test each candidate. Produces a ranked list with feasibility grades. Make sure to use this skill whenever the user wants to identify or evaluate data sources — not to search for papers or run analysis. Triggers include: "find data", "what data should I use", "find a dataset for this", "where can I get data on X", "assess datasets", "what datasets exist for", "help me find data", "is there data on this", "what are my data options", "I need data for this project", or any request to locate empirical data sources for a research question.

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Divergent research ideation — generate many candidate directions, then adversarially filter to the strongest

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Comprehensive Stata reference for writing correct .do files, data management, econometrics, causal inference, graphics, Mata programming, and 20 community packages (reghdfe, estout, did, rdrobust, etc.). Covers syntax, options, gotchas, and idiomatic patterns. Use this skill whenever the user asks you to write, debug, or explain Stata code.

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This skill covers causal inference methods in observational and quasi-experimental settings. Use when the user is implementing, choosing between, or debugging causal identification strategies — including instrumental variables, difference-in-differences, regression discontinuity, synthetic control, or matching estimators. Triggers on "causal effect", "identification strategy", "instrumental variable", "2SLS", "GMM", "difference-in-differences", "DiD", "staggered treatment", "regression discontinuity", "RDD", "synthetic control", "matching", "propensity score", "IPW", "AIPW", "doubly robust", "LATE", "ATT", "ATE", "parallel trends", "exclusion restriction", "first stage", "weak instruments", or "endogeneity".

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金融经济学实证论文计划书生成器。根据用户提供的研究方向，生成包含标题、假说、数据来源、实证策略、 预期结果表格、稳健性检验、异质性分析、机制检验等12个完整模块的研究计划书。 内置中国微观/宏观数据库（皮皮侠1599个数据集、马克数据377个数据集）和WRDS国际数据库索引， 自动匹配可用数据源。融合Edmans (2024) "Learnings From 1000 Rejections"的编辑视角作为质量护栏， 确保选题具有真正的边际贡献而非"just another determinant of Y"。 当用户提到以下任何情境时触发：写研究计划书、research proposal、论文开题、选题+计划、 帮我设计一个实证研究、empirical research design、我想研究X对Y的影响怎么做、 帮我找个能发表的选题、generate proposal、写一个可以投稿的研究方案、 研究设计、identification strategy、DID/RDD/IV研究设计。 即使用户只是描述了一个经济金融现象并想知道"能不能做成论文"，也应考虑使用此技能。

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This skill should be used when the user asks to "implement a DiD regression", "write a causal inference pipeline", "set up an event study", "implement instrumental variables", "run a regression discontinuity design", "build a synthetic control model", "implement propensity score matching", "write parallel trends test", "implement Bacon decomposition", or needs code templates for causal inference methods in Python, R, or Stata. Based on Scott Cunningham's Causal Inference: The Mixtape.

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STATA code pattern library for empirical archival accounting research. Provides tested syntax from 126 peer-reviewed JAR (Journal of Accounting Research) replication files (2017-2025). Use when the user asks procedural questions like "How do I implement [method]?" or "Show me code for [technique]" — including: entropy balancing, propensity score matching (PSM), difference-in-differences (DiD), regression discontinuity (RDD), instrumental variables (IV), event studies (CAR/BHAR), survival analysis, Fama-MacBeth regressions, bootstrap, quantile regression, reghdfe/xtreg/areg, clustering standard errors, fixed effects, esttab/outreg2 table formatting, winsorization, leads/lags. Users can specify their variables (e.g., treatment, outcomes, controls) and receive adapted syntax. NOTE: This skill provides code patterns from published papers, not research design advice.

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Production-grade Bayesian causal inference with PyMC, CausalPy, and DoWhy. Enforces DAG-first thinking, mandatory user checkpoints for assumptions, design-specific refutation, and defensible reporting with causal language guardrails. Trigger on: causal inference, causal effect estimation, treatment effects, counterfactuals, difference-in-differences (DiD), synthetic control, regression discontinuity (RDD), interrupted time series (ITS), instrumental variables (IV), propensity scores, DAGs, causal graphs, confounders, backdoor criterion, do-calculus, interventional distributions, pm.do(), pm.observe(), CausalPy, DoWhy, mediation analysis, refutation, sensitivity analysis, parallel trends, placebo tests, or any question of the form "does X cause Y" or "what is the effect of X on Y."

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Research technology and advanced-manufacturing investments using Serenity-inspired supply-chain bottleneck analysis. Use for theme scans, company thesis challenges, candidate comparisons, or learning this method. Prioritize A-shares unless another market is requested. Return research priorities, dated evidence, profit implications, and conditions that would change the judgment.

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Operate an explicitly activated LoopX Material Lifecycle for a connected project. Use for material-store inventory, lossless migration, candidate/archive transitions, exact-read-backed ranking, ranked-entry rebuilds, bounded Explore intake, owner-gated apply, rollback, and audit. Do not use for ordinary one-off reading or research when the project has not activated Material Lifecycle.

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Locate and read a Codex thread by a codex thread link, thread id, or rollout path across all local CODEX_HOME directories (~/.codex, ~/.codex-gpt, ...). Use when the user pastes a codex:// link, asks to understand the 前因后果 of a Codex session, wants a timeline of what a long-running main thread did, or needs to search / export a past Codex conversation.

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Use this skill to translate a classifier's in-place verdict into a precise, page-by-page work plan for the docs-sync panel. Activate after docs-impact-classifier returns verdict in_place; reads the candidate page list, fetches the actual page contents, narrows scope to specific sections within each page, and emits the per-page task brief the panel fans out against.

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Assess a JS SDK release candidate or release plan against the previous release and recommend ship or block.

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Verify PR #N, does this PR fix #M, check the fix in #N, or test the candidate PR.

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Finds, reads, and reconstructs coding-agent sessions across Codex, Claude, OpenCode, OMO/Senpi, and other local agent logs. Use when asked to find or search past sessions, transcripts, or subagent runs, or to recover what an earlier session did.

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Score Hong Kong and China A-share dividend candidates across yield quality, durability, cash flow, valuation and momentum. Use when the user asks to dividend strategy with AlphaGBM. Use the bundled Python runner; never silently replace real results with demos.

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Compare option candidates and score components, with funding, expiry and assignment risks. Use when the user asks to options strategies with AlphaGBM. Use the bundled Python runner; never silently replace real results with demos.

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Estimate causal effects from existing data. Use when fitting or interpreting DiD, ITS, synthetic control, regression discontinuity, or other treatment-effect analyses, including robustness checks and counterfactual plots. For choosing a study design before analysis, use designing-experiments instead.

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Design experiments and quasi-experiments before analysis. Use when choosing study design, treatment/control structure, outcomes, assumptions, validation plans after scientific experiment failure, or which of DiD, ITS, synthetic control, or regression discontinuity fits the research question. For fitting models or estimating effects on existing data, use performing-causal-analysis instead.

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CVE validation domain card. Use after CVE lookup has produced applicable or candidate CVEs and red-team mode needs scoped evidence to decide whether to continue, pivot, or report.

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