Claude Skills by brycewang-stanford

Use when building tables and figures for a Public Administration Review (PAR) manuscript so exhibits are self-contained, accessible, and communicate effect magnitude to scholars and practitioners alike. PAR excludes tables/figures/appendices from the 8,000-word count, but exhibits still must earn their space. Designs exhibits; it does not run the analysis.

Use when building the theoretical argument of a Public Administration Review (PAR) manuscript into a contribution that advances public-management theory and informs practice. PAR rewards a clear, portable mechanism with explicit scope conditions plus a defensible practitioner "so-what." Structures the argument; it does not run analyses.

Use when deciding whether a public-administration project fits Public Administration Review (PAR) and which article type to target. PAR is the practice-bridging ASPA flagship, so the test is dual — a scholarly advance AND a credible "so-what" for public managers. Helps frame the question; it does not collect data.

Use when preparing the transparency / reproducibility materials for a Public Administration Review (PAR) manuscript. PAR is a signatory of the Center for Open Science TOP Guidelines and has adopted transparency standards (data citation, data sharing via Dataverse/QDR, reporting documentation, pre-registration). Covers quantitative and qualitative transparency and the restricted-data path. Prepares the package; it does not waive requirements.

Use when starting or navigating any Public Administration Review (PAR) manuscript and unsure which skill applies. Routes to the right PAR sub-skill based on lifecycle stage and which article type (Scholarly Take, Conceptualizing PA, Early Career Intel, Practically Speaking, Public Administration in Print) fits. It dispatches; it does not draft content.

Use when drafting or polishing a Public Administration Review (PAR) manuscript so it reads for scholars AND practitioners, carries strong Evidence for Practice, and fits the limits (≤ 8,000 words incl. abstract/endnotes/references; abstract ≤ 150 words). Tightens prose and format; it does not invent content.

Use when packaging the artifacts behind an RSS (Robotics: Science and Systems) paper — code, trained policies, trial ledgers, hardware documentation, and footage — first as anonymous review-time evidence and then as the public release the venue's free open-access proceedings culture expects, without any badge program to structure it.

Use when drafting the RSS (Robotics: Science and Systems) invited one-page rebuttal — the above-threshold-only response read primarily by the Area Chair before the decision meeting — including triage, page-budget allocation, anonymity, and what to do when no rebuttal invitation arrives.

Use when converting an accepted RSS (Robotics: Science and Systems) paper into its public form — de-anonymization and platform-attribution restoration, adding the external links the CFP reserves for this stage, publication in the free roboticsproceedings.org archive with a 10.15607 DOI, and single-track presentation preparation.

Use when designing or auditing the experimental campaign for an RSS (Robotics: Science and Systems) paper — hypothesis-shaped robot experiments, trial protocols and per-condition counts, mechanism-isolating ablations, simulation-versus-hardware evidence splits, and failure attribution that supports a scientific claim rather than a demo reel.

Use when positioning an RSS (Robotics: Science and Systems) submission within the literature — covering the robotics-conference lanes (RSS/ICRA/IROS/CoRL) plus journals and ML venues, handling fast-moving arXiv concurrency in robot learning, keeping self-references double-blind, and turning related work into claim-sharpening contrasts.

Use when strengthening the reproducibility of an RSS (Robotics: Science and Systems) paper whose evidence lives partly on hardware — platform disclosure ledgers, log-backed trial claims, seeds and configs for the computational half, honest replication tiers, and release plans aligned with the free open-access proceedings culture.

Use when explaining or strategizing around RSS (Robotics: Science and Systems) peer review — the double-blind OpenReview pipeline, the two-stage design where only above-threshold papers earn a one-page rebuttal, the Area Chair meeting that sets decisions, and what single-track selectivity does to reviewer expectations.

Use when auditing an RSS (Robotics: Science and Systems) submission for OpenReview readiness — the abstract/paper/supplement deadline cascade, the 8-page-excluding-references ceiling, double-blind compliance, PDF self-containment, the no-links-until-camera-ready rule, and dual-submission exposure.

Use when assembling RSS (Robotics: Science and Systems) supplementary material under the separate one-week-later deadline — deciding what leaves the self-contained main PDF, cutting robot video that functions as evidence rather than promotion, anonymizing footage and archives, and packaging appendices and trial logs.

Use when judging whether a robotics project fits RSS (Robotics: Science and Systems), the single-track selective venue whose bar is a scientific claim about robotics, and when routing instead toward ICRA, IROS, CoRL, HRI, WAFR, T-RO, or IJRR based on claim type, evidence shape, and audience.

Use when planning an RSS (Robotics: Science and Systems) campaign end to end — the late-January abstract/paper/supplement cascade, the invited-rebuttal window, late-April decisions, July conference week — and when placing RSS inside the year-round ICRA/IROS/RSS/CoRL robotics deadline calendar.

Use when revising an RSS (Robotics: Science and Systems) draft to meet the science-first bar — a falsifiable claim about robotics up front, evidence sized to claim scope, honest failure reporting, concision under an 8-page ceiling that rewards brevity, and prose that pre-answers reviewers because a rebuttal may never come.

Use when packaging ACM RecSys code, datasets, splits, trained models, propensity logs, and seeds as an anonymous in-paper repository during review or a public archive after acceptance, even though RecSys has no separate artifact badge — covering what recommender reviewers open first and how to make a top-N ranking table regenerable end to end.

Use when drafting an ACM RecSys rebuttal during the author-response window, covering the short narrative RecSys allows, anonymity, anchoring answers to submitted evidence and the anonymous repository, the recurring objections about untuned baselines, leakage, and offline-online gaps, and Senior-PC-focused clarification.

Use when preparing an accepted ACM RecSys paper for camera-ready, covering ACM two-column de-anonymization and reflow, the ACM Digital Library rights/copyright form, metadata and DOI, integrating rebuttal-promised fixes without overreach, releasing the previously-anonymous code/data repository publicly, and registration and presentation obligations.

Use when designing or auditing ACM RecSys experiments centered on offline-versus-online evaluation — temporal splits, equal-budget baseline tuning, full-ranking versus sampled metrics, off-policy estimators (IPS, SNIPS, doubly robust), A/B tests, exposure and popularity bias, seeds and variance, and matching each recommendation claim to its evidence.

Use when positioning an ACM RecSys submission against recommender-systems literature and its neighbors (SIGIR, KDD, WSDM, TheWebConf, UAI), including the reproducibility-critique line, arXiv and prior versions, concurrent work, ACM Digital Library archival status, and the citation coverage RecSys reviewers expect across recommendation subfields.

Use when strengthening the reproducibility of an ACM RecSys paper or preparing a RecSys Reproducibility Track submission — pinning dataset versions and splits, tuning baselines under an equal budget, reporting seeds and variance, avoiding sampled-metric distortion, and structuring a reproduction study with honest divergence analysis.

Use when explaining or planning around ACM RecSys peer review — the mutually-anonymous model with at least three PC members and a Senior PC overseer, the rebuttal phase, how the reproducibility-crisis culture shapes reviewer priorities, the offline-versus-online evaluation lens, and how acceptance leads to ACM Digital Library publication.

Use when auditing an ACM RecSys submission before the deadline — routing between the Main, Reproducibility, Industry and Resource tracks, the ACM two-column content-page budgets with appendices counted inside, double-blind anonymization for recommender papers, the anonymous-repository expectation, and desk-reject triage.

Use when organizing ACM RecSys supporting material under the venue's no-separate-supplement convention — how to split a recommender paper between the body, the appendix that counts inside the page budget, and the anonymous in-paper repository, so tuning grids, extra datasets, and ablations land where reviewers use them.

Use when deciding whether a project is a strong ACM RecSys fit, routing among RecSys, SIGIR, KDD, WSDM, TheWebConf, UAI, CHI, and general ML venues, identifying whether the core contribution is genuinely about recommendation, and choosing the right RecSys track before writing begins.

Use when planning an ACM RecSys project timeline from venue fit through abstract, full paper, the anonymous repository, rebuttal, decision, ACM camera-ready, registration, and presentation, with backward-planning offsets for an offline-evaluation recommender paper and per-track date awareness (Main, Reproducibility, Industry, Resource, R&P Notes).

Use when revising an ACM RecSys paper for a recommendation-first first page, honest offline-versus-online framing, equal-budget baseline claims, leakage-aware evaluation wording, ACM two-column 8-page compression, double-blind phrasing, and claims scoped to what the ranking evidence actually supports rather than to leaderboard language.

Use when the one-sentence "so what" is the bottleneck for a Research Policy (RP) manuscript — converting solid results into an explicit advance for innovation theory/evidence that usually carries a policy or managerial implication. Frames the claim; it does not build the mechanism (respol-theory-development) or polish prose (respol-writing-style).

Use when executing and stress-testing the empirical analysis for a Research Policy (RP) manuscript — building bibliometric/patent variables, running estimation or qualitative coding, and assembling robustness that an innovation-studies referee will accept. Executes the analysis; it does not choose the design (respol-methods) or present exhibits (respol-tables-figures).

Use when staking the contribution against the innovation-studies literature for a Research Policy (RP) manuscript — anchoring in the RP/SPRU canon and showing a genuine gap rather than an incremental replication. Positions the paper; it does not build the mechanism (respol-theory-development) or write the closing claim (respol-contribution-framing).

Use when research design, identification, or measurement is the bottleneck for a Research Policy (RP) manuscript — choosing and defending a method (patent/bibliometric, causal policy evaluation, survey, case study, or mixed) appropriate to an innovation-studies claim. Sets the design; it does not execute the estimation/coding (respol-data-analysis).

Use when drafting the response to a Research Policy (RP) decision letter — converting double-blind referee and handling-editor comments into a point-by-point response and a revision plan that protects the innovation-studies contribution. Builds the response strategy; it is the terminal skill in the pack.

Use when calibrating expectations and choreography for the Research Policy (RP) review cycle — the double-blind process, the out-of-scope desk screen, handling-editor routing, the Research Note option, and what RP referees reward versus reject. Sets expectations; it does not draft the response letter (respol-rebuttal).

Use when running the final pre-submission preflight for Research Policy (RP) via Elsevier's online submission system — article-type choice, length/abstract limits, double-blind anonymization, data & code statement, and the out-of-scope desk-screen. Final checks; it does not draft content.

Use when exhibits are the bottleneck for a Research Policy (RP) manuscript — designing tables and figures (regression tables, patent/bibliometric maps, event-study plots, case data structures) that carry the innovation mechanism for an interdisciplinary readership. Builds exhibits; it does not run the analysis (respol-data-analysis) or write prose (respol-writing-style).

Use when the conceptual argument is the bottleneck for a Research Policy (RP) manuscript — building an innovation-studies mechanism or framework that an interdisciplinary readership will accept as a contribution. Builds the theory; it does not run the analysis (respol-data-analysis) or write the final contribution paragraph (respol-contribution-framing).

Use when scoping or sanity-checking the research question for a Research Policy (RP) manuscript — deciding whether it is a genuine innovation-studies question RP owns versus an economics/strategy paper that will desk-reject as out of scope. Frames the question; it does not build the theory (respol-theory-development) or stake the gap (respol-literature-positioning).

Use when deciding which respol-* sub-skill to invoke next, or when sequencing manuscript work from topic through rebuttal for a Research Policy (RP) submission. Routes — it does not replace — the specialized skills.

Use when prose and structure are the bottleneck for a Research Policy (RP) manuscript — making an interdisciplinary innovation-studies argument land for economists, management scholars, and policymakers at once. Polishes the writing; it does not change the contribution (respol-contribution-framing) or run the submission preflight (respol-submission).

Use before submitting to score a manuscript against a specific venue's own desk-reject triggers and return a ranked, fixable risk report. Venue-conditional — it reads the target's documented triggers rather than applying generic advice. Run it after rt-submission-readiness clears the mechanical bar and before the paper is uploaded.

Use when an empirical analysis should be RUN and audited, not just advised — DiD, IV, RDD, synthetic control, DML, multiple-testing, sensitivity. Maps the design to the concrete StatsPAI / Stata MCP tools in this environment and reports the fitted, audited number. Defers result placement and house style to the target journal's pack.

Use when an author asks "which journal should I send this to?" or needs the best resubmission target after a reject. Profiles the paper, shortlists candidates from an index of 743 venues with tools/match_venues.py, and ranks them into reach / match / safe with a resubmission ladder. Reads live venue facts from each pack's source-map; defers fit judgment to the venue's own topic-selection skill.

Use when choosing between submission sequences rather than between single venues — "should I try the top journal first, or start one rung down?", or when a tenure/job-market clock makes time-to-print the binding constraint. Costs a resubmission ladder in months and in probability of ever placing, using each venue's own turnaround and desk-reject figures. Follows rt-journal-match, which produces the ladder this one prices.

Use before final submission or on acceptance to assemble and validate the Data-Editor replication package against the target venue's data-and-code policy — master script, pinned environment, README/roadmap, restricted-data plan, and a script-to-exhibit output map, with a checklist that catches the numbers a Data Editor would fail to reproduce. Reads the venue policy live from the pack's source-map.

Use after an R&R (or the simulated rehearsal) to turn a referee report into a point-by-point response letter plus a revision plan — editor first, every comment answered, each empirical fix backed by a real re-run via the target pack's skill and the execution bridge. Defers the venue's response format to the pack + source-map.

Use to rehearse peer review before submitting — a calibrated Associate Editor desk-screen plus 2–3 distinct-lens referees for the target venue, adversarially verified and synthesized into a referee report, a decision band, and a prioritized, skill-mapped fix list. A rehearsal that predicts the attack surface; it does not replace real review.

Use when an author is one revision away from submission and needs a venue-parameterized go/no-go scorecard on the actual manuscript: fit, identification, robustness, exhibits, exposition, venue mechanics, data/code, and pre-empted objections. Returns PASS/FLAG/FAIL per dimension, the blocking gaps, and the desk-reject risk. Reads the venue bar from the target pack + source-map.