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Apply consistent photo adjustments across a set of images so they look like they were edited together. Use this skill whenever the user says "make my photos look cohesive", "give all these the same style", "apply a warm and golden feel to all of these", "make this cinematic", "match the look across my photos", "edit all my travel photos the same way", "batch edit these", "make these consistent", "fix my phone photos", or uploads a folder of photos and wants a unified, polished result. Also triggers for requests like "apply a preset to all of these", "make these look professional", or "they were shot in mixed lighting — can you fix them all". Outputs direct final image URLs plus an in-chat preview grid and optional Firefly Board link. Access: 🔐 Signed-In required | Gen AI: ❌

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Use when designing, reviewing, or debugging how an agent's context window gets filled, pruned, or shared — choosing what loads at boot versus on demand, sizing an install or an always-loaded file, fixing an agent that drifts, repeats itself, or forgets constraints mid-task, planning compaction or summarization, deciding single-agent versus subagents, engineering handoffs between agents, or picking a tool loadout. NOT for rewording a prompt's tone, choosing which model to pin, or debugging business logic — those are adjacent moments this skill does not serve. Historical 2024–2025 snapshot; not normative for present-day frontier models — see the Status section.

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Turn a visual or interaction technique you already built into a reusable web-design skill, by isolating the one mechanism that makes it work while reproducing its approved reference exactly around that focus, and packaging it with a demo that proves both the mechanism and the visual fidelity. Covers finding the mechanism, naming the technique plainly, disclosing the verified runtime and renderer, auditing reference layers, carrying real numbers instead of adjectives, preserving owned staging, keeping expensive gotchas, declaring the boundary against neighbouring skills, and browser-verifying before claiming it works. Use when a page, canvas scene, shader, scroll effect, layout system, or hover interaction turned out well and should become a skill rather than staying in one project.

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Explain code, concepts, or system behavior with adjustable depth levels

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Human and accessibility skill for antislop. Contrast, keyboard, focus, and states for real people. Includes the contrast checker.

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Mobile layout skill for antislop. Use for layouts that reflow across screen sizes, phone to desktop: grids, overflow, tap targets. Load with the core.

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Code comment hygiene for AI coding agents: remove generic AI-slop comments, keep the valuable ones, never touch the code.

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Copy and text skill for antislop. Use when writing or editing prose: headlines, tone, CTAs, and anti-AI-writing patterns. Load with the core.

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Anti Slop: Rules for AI Coding Agents. The core filter. Load always to stop generic AI slop.

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UI and visual skill for antislop. Use when building or editing any interface: color, layout, components, motion. Load with the core.

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Guide for theming .NET MAUI apps — light/dark mode via AppThemeBinding, ResourceDictionary theme switching, DynamicResource bindings, system theme detection, and user theme preferences. Use when: "dark mode", "light mode", "theming", "AppThemeBinding", "theme switching", "ResourceDictionary theme", "dynamic resources", "system theme detection", "color scheme", "app theme", "DynamicResource". Do not use for: localization or language switching (see .NET MAUI localization documentation), accessibility visual adjustments (see .NET MAUI accessibility documentation), app icons or splash screens (see .NET MAUI app icons documentation), or Bootstrap-style class theming (see Plugin.Maui.BootstrapTheme NuGet package).

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Mobile app pentest for bug bounty (Android APK + iOS IPA) — runtime-first workflow: install app, proxy through Burp/mitmproxy, drive the UI, capture packets, then test the API exactly like a web target; escalate to decompile (apktool/jadx) and Frida/objection only when traffic is SSL-pinned, encrypted, or absent. Covers APK/IPA decompile for hardcoded secrets + hidden API endpoints + base URLs the web app never exposes, exported-activity and deeplink intent injection, WebView addJavascriptInterface bridge abuse, SSL pinning bypass (objection patchapk / Frida CertificatePinner + checkServerTrusted hooks), OkHttp interceptor chain to recover request signing, JNI native-lib triage, and the quick apktool/grep secret + endpoint sweep. Use when the program scope includes a mobile app, when web recon dries up and you need a fresh attack surface, or when traffic is pinned and you must MitM it.

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Find and fix the docs and agent guidance that a branch makes stale - agent skills and their reference files, AGENTS.md and .agents/*.md prompts, READMEs, docs/ folders, and code-adjacent docs that name a changed path, command, symbol, env var, setting, endpoint or behaviour. Use before opening a PR, before pushing new commits to an open PR, and when asked to update docs or skills after a change. The open-pr skill runs it every time.

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Hunt server-side template injection (SSTI) across Jinja2 (Flask/Django), Twig (Symfony), Freemarker (Java), ERB (Rails), Spring, Velocity, Mako, Thymeleaf, Smarty. Detection probes use double-curly and dollar-curly math expressions evaluated server-side. Once an engine is fingerprinted, escalate to RCE via the engine-specific class-walker, callback-registrar, or Execute-utility patterns documented in disclosed reports. Detection patterns: error messages reveal engine, blank or numeric eval reveals expression mode. Targets: email templates, PDF/report generators, CMS preview features, error pages with user input. Use when hunting RCE via template rendering, when content shows engine fingerprints, when finding endpoints that compose strings with user input before render.

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Update peon-ping configuration — volume, pack rotation, categories, active pack, and other settings. Use when user wants to change peon-ping settings like volume, enable round-robin, add packs to rotation, toggle sound categories, or adjust any config.

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Inverse-fold a backbone with SolubleMPNN — ProteinMPNN retrained on a soluble-PDB subset (Dauparas et al. 2022) — for sequences biased toward cytosolic expression and reduced aggregation. Reach for this skill when designs from vanilla ProteinMPNN are aggregating or going to inclusion bodies, when redesigning a membrane-adjacent fold for soluble expression, or when an E. coli expression screen is the next step.

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Industrial AI literature research with mandatory intake questions, venue-aware source prioritization, structured report outputs, and survey draft generation. Use when the user needs up-to-date research on predictive maintenance, intelligent scheduling, industrial anomaly detection, smart manufacturing, cyber-physical systems, edge AI for automation, or crossover robotics-for-industry topics. Also trigger for adjacent terms: "digital twin", "industrial IoT", "Industry 4.0", "manufacturing AI", "factory automation", "process optimization", or "survey draft" in industrial contexts.

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Restructure an academic paper's title and section structure to match a target journal's house style. Asks the user to specify the target journal first, then adjusts main.tex accordingly (title wording and case, section skeleton, heading case, numbering scheme, merging/splitting sections). Trigger when user says "paper style" / "paper-style" / "期刊风格" / "按目标期刊调整" / "调整为XX风格" / "JFE风格" / "convert to journal style" / "restructure for [journal]", or wants a paper's title/sections reformatted for a specific journal submission.

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Analyzes how your campaigns perform across mobile, desktop, and tablet. Identifies where device performance diverges significantly and recommends bid adjustments, campaign splits, or creative/landing page changes to capture the gap. Platform: Google and Meta.

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Tracks daily spend against monthly budget targets across all campaigns and accounts. Tells you exactly where you'll land at current pace, flags campaigns that are over or underspending, and calculates the daily budget adjustments needed to hit your target by month end. Platform: Google and Meta.

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Breaks down campaign performance by geographic location at whatever level matters — country, state, city, DMA, zip code. Flags underperforming geos that are quietly eating budget and high-performing ones that deserve more spend. Recommends geo bid adjustments or campaign splits. Platform: Google and Meta.

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Compares your key metrics against industry benchmarks for your specific vertical, campaign type, and platform. Tells you where you're ahead, where you're behind, and where there's room to improve — adjusted for your account size and spend level, because a $10K/month account and a $500K/month account have different benchmark realities. Platform: Google and Meta.

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Analyzes performance by day of week and hour of day across your campaigns. Identifies when your ads perform best and worst, recommends ad schedule adjustments with estimated savings, and tells you exactly what you'd give up by restricting hours. Platform: Google and Meta.

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Seedance 2.0 video & image generation via LibTV Gateway - AI text-to-video, image-to-video, video continuation, style transfer, and text-to-image using Seedance 2.0 model. Also supports Kling 3.0, Wan 2.6, Midjourney, Seedream 5.0. Trigger phrases: seedance, generate video, make a video, generate image, make an image, draw, libtv, liblib.

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。