「sli」の検索結果

Use when testing file upload endpoints for unrestricted file upload, MIME type bypass, magic byte spoofing, polyglot files, SVG XSS, XXE via Office documents, ZIP slip, and path traversal in filenames. Trigger on: multipart/form-data endpoints, avatar/document upload flows, import-from-file features, profile image, CSV/Excel import, DOCX/XLSX parsing, image resizing pipelines, archive extraction, and any endpoint that stores or serves user-supplied files. Detects extension bypass (shell.php.jpg), null byte injection, double extension, ImageMagick exploits, and content-type confusion.

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Biome v2 unified linter, formatter, and import organizer — single Rust-powered tool replacing ESLint + Prettier with 97% Prettier compatibility and 20x faster performance

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Toolkit for styling artifacts with a theme. These artifacts can be slides, docs, reportings, HTML landing pages, etc. There are 10 pre-set themes with colors/fonts that you can apply to any artifact that has been creating, or can generate a new theme on-the-fly.

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Generate web, mobile, and desktop prototypes, slides, dashboards, and editorial layouts from a single prompt using brand-grade design systems. Use when the user wants /web-prototype, /mobile-app, or /dashboard interactive HTML previews exportable to HTML, PDF, PPTX, or MP4 without Figma.

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Drain the Pharos Dependency Map coverage-audit queues into evidence-refreshed edges, reviewed target dispositions, or documented deferrals. Use monthly after weekly production dependency audits, after dependency coverage drops, or when reviewing adapter gaps, material unlinked slices, symbol leads, duplicate/split groups, or unverifiable published edges.

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Research stablecoin reserve composition, review provenance, custody evidence, dependency edges, and backing-pillar slices; populate the owning reserves sidecar only when explicitly requested.

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Post-mortem analysis of a PhysicsIntern workspace run (Claude Code, Pi, Codex, or OpenCode host). Reconstructs the trajectory from the session record — JSONL file(s) for Claude/Pi/Codex, the SQLite store for OpenCode — audits methodology adherence against the workspace's own CLAUDE.md / AGENTS.md and skill/agent prompts, checks commit discipline and flag dispositions, and assesses substantive quality. Produces a thorough evidence-anchored markdown report. Use after a workspace has been worked on to identify what went well, where the methodology slipped, and what prompts to improve.

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SQL discipline for querying Pulp Perfetto traces (.pftrace) with trace_processor — idempotent CREATE OR REPLACE PERFETTO views, GLOB not LIKE, dur = -1 incomplete-slice handling, EXTRACT_ARG for span args, joining on stable utid/upid, SPAN_JOIN PARTITIONED, and the draft→validate→execute loop. Ships Pulp's CPU/UI trace stdlib plus closed GPU startup, health, and probe views. TRIGGER when writing or debugging SQL over a .pftrace, when `pulp trace query` returns wrong/empty rows, or when the trace-analysis skill needs a query primitive.

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Debug or validate Pulp animations / transitions / scroll behavior using in-process motion fixtures and offline visual analysis. TRIGGER on phrases like "animation is wrong", "transition timing off", "fade too late", "card slides too far", "scroll jumps on restore", "scroll offset wrong on restore", "frame jump in the timeline", "easing looks wonky", "imported Figma motion doesn't match source", "settle time / overshoot / drift / monotonic", "what value does the knob reach at frame N", "record this animation so I can replay it", "this animation is expensive — which one and why", "reduced-motion broken", "scrub a captured fixture". Captures fixtures (.motion.jsonl), and ships assertion helpers (is_monotonic / settling_time_seconds / overshoot / start_delay_seconds / final_value / local_step_outlier_ratio) and the `scroll_geometry` trace for `ScrollView` content-offset / visible-rect / content-size observability. Off by default in prod.

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Diagnose and fix slow pyfixest regression GRIDS (many feols/fepois calls run sequentially) that stay slow despite demeaner_backend="cupy64" and an idle GPU. Use when: (1) a script looping dozens of pf.feols models on a 100k+ row panel takes ~1 min/model, (2) process inspection shows ~1-1.5 cores busy and nvidia-smi shows ~0% GPU utilization with a resident cupy context, (3) planning any worker prompt that will run a model grid (robustness variants x FE structures x domains). Root cause: per-model CPU-side single-threaded fixed costs (formulaic model-matrix build, interaction construction, singleton detection, cluster vcov) dominate wall time; GPU demeaning is a small slice. Fix: shard the model grid across OS processes and/or use pyfixest multiple-estimation syntax; mandate this IN THE WORKER PROMPT.

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Style every matplotlib figure like the Stata 18/19 default (stcolor) scheme — Arial embedded as TrueType, white background, recessive light-gray grid below the data, no top/right spines, unframed legends, and the validated blue/red/gray palette. Use whenever a task generates or restyles charts, plots, or figures for papers, reports, or slides, even if the user doesn't mention Stata — this is the house style for all publication figures. Also use when asked to make figures "look like Stata", match the stcolor scheme, or restyle existing matplotlib output.

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用于安全移除前端死代码、未使用导出、陈旧组件、废弃路由、未使用依赖，或由 knip、depcheck、ts-prune、TypeScript、ESLint、人工审查发现的清理目标。不要用于会改变行为的功能重写；中文触发词包括 死代码、清理未使用、refactor clean、依赖清理、删除无用代码。

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Use when safely removing dead frontend code, unused exports, stale components, obsolete routes, unused dependencies, or cleanup targets found by tools such as knip, depcheck, ts-prune, TypeScript, ESLint, or manual review. Do not use for feature rewrites that change behavior; Chinese triggers include dead code, clean up unused, refactor clean, dependency cleanup, delete useless code.

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Upgrade the vendored NeMo-Skills slice to a newer upstream commit. Use when the user asks to "bump vendored", "sync NeMo-Skills", "update vendored sha", "upgrade NS", or wants to pull a specific upstream fix. Inside the sgl-eval repo.

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Sales enablement, technical presales and deal acceleration for B2B SaaS revenue teams. Use this skill when planning outbound campaigns, running discovery calls, building deal strategy, creating battle cards, writing proposals, responding to RFPs, preparing a product demo, scoping a proof of concept or pilot, answering a security questionnaire or the AI annex buyers now attach to it, executing sales sequences, coaching qualification, or deciding what the website's AI chat agent is allowed to say. Also triggers on: outbound, cold outreach, reply handling, reply classification, out-of-office replies, auto-replies counted as replies, our reply rate is inflated, prospect replied remove me, opt-out by reply, is this list ready to send, launch readiness, CRM segment outbound, we emailed a current customer, outbound hit an open opportunity, ownerless leads, prospects engaging our competitors, competitor connections, displacement list, is this personalization creepy, can we mention they visited our site, signal freshness, deal strategy, pipeline, battle card, objection handling, common objections, why do we keep hearing this objection, mine call transcripts for objections, objection register, proposal, RFP, discovery call, MEDDPICC, sales sequence, qualification, sales engineer, solutions engineer, presales, demo plan, demo script, POC, proof of concept, pilot success criteria, technical evaluation, technical win, security questionnaire, vendor security review, SOC 2 questionnaire, AI security questionnaire, AI annex, AI vendor due diligence, the buyer is asking if we train on their data, do you train on customer data, which model do you use, model provider sub-processor, AI subprocessor disclosure, where does inference run, can the customer turn the AI feature off, AI-CAIQ, CSA AI Controls Matrix, ISO 42001, ISO/IEC 42001, NIST AI RMF, EU AI Act Article 50, are we a provider or a deployer, AI feature disclosure, our model vendor changed and nobody told the buyer, technical objection, mutual action plan, MAP, close plan, mutual close plan, buyer enablement, the deal is stalled, deal stalled after the demo, how do I get this deal to close, close date keeps slipping, JOLT, buyer indecision, no-decision loss, website chatbot, chat widget, AI chat agent, AI SDR, inbound AI SDR, conversational marketing, live chat, what should our chatbot be allowed to say, our chatbot quoted the wrong price, the bot promised a discount, do we have to disclose it is AI, EU AI Act chatbot disclosure, chatbot handoff to sales, chat transcript review, prompt injection on our chatbot, Drift, Qualified, Intercom.

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Use when a task needs the judgment of a Dining Room Attendant or Bartender Helper — triaging which table to bus first during a rush, sequencing a table reset, setting bar-back par levels and restock timing, writing or triaging a side-work checklist, or diagnosing why table turn time or bar throughput has slipped.

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Use when a task needs the judgment of a Dispatcher — assigning a new job to the best available driver/technician in real time, managing an ETA slip and deciding who to notify, triaging a coverage gap in a service territory, sequencing a driver's remaining stops after a delay, or deciding whether a job needs to be reassigned versus the customer rescheduled.

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Use when a task needs the judgment of a cytotechnologist — screening a gynecologic (Pap test) slide and applying Bethesda System criteria, assessing non-gynecologic specimen adequacy (FNA rapid on-site evaluation, body fluid, brushing) against the relevant reporting system, deciding whether a finding can be signed out independently or must be referred to a pathologist, or auditing screening quality metrics (ASC:SIL ratio, rescreen concordance, workload compliance). A reasoning aid, not a substitute for a certified cytotechnologist or pathologist signing out an actual case.

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Use when a task needs the judgment of a Postsecondary Chemistry Teacher — grading a lab report where the numeric result misses the "expected" value but the methodology is sound, distinguishing a conceptual misunderstanding from an arithmetic slip on a problem set, deciding how much procedural freedom to give students on an open-ended lab, or vetting whether a demo/lab procedure is safe to run in this specific room.

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Use when a task needs the judgment of a radio broadcast announcer or on-air disc jockey — backtiming a live break against a hard network join or legal ID, deciding what to cut when a caller or breaking-news read runs long, judging FCC indecency/EAS exposure on live content, structuring a voice-tracked shift so it doesn't read as canned, or diagnosing why a daypart's PPM numbers are sliding.

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Spring Boot 4 testing strategies and patterns. Use when writing unit tests, slice tests (@WebMvcTest, @DataJpaTest), integration tests, Testcontainers with @ServiceConnection, security testing (@WithMockUser, JWT), or Modulith event testing with Scenario API. Covers the critical @MockitoBean migration from @MockBean.

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Scan a project to detect configured quality and testing tools across JVM (Gradle/Maven), Android (AGP/Compose/KMP), Node.js/TypeScript, and Python ecosystems. Cross-reference against research-backed recommendations and assist with setup. Auto-detects project type(s) including monorepos with mixed ecosystems. Use when user asks to "audit tooling", "recommend tools", "quality stack", "what tools am I missing", "setup eslint", "setup detekt", "add coverage", "add ruff", "configure CI quality pipeline", "scan project tools", "tooling audit", "android tooling", "android quality", "compose testing", "kmp testing", or "screenshot testing".

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Guideline for designing, implementing, and verifying secure TypeScript and JavaScript applications following OWASP Top 10 best practices. Use when the user wants to: (1) review TypeScript or JavaScript code for security vulnerabilities, (2) design a secure Node.js, Deno, or browser application architecture, (3) implement security features (authentication, authorization, cryptography, input validation), (4) audit npm/yarn/pnpm dependencies for known vulnerabilities, (5) create security checklists or verification plans, (6) fix security bugs or harden existing TypeScript or JavaScript code, (7) set up security testing and static analysis (ESLint security plugins, Semgrep, Snyk), or (8) handle any TypeScript/JavaScript security concern including injection prevention, prototype pollution, XSS protection, SSRF prevention, secrets management, and secure deployment.

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Defines service level objectives, creates error budget policies, designs incident response procedures, develops capacity models, and produces monitoring configurations and automation scripts for production systems. Use when defining SLIs/SLOs, managing error budgets, building reliable systems at scale, incident management, chaos engineering, toil reduction, or capacity planning.

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。