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Answer a construction request for information so the site can act on it — a direct answer, the drawing or specification reference that supports it, and an explicit statement of whether it changes cost or programme. Use when asked to respond to an RFI, answer a contractor query, or handle a request for information on site, or when RFIs keep coming back for clarification. Produces the answer with its reference, the cost and time implication stated, the instruction status, the distribution list, and the register entry.

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Produce a CV shaped for one target company and role: its applicant tracking system, the country's conventions, the sector's norms, its published values and the job ad, with every choice explained. Use when asked to write my CV for a specific company, tailor my CV to Google, Amazon or any named employer, make my CV fit this company's format, or turn my experience into a CV for this job. Produces the tailored CV, a short format rationale citing public sources with dates, and a list of claims the person must be ready to defend. Never invents experience or insider knowledge.

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Use when asked 个人信息保护影响评估怎么做, 写一份 PIA, 处理敏感个人信息要评估吗, 自动化决策评估, PIPL 第五十五条, or assess a processing activity under China's Personal Information Protection Law. Produces a personal information protection impact assessment (个人信息保护影响评估) covering the PIPL Article 55 triggers, the Article 56 questions (lawful, legitimate and necessary; impact and risk; adequacy of protection), a risk register with mitigations and owners, and a record kept for at least three years.

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Use when asked 数据出境要申报吗, 数据出境安全评估还是标准合同, 个人信息出境标准合同怎么备案, 我们把数据传到海外总部合规吗, or decide how to transfer data out of mainland China lawfully. Produces a data map of what leaves China, the route that applies (an exemption, the personal information export standard contract, certification, or a CAC security assessment) with the thresholds to confirm, the filing steps, and the documents to prepare, including the personal information protection impact assessment.

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Write a UK Civil Service or NHS application: behaviour statements in the Success Profiles format, a personal statement mapped to the essential criteria, and a CV in the form these schemes expect. Use when asked to write my civil service application, answer Success Profiles behaviours, write a 250-word behaviour example, write my NHS supporting information, or apply on Civil Service Jobs or NHS Jobs. Produces each behaviour statement within the word limit using a situation, task, action, result structure, a criteria-mapped personal statement, and a check against the advert's essential criteria.

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Convert a meeting into async work that actually decides — the doc-plus-deadline format that replaces the room, the comment-window rules, the decision-closure step that async usually fumbles, and the honest test for what still needs synchronous. Use when asked can this meeting be async, replace our status meeting with a doc, run this decision without a call, or async isn't working for us. Produces the conversion design, the async artifact format, the closure protocol, and the still-needs-a-room list.

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Build the context an AI needs to do a task well — the background, constraints, examples, and format it can't guess — so you get a great result on the first try instead of a generic one you have to keep correcting. Use when asked why does AI give me generic answers, how do I give AI better context, my AI results are mediocre, or how do I get it right the first time. Produces the specific context this task needs (who/what/constraints/examples/format), a reusable primer you can paste ahead of the request, the difference between a starved prompt and a well-briefed one, and what to leave out — turning vague back-and-forth into a strong first result.

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Kotlin Multiplatform structure and language traps: module splits, expect/actual, Koin and ViewModel scoping, layers, version catalogs, erased overloads, Char and string-resource formatting limits. Use when shaping modules or DI, when a format specifier renders verbatim, or code misbehaves on one platform.

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Use this skill to build a multi-element Excel dashboard — Dashboard sheet on open, multiple formula-driven KPI cards, multiple charts, sparklines, and conditional formatting — from CSV or tabular input. Trigger on: 'dashboard', 'KPI dashboard', 'analytics dashboard', 'executive dashboard', 'metrics dashboard', 'CSV to dashboard', 'data visualization'. Output is a single .xlsx. Scene-layer on officecli-xlsx: inherits every xlsx hard rule. DO NOT invoke for: a single budget tracker / one-sheet CSV-with-formatting (use xlsx), a 3-statement / DCF / LBO financial model (use financial-model), a weekly report with ≤ 1 chart and < 10 rows (use xlsx).

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Use when drafting or polishing an American Anthropologist (AA) manuscript so it reads across all four fields, follows AA house style (Chicago author-date; free-format submission accepted), fits the section word caps (Research Articles ≤ 8,000 words; abstract 200 words), and uses inclusive, care-ful language. Tightens prose and format; it does not invent content.

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Use when drafting or polishing an Agricultural Systems (AgSy) manuscript so it reads clearly for a systems-science audience, follows the journal's format, and meets front-matter requirements (abstract <= 250 words, Highlights, graphical abstract). Research papers run to about 8,000 words (no hard cap). Tightens prose and format; it does not invent content.

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Use when partitioning content between an Advanced Materials main text and its Supporting Information, ensuring the paper stands alone while extended data, full experimental detail, and secondary characterization live in the SI. Organizes the Supporting Information; does not write the main text.

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Use when running the final pre-submission preflight for an Advanced Materials manuscript — article type, format, files, Supporting Information, TOC graphic, ORCID, and metadata for the Wiley submission system. Verifies submission readiness; does not write content.

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Use when the mechanism analysis for a 《会计研究》 (Accounting Research) manuscript is missing or mis-specified — the channel must be an information mechanism (reduce information asymmetry, raise disclosure quality, constrain earnings management, improve audit assurance), not a generic pricing or financing channel. Use after the main result holds.

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Create LinkedIn carousel posts as high-quality PNG images. Design informational multi-slide posts like "5 AI GTM workflows" with consistent styling, then automatically screenshot each slide at LinkedIn's optimal 1080x1080px format.

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End-to-end skill that turns a single reference image into a published Gooseworks style — analyzes the image, drafts the slim style spec, renders a hero example plus 2-3 additional formats via Playwright, writes the `gooseworks-style.json` manifest, and publishes via `npx gooseworks styles publish` so other agents can discover it. Mirrors goose-graphics-create-format but for styles.

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Assemble a narrated motion-graphic LISTICLE video ad from a config — a spoken voiceover (cloned or cast, in the chosen tone) carries a numbered listicle while N web-animated hyperframe beats (HTML plus the Web Animations API, one branded design system of alternating tiles, big hero numerals, and callouts in the chosen visual style) are rendered frame-by-frame via Playwright and anchored to the VO's word-level timestamps, optional color-graded breather windows (brand clip, AI clip, or motion-graphic) give visual breath, and captions burn ONLY inside those B-roll windows (2-word chunks, ASS Format header carrying a Name field so none drop) with the VO mixed under a low music bed. This is the FREE deterministic assembly stage (Playwright beat render plus ffmpeg concat plus window-masked caption burn plus VO-and-music mix plus final composite) — the VO, the music bed, and any AI breather clips come from create-vo-elevenlabs, create-music-elevenlabs, and create-video-fal. Use for the vo-anchored-motion-listicle format.

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Assemble the FREE steps of the flat-vector-explainer video format — a flat-illustration host character (who, where, tone, voice and music come from the recipe choices) walks a countable N-step product routine, one step per beat, and Remotion composites every chip/numeral/tagline/slate/CTA as an animated DOM overlay ON TOP of the Kling i2v character clips (text is NEVER baked into a keyframe — i2v warps type), the closing 'N products' grid is a PIL composite of the REAL product photos (not AI), full-sentence VO drives word-by-word burned captions over a VO-forward music bed, and the ~50s animated silent master is re-cut to a 30s deliverable FROM the animated master (never a static intermediate). Documentation-grade — ships config.example.json + PIPELINE.md + a README of the free assembly; the paid gen steps (keyframes, Kling i2v, VO, music) are separate capabilities the recipe orchestrates. Use for the flat-vector-explainer format.

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Assemble the FREE steps of the product-hypermotion + kinetic-typography video format — dice ONE Seedance 2.0 hypermotion clip into 5-6 segments and intercut them with PIL kinetic-typography spec/CTA cards (italic skew, 1.08x outline echo, 3D extrusion, slam-with-shake, inversion flash) plus a real-logo PIL end card (base64-decoded from the brand SVG), center-crop 1:1 to 9:16, and explicit-map mux a music bed. Deterministic, FREE (PIL + FFmpeg), no paid calls, the real logo and spec typography stay pixel-crisp. The paid steps (the ONE Seedance i2v, the music) are separate capabilities; the recipe orchestrates them. Use for the product-hypermotion format.

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Composite a generated creator track into a product layer, per beat — in the creator's zone on split beats (either side of a seam), full frame on creator beats, hidden on product beats — with the creator's voice as the audio (loudness -14 LUFS) and an optional ducked music bed, or a music-only or silent master for voiceless formats. Free, local ffmpeg. Pairs with footage-cutlist (the layer) and create-creator-takes-h3 (the creator). Use for split-screen (with an optional zoom and brand-colour seam bar), screen-insert and any creator-over-product format.

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Choose the right serialization format for .NET applications. Prefer schema-based formats (Protobuf, MessagePack) over reflection-based (Newtonsoft.Json). Use System.Text.Json with AOT source generators for JSON scenarios.

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Predict B-cell and T-cell epitopes for vaccine antigen design and epitope mapping with BepiPred-3.0, DiscoTope-3.0, the IEDB tools, and EL-mode MHC presentation. Encodes the load-bearing asymmetry that T-cell epitope prediction is mature (it reduces to MHC presentation, AUC>0.9) while B-cell prediction is unreliable (linear predictors ~AUC 0.6 because ~90% of real epitopes are conformational) — so structure-based DiscoTope-3.0 on AlphaFold models is the only defensible B-cell path, propensity scales are obsolete, and NetChop is largely redundant on EL-trained models. Use when mapping epitopes or selecting vaccine antigens. MHC binding lives in mhc-binding-prediction.

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Stores and operates on sparse expression matrices for single-cell and large bulk RNA-seq, covering dgCMatrix/dgRMatrix/dgTMatrix when-each-is-fast, the dgCMatrix (CSC, R) <-> CSR (Python) implicit transpose, AnnData (cells-rows) <-> SingleCellExperiment (cells-cols) orientation flip, HDF5/h5ad vs Zarr cloud-native shift, HDF5SummarizedExperiment + DelayedArray for out-of-memory bulk, scanpy backed mode for large h5ad, the ~10-15% density crossover where dense beats sparse, 10X format proliferation (MTX vs CellRanger H5 vs h5ad), the dense-conversion memory blow-up, and Dask + Zarr for consortium-scale matrices. Use when choosing sparse format, working with single-cell-sized matrices, importing/exporting 10X, debugging R/Python interop transposes, processing matrices too large for RAM, or building cloud-native pipelines.

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Download genome assemblies, gene records, and ortholog data from NCBI using the modern Datasets v2 CLI (replaces assembly_summary.txt scraping and many EFetch workflows). Use when bulk-pulling genome assemblies, gene metadata across species, ortholog sets, or BLAST databases; when E-utilities are too slow for genome-scale work; or when automatic checksum verification, parallel download, and clean accession-driven retrieval are required. Encodes the JSON-lines output format, dataformat conversion, --dehydrated for cloud workflows, and when Datasets is/isn't the right tool.

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