「sem」の検索結果

Test application security against OWASP Top 10 (2025) with automated CI tooling: OWASP ZAP (DAST), dependency/supply-chain scanning (OSV-Scanner, SBOM, provenance), Semgrep SAST, auth/session tests (JWT, OAuth, RBAC), and XSS/CSRF/SQLi/SSRF Playwright patterns. Use when: "security test," "OWASP," "vulnerability," "ZAP," "XSS," "SSRF," "dependency scan," "auth testing," "OWASP LLM Top 10." Scope is automated scanning + negative-path security tests in CI, not manual penetration testing. Not for: mapping security controls to regulations (SOC 2, HIPAA, PCI, GDPR) — use compliance-testing; pipeline stage wiring and deploy gating mechanics — use ci-cd-integration; purely functional API auth/input tests with no attacker model — see api-testing; testing your product's own LLM features or defending the agent itself (prompt-injection detector, indirect injection, jailbreak red-teaming) — use ai-system-testing. Related: ci-cd-integration, compliance-testing, api-testing, shift-left-testing, ai-system-testing.

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Summarize a contributor's Git history, a provided work log, or both into a concise, review-friendly self-evaluation for quarterly, semi-annual, or promotion cycles.

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Learn the target framework from enabled knowledge tools and implement a baseline GPU kernel. Use this skill to understand compute semantics, determine the target platform and framework, search reference implementations, and produce a correct V0 baseline with performance records for later profile-driven optimization.

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Cut a new TMDb release — work out the next SemVer version from the evidence, do the pre-tag housekeeping a tag would otherwise freeze in place, draft release notes, then tag and publish the GitHub release. Presents the version (with its reasoning) and the full notes for approval and STOPS; nothing is tagged or published until you say go.

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Написание русскоязычной статьи или лонгрида с нуля: бриф, фактура, структура, черновик и самопроверка по методу Людмилы Сарычевой. Use this skill whenever the user asks to write, outline, structure, draft, assemble, or turn notes/interviews/materials into a Russian article, newsletter essay, longread, case, guide, or expert text. Триггеры: «напиши статью», «собери лонгрид», «напиши текст для блога про…», «есть интервью с экспертом, сделай статью», «составь план статьи», «придумай структуру», «собери материал», «сделай черновик», «напиши колонку», «из интервью в статью», «план лонгрида», «текст с нуля», «помоги написать материал». Для коротких постов в соцсети см. skill post; для правки готового текста — redaktura; для текстов интерфейса — ux-copy; для настройки проекта — redpolitika.

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Autonomously cut a Rove (`@sma1lboy/rove`) release end-to-end — detect the semver bump from pending changesets (flagging an upstream `minor` you didn't intend), run the release gates, dispatch the Changesets workflow (or run `scripts/release.sh` locally), then poll the GitHub Actions Release workflow with `gh` until npm publish completes, diagnosing CI failures (npm token, registry 404, lint, branch mismatch) instead of leaving them silent. Use when the user says "cut a release", "ship a version", "release Rove", "release rove", "发版", "release.sh", or "bump the version". Never force-pushes; always verifies the release landed on `main`.

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Apply when designing types, reviewing a function signature, or writing code in any statically-typed language. Make illegal states unrepresentable, brand semantic primitives, parse external data at boundaries, refuse to lie to the compiler, exhaust variants, derive from authoritative schemas.

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Analyze indexed codebases via graph database (neug) and vector index (zvec). Covers call graphs, dependencies, dead code, hotspots, module coupling, architecture reports, semantic search, impact analysis, bug root cause from GitHub issues, class diagrams (UML), and PR review (risk scoring, conflict detection, auto-merge candidates, labeling). Also covers creating, inspecting, and repairing a CodeScope index. Use for: code structure, who calls what, why something changed, similar functions, module boundaries, bug tracing, class relationships, PR risk/conflicts, or any question benefiting from a code knowledge graph. Applies when a `.codegraph` index exists in the workspace, or when the user wants to create one.

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Audit project dependencies for outdated packages, security vulnerabilities, and breaking changes. Use when someone asks to "check for outdated packages", "audit dependencies", "find vulnerable dependencies", "plan dependency upgrades", "detect breaking changes", or "update npm packages safely". Analyzes package.json/lock files, cross-references changelogs, and produces ordered upgrade plans.

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Assists with building real-time reactive backends using Convex. Use when creating databases with automatic client sync, reactive queries, file storage, scheduled functions, or full-text and vector search. Trigger words: convex, reactive backend, real-time database, useQuery, useMutation, convex functions, convex schema.

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commitlint checks that Git commit messages follow the Conventional Commits format (type(scope): subject) and fails when they do not. Use when a user asks to standardize commit messages, enforce commit conventions with a husky commit-msg hook, lint commits or PR commits in CI, customize rules, or prepare commits for automated changelogs and semantic versioning.

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Cloudflare Workers is an edge computing platform for running JavaScript, TypeScript, and WebAssembly close to users worldwide with sub-millisecond cold starts. Use when working with the Workers runtime, the Wrangler CLI, KV, D1, R2, Durable Objects, Queues, or Hyperdrive, or when a user asks to build and deploy an edge function.

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Cohere API for enterprise NLP — embeddings, reranking, RAG, and text generation. Use when building RAG pipelines, semantic search, document reranking, or enterprise NLP applications. Command R+ excels at tool use and retrieval-augmented generation; Embed v3 and Rerank 3 are best-in-class for search quality.

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Automate video editing in Blender's Video Sequence Editor with Python. Use when the user wants to add video, image, or audio strips, create transitions, apply effects, build edit timelines, batch assemble footage, estimate render times, or script any VSE workflow from the command line.

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Generate short-form videos with AI — script writing, text-to-speech narration, stock footage selection, subtitle generation, and video assembly. Use when: creating TikTok/YouTube Shorts/Reels content, automating video production, building content pipelines.

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Generate changelog entries and GitHub releases from git history. Categorizes commits into features, fixes, breaking changes, and docs. Supports conventional commits, PR-based grouping, and semantic versioning. Creates formatted CHANGELOG.md entries and GitHub releases.

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Use when computing orbits, planning maneuvers, propagating trajectories, or analyzing orbital perturbations for spacecraft or celestial bodies. Use when "orbit, trajectory, maneuver, delta-v, Hohmann, Keplerian, perturbation, J2, TLE, orbital elements, semi-major axis, eccentricity, inclination, RAAN, spacecraft propagation, Lambert solver, interplanetary, " mentioned.

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The craft of designing icons that communicate instantly across cultures, contexts, and scales. Icon design bridges semiotics, cognitive psychology, and visual craft to create symbols that users understand without thinking. Great icons are invisible in the best way - they convey meaning so naturally that users never pause to decode them. This skill covers icon grid systems, optical alignment, stroke consistency, metaphor selection, scalability across sizes, SVG optimization, and icon set coherence. The best icon designers understand that icons are a visual language - each icon must speak the same dialect while carrying its own distinct meaning. Use when "icon, iconography, symbol, glyph, icon set, icon library, pictogram, svg icon, icon grid, icon pack, feather icons, lucide, phosphor, heroicons, icon system, icon style, icons, iconography, svg, symbols, glyphs, pictograms, ui-icons, icon-set, visual-design, design-system" mentioned.

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Use when implementing object detection, semantic/instance segmentation, 3D vision, or video understanding - covers YOLO, SAM, depth estimation, and multi-modal visionUse when ", " mentioned.

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World-class color theory expertise combining the scientific precision of Josef Albers' "Interaction of Color," the systematic thinking of color systems from Pantone and RAL, and the perceptual psychology insights from researchers like Bevil Conway. Color is not just aesthetics - it's communication, emotion, and usability compressed into wavelengths. Great color work is invisible when done right. Users don't notice "nice colors" - they notice when they can't read text, when buttons don't look clickable, when errors don't feel urgent, or when the interface feels "off" without knowing why. Color theory is the science of making the right thing feel obvious. Use when "color theory, color palette, color scheme, color harmony, complementary colors, analogous colors, contrast ratio, dark mode colors, light mode, color tokens, semantic colors, color accessibility, color blindness, color psychology, color system, brand colors, data visualization colors, color, design, accessibility, contrast, dark-mode, theming, tokens, wcag, palette, harmony" mentioned.

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Checkable-ish surface presentation signals a reviewer notices first — duplicate/near-identical tables, leftover pipeline/template strings, too-few or LLM-looking figures, and page-padding. AUXILIARY ONLY and weak by design: a deterministic pass (tools/check_presentation.py — dup-table + pipeline-artifact) plus a fresh cross-model GROSS-cases-only semantic pass (thin-float, LLM-figure, page-padding), every above-info finding span-anchored to the evidence ledger (claims.json). The adjudicator CAPS everything at minor (SURFACE_ONLY_SKILLS + SURFACE_PATTERNS) — these contribute at most SOFT_FLAGS, never a HARD verdict — default false_positive_risk:high. NOTE: the pure AI writing-STYLE impressions (AI-flavor prose, defensive 'not-X-but-Y' hedging, narrative-arc, jargon-stuffing, invented codenames) MOVED to the zero-verdict-weight AIS track — for those use skills/ai-style-impressions, NOT this. Emits presentation-signals.findings.json; NEVER computes the verdict. Triggers: "presentation signals", "surface check", "duplicate tables", "排版信号".

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Build the deterministic evidence ledger (artifact_manifest.json + claims.json) that every other Anti-Autoresearch auditor reads. One pass inventories artifacts, derives the observability level (L0 PDF-only / L1 +LaTeX / L2 +repo+results) by fixed rule, and extracts span-anchored, hashed, checkable claims (numbers, comparisons, scope, method, baselines, citations, captions, table cells) into claims.json. An OPTIONAL additive cross-model pass ADDS span-anchored semantic claims — method, theorem statements with their assumptions, definitions, proof/derivation steps and equations, scope, baselines, conclusions, the motivation span, and reproducibility-artifact references (the proof, derivation, and structure anchors the family B/D/G auditors need) — it never invents a number, emits a finding, or computes a verdict. Run FIRST, before any audit skill. Triggers: "build the ledger", "extract claims", "prep for integrity audit", "evidence ledger", "建证据账本".

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Flagship intra-paper self-consistency forensics: does the paper contradict ITSELF across abstract/intro/tables/body/appendix, and does the method DESCRIBED match the method EVALUATED? Needs no external ground truth — works PDF-only (L0). Runs a deterministic arithmetic pass + a fresh cross-model semantic pass, every finding span-anchored to the evidence ledger (claims.json), reviewer≠adjudicator. Emits consistency-audit.findings.json; NEVER computes the verdict. Triggers: "consistency audit", "check the paper against itself", "self-consistency", "内部自洽".

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Transparent, itemized impressions of AI-generated WRITING STYLE — the repo's ONLY non-integrity track. Two passes: a deterministic defensive-hedge density screen (tools/check_ai_style.py, AIS-DEFENSIVE-HEDGE) plus a fresh cross-model GROSS-cases-only semantic pass over the 13 AIS-* style tells (broken narrative arc, LLM phrase tics, jargon-stuffing, invented codenames, clause/formula walls, gratuitous pseudocode, bullet overuse, bold-module spam, restatement loops, focus drift, single-style figures, appendix dumping). Every finding is named, LOCATED, span-anchored to the evidence ledger (claims.json), carries not_integrity_finding:true + false_positive_risk:high + an fp_case, and gets ZERO verdict weight: the adjudicator forces it to info, excludes it from overall_verdict, and renders it in a SEPARATE report section. NOT an AI-text classifier — no scores, no "this is AI-written", no authorship probability; a paper can be CLEAN_GIVEN_EVIDENCE and still list many. Emits ai-style-impressions.findings.json; computes NO verdict; if a tell is actually substantive it routes to the integrity auditor. Triggers: "AI style", "vibe check", "writing fingerprint", "AI 文风", "vibe paper".

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。