「correction」の検索結果

Use when a Scenario output must be checked and improved rather than accepted first roll: iterating a generation until it matches the brief, fixing a batch that came back off-brief, retrying failed shots methodically, wiring an automated generate, review, revise loop, or deciding whether to re-prompt, swap references, inpaint, post-process, or change model. Keywords: refine, iterate, critique, review loop, QA, self-correction, verify, acceptance criteria, retry, drift.

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Use when editing an existing image on Scenario through MCP with a tool model, not a new generation: upscale or enhance to 2x, 4K, 8K, super resolution, 3D LUT color grade, color correction, posterize, solarize, vignette, film grain, blur, sharpen, glow, chromatic aberration, oilify, cubism, crystallize, dodge and burn, tint, desaturate, expand or uncrop, reframe an aspect ratio, resize to exact pixels, slice tiles, contact sheet, split into layers, remove a background or watermark, vectorize.

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Capture a human-approved UI design lesson as a privacy-minimized local StyleSeed candidate, review it, and prepare an opt-in share package without transmitting project code, prompts, screenshots, or brand data. Use when a person asks StyleSeed to remember, learn from, generalize, review, or prepare a reusable rule from an accepted design correction.

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Post-ship documentation sync. Updates README, ARCHITECTURE, CONTRIBUTING, CHANGELOG, TODOS, VERSION. Auto-applies factual corrections, asks for risky rewrites. CHANGELOG voice polish. Cross-doc consistency checks.

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Prevent 此地无银三百两式 residue: finalize artifacts without echoing rejected session-only alternatives into labels, metadata, commits, PRs, or handoffs. Use after corrections or discarded proposals. Not for ordinary deletion, deprecation, migration, or exclusions materially required for safety, accuracy, compatibility, audit, quotation, or requested comparison.

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Applied statistics for business and product questions — test selection, assumption checks, power planning, effect sizes with intervals, multiplicity correction. Use when interpreting an experiment, sizing a study, or vetting a claim.

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。

Show autopilot status: health, auto-corrections, guardrails, escalations. "what did the autopilot change"

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Guide to analyze a current work session and propose improvements to skills. Use (1) automatically after working with a skill to capture learnings, (2) when the user suggests improvements, corrections, or additions during a skill-related session, or (3) when the user manually invokes `self-improve`.

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La compétence Normalisation juridique FR nettoie un document Word rédigé en français juridique. Elle distingue deux régimes : les corrections déterministes (apostrophes courbes, guillemets français, espaces insécables, insécables des références — art. 1240, n° 21-12.345, 50 % —, ordinaux 1ère → 1re, ligatures œ, la loi dispose et non stipule, anglicismes-calques), appliquées directement ; et les réécritures de jugement (virgule avant « et », ternaires creux, formules emphatiques, harmonisations), posées en révisions Word soumises à votre acceptation. Chaque changement est consigné et restitué en tableau récapitulatif : on revient en arrière mot à mot — « défais 3, 7-10 ». La normalisation des citations reste purement formelle. Fonctionne en Cowork ou sur fichier Word.

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Génération de sujets d'entraînement complets pour l'épreuve de NOTE DE SYNTHÈSE (5 heures, coefficient 3) de l'examen d'accès au CRFPA : choix du sujet, dossier documentaire de 16 à 20 documents assemblé à partir de sources réelles vérifiées (textes, jurisprudence, travaux parlementaires, doctrine) avec contrôle du verbatim, puis grille de notation au modèle de la Commission nationale (résumé de chaque document, problématique, thèmes cotés sur 20, plan). Livraison en deux fichiers .docx distincts, le sujet et la grille. Si l'utilisateur n'impose pas de sujet, en chercher un dans l'actualité juridique. Mode aveugle pour l'étudiant qui compose sans rien voir du corrigé. Déclencher dès que l'utilisateur demande un sujet de note de synthèse, un dossier documentaire, une note de synthèse blanche, un entraînement à l'épreuve de 5 heures du CRFPA ou de l'IEJ, ou une grille de note de synthèse. NE PAS déclencher pour les épreuves de 3 heures (sujet-crfpa-toutes-matieres) ni pour la correction de copies.

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Use Nara/PNU Korean spell-check surfaces conservatively to proofread Korean text, chunk long input, and return change-focused correction suggestions.

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Analyze the current session and propose improvements to skills. **Proactively invoke this skill** when you notice user corrections after skill usage, or at the end of skill-heavy sessions. Also use when user says "reflect", "improve skill", or "learn from this".

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Diagnose and fix video/image color OBJECTIVELY with the get_image (action:"analyze_color") tool (scopes/stats such as black/white points, contrast, saturation, clipping, cast) instead of eyeballing a contact sheet. Covers the "washed out" signature, why reference color-match (mkl/ColorMatch/ColorMatchAdobe) CAN'T add contrast a flat source lacks, the levels/contrast-stretch fix (core AdjustContrast / CurveEditor), the measure→fix→re-measure loop, the side-by-side sandbox pattern, and where to place the fix in a render graph (after decode, before save). Use when a render looks washed out / flat / dull / over-saturated / color-cast, or when deciding between a color-match and a contrast/levels fix.

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standR is an user-friendly R package providing functions to assist conducting good-practice analysis of Nanostring's GeoMX DSP data. All functions in the package are built based on the SpatialExperiment object, allowing integration into various spatial transcriptomics-related packages from Bioconductor. standR allows data inspection, quality control, normalization, batch correction and evaluation with informative visualizations.

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Implements a variety of methods for batch correction of single-cell (RNA sequencing) data. This includes methods based on detecting mutually nearest neighbors, as well as several efficient variants of linear regression of the log-expression

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BUSseq R package fits an interpretable Bayesian hierarchical model---the Batch Effects Correction with Unknown Subtypes for scRNA seq Data (BUSseq)---to correct batch effects in the presence of unknown cell types. BUSseq is able to simultaneously correct batch effects, clusters cell types, and takes care of the count data nature, the overdispersion, the dropout events, and the cell-specific sequencing depth of scRNA-seq data. After correcting the batch effects with BUSseq, the corrected value ca

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Interactive loop that builds and tunes an AI prompt for evaluating whether a company fits a client's ICP. Run after any list-building skill (disco-like, blitz-list-builder, google-maps-list-builder, prospeo-full-export) to qualify companies before scaling. Iterates batches of 10 companies with user feedback, stops when 2 consecutive rounds have zero corrections, saves the final prompt for reuse. Always uses Claude Code Task sub-agents — never an external API key.

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Cialdini's seven principles of compliance (the classic six — reciprocation, commitment-consistency, social proof, liking, authority, scarcity — plus Unity, added 2016) and Pre-Suasion. Use when crafting ethical persuasion (marketing, sales, negotiation), defending against manipulation, or designing systems that need cooperation. Includes replication-crisis caveats and known factual corrections (Bickman parking-meter numbers, Hofling modern replication, Drive Carefully condition disambiguation, Genovese myth).

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Build and maintain a Karpathy-style LLM knowledge base — a self-compiling Obsidian markdown wiki where an Agent ingests raw sources, compiles cross-linked concept/entity/summary pages, answers queries against the corpus, lints the graph for health, and audits in-context human feedback filed from Obsidian or the local web viewer. Use when (1) scaffolding a new knowledge base for any research topic, (2) ingesting articles/papers/PDFs/web pages into raw/, (3) compiling or restructuring wiki articles from existing raw material, (4) answering questions against the wiki and filing durable answers back, (5) running lint passes for dead links / orphan pages / coverage gaps / audit shape, (6) processing human feedback from the audit/ directory and applying corrections. Not for general note-taking, daily journals, or non-wiki Obsidian use.

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Weekdays, file work only, no browser at all. Takes the single highest ranked open fault on the fleet and turns it into something the member can act on in a minute: the fault in one sentence, the first record that shows it, what changed around that date, three ranked causes each with its evidence line, what the failure has cost, and one paste ready correction line with the exact path it belongs in. It reads other Employees strictly read only, writes nothing outside its own folder, sends only where you released the channel, spends only where you released it, and never touches a credential.

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Hire one of the eight open source AI Employees (GTM Engineer, SEO/AEO, Web Dev, Social Media, Ad Manager, Sales, Customer Satisfaction, Chief of Staff) into a local folder, prove it runs, and hand over its install prompt. Also answers which one to hire first, how a hired employee runs on a schedule, how to direct it, how it improves from corrections, and how to upgrade it. Use when the user says hire an employee, install an AI employee, set up the GTM Engineer, names one of the eight, or asks how AI Employees work. It copies files and runs self tests only. It registers no schedule and sends nothing.

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Knowledge base from "Solving a Million-Step LLM Task with Zero Errors" (Meyerson, Paolo, Dailey, Shahrzad, Francon, Hayes, Qiu, Hodjat, Miikkulainen — arXiv:2511.09030). Use when designing long-horizon, multi-step agentic LLM pipelines that need very high reliability, applying Maximal Agentic Decomposition (MAD), first-to-ahead-by-k voting, or red-flagging error correction, estimating cost/reliability scaling laws for multi-agent systems, or studying Massively Decomposed Agentic Processes (MDAPs).

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Review session corrections, trigger misses and recurring agent mistakes to propose targeted skill-library changes. Use for retrospective, rework or routing-gap analysis before registry edits; load +common/common-learning-log for redacted evidence.

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。

Capture Elixir/Ecto/LiveView lessons and Hex API rules. Use after corrections or when asked to document learning…

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。