「its」の検索結果

Wren CLI for AI agents — a semantic SQL layer over 22+ databases (Postgres, MySQL, BigQuery, Snowflake, Spark, …). The actual workflow guides live inside the `wren` CLI itself; this is just a discovery stub. Use whenever the user asks a data question (how many, show me, top N, compare, trend, breakdown, metric, revenue, customers, orders), wants to install / set up Wren Engine, connect a new database, connect SaaS data via dlt (HubSpot, Stripe, Salesforce, GitHub, Slack), generate or regenerate an MDL project from a database schema, enrich a project with business context (enum meanings, units, cubes like ARR / DAU / churn), or turn a project's context layer into a shareable GenBI web app / dashboard and deploy it to Vercel or Cloudflare. Triggers: 'install wren', 'set up wren engine', 'connect database to wren', 'connect SaaS to wren', 'load hubspot / stripe / salesforce data', 'generate mdl', 'scaffold wren project', 'enrich wren context', 'augment my project', 'add cubes', 'build a dashboard', 'make a shareable analytics app', 'deploy my context layer as a web app', 'genbi app', 'wren onboarding', 'wren usage', 'wren generate mdl', 'wren dlt connector', 'wren enrich context', 'wren genbi'.

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Develop and verify a mathematical proof in Lean, continue an incomplete Lean project, or audit whether it proves the original statement. Connect actual inputs to intermediate lemmas, assemble the target theorem, check its transitive axioms, and provide a reproducible handoff. Use when Lean is requested or a specific proof obligation benefits from formal verification; use proof-writer for ordinary mathematical drafting.

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Automatically creates user-facing changelogs from git commits by analyzing commit history, categorizing changes, and transforming technical commits into clear, customer-friendly release notes. Turns hours of manual changelog writing into minutes of automated generation.

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Reverse-lookup glossary that turns a vague description of a web animation or motion effect into its exact term ("the bouncy thing when a popover opens" → Pop in; "the iOS rubber-band scroll" → Rubber-banding). Use when the user asks "what's it called when…", or describes a motion effect without knowing its name and wants the right word to prompt an AI or designer with. For naming an effect, not designing or building one.

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Builds CARLA on Unreal Engine 5.8 from the ue58-dev branch using its CMake build system — configure a preset, then build the carla-unreal / carla-unreal-editor / carla-python-api-install / launch targets — with ROS 2, DLSS, RSS and cook-scope options, and the engine-side build of the CarlaUnreal UE 5.8 fork. There is no Makefile and no Util/BuildTools in this tree, so every UE4 `make` recipe is invalid here. Use when the user asks to "build CARLA UE5", "build ue58-dev", "compile CARLA with cmake", "rebuild the Python API", or hits a CMake configure or target failure.

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Installs the CARLA Autonomous Driving Leaderboard (leaderboard.carla.org) at a chosen version — 1.0, 2.0 or 2.1 — together with the exact scenario_runner branch and CARLA build each one requires, then wires the four-root PYTHONPATH and verifies the whole stack imports. Detects which version an existing checkout is by reading its code, not its branch name. Use when the user asks to "install the leaderboard", "set up the CARLA challenge", "evaluate my driving agent", "reproduce my leaderboard score", or names a leaderboard version.

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Writes an autonomous agent for the CARLA Leaderboard — an AutonomousAgent subclass with setup/sensors/run_step/destroy, a sensor suite inside the track's budget, and the right constructor signature for the leaderboard version (1.0 and 2.x differ). Generates a working skeleton, validates the sensor configuration against the real per-track limits offline, and covers the ROS1/ROS2 agent base classes. Use when the user asks to "write a leaderboard agent", "make my agent work with the leaderboard", "add sensors to my agent", "port my agent to 2.1", or hits SensorConfigurationInvalid.

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Quantizes LLMs to 8-bit or 4-bit for 50-75% memory reduction with minimal accuracy loss. Use when GPU memory is limited, need to fit larger models, or want faster inference. Supports INT8, NF4, FP4 formats, QLoRA training, and 8-bit optimizers. Works with HuggingFace Transformers.

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Identifies security vulnerabilities, generates structured audit reports with severity ratings, and provides actionable remediation guidance. Use when conducting security audits, reviewing code for vulnerabilities, or analyzing infrastructure security. Invoke for SAST scans, penetration testing, DevSecOps practices, cloud security reviews, dependency audits, secrets scanning, or compliance checks. Produces vulnerability reports, prioritized recommendations, and compliance checklists.

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Required before calling image_generate. Create and edit images from a prompt — generate an image, make a picture / logo / illustration / icon / banner / poster / thumbnail / hero image / mockup / product shot / social graphic, or edit / restyle / combine existing images from reference images. Uses OpenAI gpt-image-2 through the Hive image service, billed to the user's Hive credits like an LLM call (no API key needed). Teaches the exact call shape, the quality/cost tradeoff (quality="low" is the default and cheapest), reference-image editing, how to show the result to the user with attach_file, and the failure modes (out of credits, model unavailable, moderation).

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Detects when Luminance, Kira, or a similar bulk-review tool is in use, hands off the high-volume clause extraction to it, and QAs its output per the trust level in `~/.claude/plugins/config/claude-for-legal/corporate-legal/CLAUDE.md`. Use when user says "send to Luminance", "bulk review", "AI extraction", or when diligence-issue-extraction hits a high-volume category.

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Audits Convex performance for reads, subscriptions, write contention, and function limits. Use for slow features, insights findings, OCC conflicts, or read amplification.

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Hunts for the other instances of a bug already found — the variants of one root cause across a codebase. Use immediately after a vulnerability, logic bug, or bad pattern turns up in a specific file and the question becomes where else it occurs, including the bare conversational form ("are there others like this?", "is this the same bug?"). Also for generalizing one known instance into a CodeQL or Semgrep query for its whole pattern family, and for triaging a set of look-alike candidates against a known root cause. Not for initial discovery with no bug in hand.

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Sets up and runs Ruzzy, Trail of Bits' coverage-guided Ruby fuzzer and the only production-ready one for the language. Covers harness structure, fuzzing pure Ruby and the native C extensions in gems, and sanitizer builds. Use when fuzzing a Ruby library or gem, testing a Ruby C extension for memory safety, or asking how to fuzz Ruby at all.

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Generates Claude Code skills from the Trail of Bits Testing Handbook (appsec.guide), analyzing handbook pages and emitting SKILL.md files with the structure each skill type requires. Use when creating or refreshing a skill from handbook content, or when the user names the testing handbook or appsec.guide. Not for answering security testing questions — the generated skills cover those.

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Scans Android APKs for Firebase security misconfigurations including open databases, storage buckets, authentication issues, and exposed cloud functions. Use when analyzing APK files for Firebase vulnerabilities, performing mobile app security audits, or testing Firebase endpoint security. For authorized security research only.

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。

Gets independent code reviews from Codex or Antigravity for uncommitted changes, branch diffs, and commits. Use when the user requests an external review, a second opinion on code, a codex review, a gemini review, an antigravity review, or /second-opinion.

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Guides through Trail of Bits' 5-step secure development workflow. Runs Slither scans, checks special features (upgradeability/ERC conformance/token integration), generates visual security diagrams, helps document security properties for fuzzing/verification, and reviews manual security areas. Use when securing a smart contract end to end rather than hunting one bug, checking a project on every check-in or before deployment, triaging a Slither report, or asking where to start on smart contract security.

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Audits a project's dependencies for supply-chain risk: version-matched advisories for direct dependencies and the full lockfile tree, abandoned or archived upstreams, npm publisher concentration, and install-time script execution. Use when asked to audit dependencies, assess supply-chain or third-party package risk, or review a dependency tree before an engagement.

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Smart contract development advisor based on Trail of Bits' best practices. Analyzes codebase to generate documentation/specifications, review architecture, check upgradeability patterns, assess implementation quality, identify pitfalls, review dependencies, and evaluate testing. Use when asking whether a smart contract project follows development best practices, reviewing on-chain/off-chain split, upgradeability, or delegatecall proxy patterns against guidelines, or seeking recommendations on contract design, inheritance, events, documentation, dependencies, or test strategy.

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Interprets Culture Index (CI) surveys, behavioral profiles, and personality assessment data. Supports individual profile interpretation, team composition analysis (gas/brake/glue), burnout detection, profile comparison, hiring profiles, manager coaching, interview transcript analysis for trait prediction, candidate debrief, onboarding planning, and conflict mediation. Accepts extracted JSON or PDF input via OpenCV extraction script. Use when the user shares a Culture Index PDF or JSON profile, asks what someone's CI traits mean, compares Culture Index profiles across a team or against a hiring profile, or asks about burnout risk from Survey-versus-Job gaps.

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。

Prepares codebases for security review using Trail of Bits' checklist. Helps set review goals, runs static analysis tools, increases test coverage, removes dead code, ensures accessibility, and generates documentation (flowcharts, user stories, inline comments). Use when preparing your own codebase to be audited by someone else, getting a repository review-ready before an external security review, deciding what to fix before auditors start, or asking what assessors need from a project. For understanding unfamiliar code you are about to audit, use audit-context-building instead.

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。

Token integration and implementation analyzer based on Trail of Bits' token integration checklist. Analyzes token implementations for ERC20/ERC721 conformity, checks for 20+ weird token patterns, assesses contract composition and owner privileges, performs on-chain scarcity analysis, and evaluates how protocols handle non-standard tokens. Use when integrating or accepting arbitrary ERC20/ERC721 tokens, auditing a token implementation for standards conformity, or assessing risk from weird tokens such as fee-on-transfer, rebasing, missing return values, or blocklists.

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。

Validates security patches with reproducible baseline-versus-patched evidence, including original exploits, root-cause variants, behavior preservation, regressions, and newly introduced security failures. Use after a patch exists and before accepting, merging, or reporting it as fixed; also use when an AI-generated patch, remediation commit, pull request, or proposed upstream fix needs adversarial post-patch validation across any language.

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。