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概要と使いどころ
Zero-shot time series forecasting with Google's TimesFM foundation model. Use for any univariate time series (sales, sensors, energy, vitals, weather) without training a custom model. Supports CSV/DataFrame/array inputs with point forecasts and prediction intervals. Includes a preflight system checker script to verify RAM/GPU before first use.
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Compose one publication-grade multi-panel figure. Start from a one-line claim plus immutable data Artifact Version references, or inspect an existing figure and draft its outline directly. Plan a 12-column panel outline, delegate one worker per panel, compose and inspect the result, then run at most three adversarial review rounds while regenerating only affected panels. For a standalone plot use `figure-style`; for whole-paper figure ordering use `paper-narrative`.
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Judge and reshape the story told by an entire paper figure deck. Use when writing or revising a paper to derive a grounded brief from the manuscript and captions, review the full deck as a handling editor, and hand an ordered figure arc to `figure-composer`.
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Publication-grade correctness and legibility rules for final-deliverable scientific figures, not exploratory plots. Use for a figure that will ship in a report, paper, export, or kept artifact. Covers data fidelity, label economy, color threading, chart choice, layout, and render-then-verify QA without imposing a visual house style. For multi-panel composition use `figure-composer`; for whole-paper ordering use `paper-narrative`.
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Takes your raw campaign performance data and writes the executive summary paragraph that goes at the top of the report. The plain English explanation of what happened, why it happened, and what's being done about it. The part every client actually reads. Platform: Google and Meta.
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End-to-end R data analysis for the sewage project. Writes analysis scripts following project conventions (here::here, arrow/parquet, fixest, modelsummary, native pipe), runs code review, and produces publication-ready tables and figures. This skill should be used when asked to "run an analysis", "estimate the model", "add a specification", or "write an R script".
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VS-Enhanced Qualitative Design Consultant with Ethnography & Action Research Enhanced VS 3-Phase process: Avoids overused phenomenology, proposes context-optimal qualitative strategies Absorbed H1 (Ethnographic Research Advisor) and H2 (Action Research Facilitator) capabilities Use when: selecting qualitative research design, planning phenomenology/grounded theory/case study/ethnography/action research Triggers: phenomenology, 현상학, grounded theory, 근거이론, case study, 사례연구, narrative inquiry, ethnography, 민족지, action research, 실행연구, qualitative design
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Activate when the user is drafting any academic economics content from scratch (e.g., outlines, abstracts, introductions, data/methods/identification sections, results narratives, conclusions, referee responses, table/figure captions) and needs economics-specific structure, phrasing options, and quality checks to produce publication-ready prose.
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Conduct rigorous thematic analysis (TA) of qualitative data following Braun and Clarke's (2006) six-phase framework. Use whenever the user mentions 'thematic analysis', 'TA', 'Braun and Clarke', 'qualitative coding', 'identifying themes', or asks for help analysing interviews, focus groups, open-ended survey responses, or transcripts to identify patterns. Also trigger for questions about inductive vs theoretical coding, semantic vs latent themes, essentialist vs constructionist epistemology, building a thematic map, or writing up a qualitative findings section. Covers all six phases, the four upfront analytic decisions, the 15-point quality checklist, and the five common pitfalls. Produces a Word document write-up and an annotated thematic map. Does NOT cover IPA, grounded theory, discourse analysis, conversation analysis, or narrative analysis — use a different method for those.
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Write competitive research proposals for NSF, NIH, DOE, DARPA, and Taiwan NSTC. Agency-specific formatting, review criteria, budget preparation, broader impacts, significance statements, innovation narratives, and compliance with submission requirements.
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Remove AI writing patterns from prose. Use this skill when writing, drafting, editing, reviewing, or revising any text to eliminate predictable AI tells, slop, and formulaic patterns. Trigger this skill whenever the user asks to "deslop", "de-AI", "make it sound human," "remove AI patterns," "remove AI tropes," "clean up AI writing," fix "slop," "deslop" text, or review prose for authenticity. Also use when the user asks you to write or draft anything and wants it to sound natural rather than AI-generated. Common use cases include scientific writing (manuscripts, abstracts, cover letters, grant narratives, discussion sections, peer review responses), blog posts, newsletters, memos, reports, and any other substantial prose.
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Guide users through writing a systematic literature review (SLR) following the PRISMA 2020 framework. Use this skill whenever the user mentions 'systematic review', 'systematic literature review', 'SLR', 'PRISMA', 'PRISMA 2020', 'PRISMA flow diagram', 'PRISMA checklist', or asks for help writing, structuring, or auditing a literature review that follows reporting guidelines. Also trigger when the user asks about inclusion/exclusion criteria for a review, search strategies for databases like Scopus/WoS/PubMed, study selection processes, risk of bias assessment, or narrative synthesis for a review paper. This skill covers the full PRISMA 2020 checklist (27 items), produces a Word document manuscript in strict journal article format, generates an annotated PRISMA flow diagram, and enforces APA 7th Edition referencing throughout. It does NOT cover meta-analysis or statistical pooling. By Chuah Kee Man.
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。
Write competitive research proposals for NSF, NIH, DOE, and DARPA. Agency-specific formatting, review criteria, budget preparation, broader impacts, significance statements, innovation narratives, and compliance with submission 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。
Promote existing research into a stable-status canonical article under `articles/` in a Knowledge Base project (the `knowledge-base` starter pack). Read when a decision has actually been made and the team wants the source-of-truth written down, or when asked to consolidate, canonicalize, promote research, or supersede an older article. Carries the decision-confirmation gate, the `supersedes:` chain that keeps the evidence trail intact, and the canonical voice. Does not conduct new research — that is the sibling `research-with-sources` skill.
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How to work in a Personal CRM project (the `entity-vault` starter pack, GBrain-compatible): a typed-entity vault of people, companies, meetings, and concepts, each a dossier with a rewritable summary plus an append-only timeline. Read when the project has these folders, OR when asked to capture notes about a person or company, log a meeting, prep for an upcoming meeting, or answer who someone is and what was last said. Carries the dossier convention and entity-extraction behaviors so that guidance does not live inside template bodies or folder descriptions. Complements the platform `open-knowledge` skill; does not replace it.
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Investigate a topic against preserved sources and write a draft-status research article under `research/` in a Knowledge Base project (the `knowledge-base` starter pack). Read when asked to research a topic, compare options, synthesize sources, gather evidence, or extend an existing research doc. Carries the full procedure: scan existing coverage, agree a research rubric, capture every source verbatim before analyzing, write the article incrementally so a crash never loses work, cite every claim, and link it back into the graph. Does not promote findings to canonical knowledge — that is the sibling `consolidate-notes` skill, after a decision lands.
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How to work in a Software Lifecycle project (the `software-lifecycle` starter pack): proposals → decisions → specs → postmortems, plus guides. Read when the project has these folders, or when asked how this project is organized. Carries the doc lifecycle, status flows, and per-folder agent behaviors so that guidance does not live inside template bodies or folder descriptions. The five workflows — frame a proposal, write a spec, record a decision, write a postmortem, review a design — each ship as their own sibling skill in this pack. Complements the platform `open-knowledge` skill; does not replace it.
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。
How to work in a Codebase Wiki project (the `codebase-wiki` starter pack): an agent-authored, source-grounded wiki of the surrounding codebase. Read when the project has a `wiki/` knowledge base with `architecture/`, `modules/`, `flows/`, `concepts/`, and `guides/` sections plus `wiki/OVERVIEW.md`, or when asked to generate or refresh a wiki of this codebase. Carries the per-folder rules and freshness + log discipline, summarizes the audience/depth knobs and source-reference convention, and bundles the full generate/refresh procedure in `references/`. Complements the platform `open-knowledge` skill; does not replace it.
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。
How to work in a Plain Notes project (the `plain-notes` starter pack): a flat notes/ folder plus a daily/ journal. The 'I just want to write' layout. Read when the project has these folders, OR when asked to jot a note, capture a quick thought, or write today's journal entry. Carries the linking habit and daily-entry behavior so templates and folder descriptions stay minimal. Complements the platform `open-knowledge` skill; does not replace it.
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。
How to work in a Knowledge Base project (the `knowledge-base` starter pack). Read when the project has the three-layer source-grounded layout — `external-sources/` → `research/` → `articles/` — or when asked how this project is organized. Carries the layer model, per-folder rules, status flows, and log discipline so this guidance does NOT live inside template bodies or log.md. The three procedures live elsewhere: ingest in the platform `open-knowledge` skill, research and consolidate as their own sibling skills in this pack. Complements the platform `open-knowledge` skill; does not replace it.
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。
How to work in a Worldbuilding project (the `worldbuilding` starter pack): a fiction encyclopedia of characters, settings, themes, factions, and lore. Read when the project has these folders, OR when asked to add a character, setting, faction, or piece of lore, or to check the world for internal consistency. Carries the auto-stub and consistency behaviors so that guidance does not live inside template bodies or folder descriptions. Complements the platform `open-knowledge` skill; does not replace it.
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。
How to work in a Writing Pipeline project (the `writing-pipeline` starter pack): a three-stage drafting flow, ideas → drafts → published. Read when the project has these folders, OR when asked to capture an idea, start or revise a draft, review a piece before it ships, or publish it. Carries the stage flow and review behaviors so that guidance does not live inside template bodies or folder descriptions. Complements the platform `open-knowledge` skill; does not replace it.
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。
Retrieve the list of root GameObjects in the specified opened scene (or the active scene when `openedSceneName` is empty). Supports token-saving path-scoped reads over the root-GameObjects array via `paths` or `viewQuery`. Use 'scene-list-opened' to enumerate scenes.
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。
Generate a JSON Schema for a C# type name via reflection. Supports primitives, enums, arrays, generic collections, dictionaries, and complex objects. Knobs control inclusion of nested `$defs` and whether type-level / property-level descriptions are emitted.
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。