「strings」の検索結果

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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In-process semantic search over text files or in-memory strings, using Gemini embeddings via the CF AI Gateway. Use when user wants fuzzy/conceptual search where exact-keyword grep would miss — "sessions discussing regulatory constraints", "code about retry logic", "notes mentioning burnout even if the word isn't there". Complements searching-codebases (regex/AST) and extracting-keywords (YAKE). Do NOT use when an exact string/regex match is what's wanted — grep/rg wins on speed and precision there.

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Build a frontend with a deliberate visual direction held through palette, typography, structure, and texture, with on-screen strings that name real information rather than fabricated demo data or filler labels. Use when building or restyling a frontend.

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。

Generate and update language-idiomatic doc comments for TypeScript (TSDoc/JSDoc) and Rust (Rustdoc). Use when asked to "add docs", "document this file", "generate docstrings", "add JSDoc", "add Rustdoc", "write TSDoc", or when working with TypeScript or Rust source files that need documentation comments. Supports file-level and selection-level operations. Deterministic and idempotent — safe to run repeatedly on the same code. Does NOT change behavior, rename symbols, or refactor code.

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Generate and maintain AI-readable markdown documentation for a codebase in docs/. Use this skill whenever the user asks to document their codebase, generate project docs, create architecture docs, update documentation after code changes, build a knowledge base for AI agents, create onboarding docs, map out a codebase, or explain a project structure. Also trigger when users say things like "document this repo", "create docs for my project", "update the docs", "write architecture documentation", "help AI understand my code", "generate a codebase overview", or "maintain docs". This skill is specifically about creating structured markdown files in a docs/ directory that serve as a knowledge base — NOT about writing inline code comments, README files, or API reference docs from docstrings.

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。

Use when reviewing Python changes - Django, Flask and FastAPI handlers, subprocess, SQL, Jinja2, format strings, and data/ML code that loads pickles, models, notebooks or remote model code

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Cut a new timepicker-ui release. Use when the user wants to bump the version, prepare a release, write/update the changelog, or sync version + changelog across README, CHANGELOG.md and the docs-app (changelog/what's-new/version strings). Drives the build-release subagent in an audit → approve → apply flow, then runs a sanity build. Does NOT git-commit/tag or npm-publish — proposes those for the user to run.

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Compact reference for the Hyperliquid API as the desk uses it - endpoints and envelopes, every /info request type, every /exchange action with its signing scheme, order and status vocabularies, asset ids, tick and lot rules, rate limits, WebSocket subscription list, error strings, and where the official docs are. Use to look up an exact field, request type or limit before writing a call, and to map an error string to its cause.

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ALWAYS start here when the user wants a page OR a doc: page, living page, doc, document, living doc, bongo or bongo doc (the old name; new ones are made here, not by a Bongo connector), shared or collaborative doc, write-up, one-pager, memo, brief, proposal, plan, spec, report, meeting notes, notes, checklist, tracker, or anything to read, edit, comment on and share together — 'write this up', 'put this in a doc', 'draft a brief', 'jot this down somewhere we can share and edit', 'turn this file into a page' — the DEFAULT for any of these when no file format is named; a Word/.docx, PDF, .pptx or .xlsx FILE asked for by name goes to that format's skill instead. Also whenever the pages connector is about to be used to create a page. Not for READMEs, code or docstrings. A lowercase /pages as its own word anywhere in the message (not in a URL, path or longer name) invokes this skill whatever else is asked — a summary, an answer, a comparison, a plan: the page is the reply.

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。

Maintain PlainTab runtime UI translations and i18n language packs. Use when adding, editing, formatting, validating, or reviewing UI text in js/i18n/*.js, js/languages.js, data-i18n markup, or t(key) calls; also use when onboarding/help copy or new settings need localized strings.

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Full localization pipeline: scan for hardcoded strings, extract and manage string tables, validate translations, generate translator briefings, run cultural/sensitivity review, manage VO localization, test RTL/platform requirements, enforce string freeze, and report coverage.

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。

Implementing i18n and l10n: extracts hardcoded strings to t() functions, integrates Intl API for date/currency/number formatting, manages translation keys, and adds RTL layout support.

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。

Use this when creating, editing, or reviewing Python code — including type hints, docstrings, naming conventions, imports, error handling, and code structure.

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。

Detect AI-generated writing patterns in developer text — docs, docstrings, commit messages, PR descriptions, and code comments. Use when reviewing any text artifact for authenticity and clarity, or when the user mentions ai writing, ai-generated or robotic writing, text that sounds like AI or ChatGPT, or writing quality. Builds on the docs-style core writing principles.

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。

Register and implement PydanticAI tools with proper context handling, type annotations, and docstrings. Use when adding tool capabilities to agents, implementing function calling, or creating agent actions.

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。

Instrumentation, arranging, and timbral analysis for ensemble and orchestral writing. Covers instrument families (strings, woodwinds, brass, percussion, keyboards), ranges, transpositions, timbral characteristics, scoring techniques, doubling and voicing, Rimsky-Korsakov's orchestration principles, Ravel's and Messiaen's timbral innovations, jazz big band voicing, popular music arranging, and notation conventions. Use when choosing instruments, scoring for ensembles, analyzing orchestral texture, or arranging music for different forces.

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Core programming concepts from variables through recursion. Covers data types (integers, floats, strings, booleans, arrays, objects), variables and scope (lexical, dynamic, block, function, global), control flow (conditionals, loops, pattern matching), functions (parameters, return values, closures, higher-order functions), recursion (base cases, call stack, tail recursion, mutual recursion), type systems (static vs dynamic, strong vs weak, type inference, generics), and error handling (exceptions, Result types, defensive programming). Use when teaching, reviewing, or diagnosing issues with fundamental programming constructs.

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Performs initial binary triage by surveying memory layout, strings, imports/exports, and functions to quickly understand what a binary does and identify suspicious behavior. Use when first examining a binary, when user asks to triage/survey/analyze a program, or wants an overview before deeper reverse engineering.

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This skill should be used when the user asks to "add docstrings", "write error handling", "use naming conventions", "handle exceptions", or when writing new Python code for StreamFlow that requires docstring format, naming conventions, exception handling, or async cleanup patterns.

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Create user-centered, accessible interface copy (microcopy) for digital products including buttons, labels, error messages, notifications, forms, onboarding, empty states, success messages, and help text. Use when writing or editing any text that appears in apps, websites, or software interfaces, designing conversational flows, establishing voice and tone guidelines, auditing product content for consistency and usability, reviewing UI strings, or improving existing interface copy. Applies UX writing best practices based on four quality standards — purposeful, concise, conversational, and clear. Includes accessibility guidelines, research-backed benchmarks (sentence length, comprehension rates, reading levels), expanded error patterns, tone adaptation frameworks, and comprehensive reference materials.

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Use this skill whenever the user wants to analyze binaries with radare2/r2 from the command line, including reverse engineering, disassembly, function analysis, strings/import inspection, patching, binary diffing, hex inspection, or r2 scripting. Also use it when the user mentions PE/ELF/Mach-O/DEX/WASM files together with CLI analysis, `rabin2`, `rasm2`, `radiff2`, `r2pipe`, or asks for radare2 command help on Windows/Linux/macOS.

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。

Use when translating, extracting, or mass-converting hardcoded strings to i18n t() calls. Enforces multi-pass batching, parallel-per-language dispatch, 8 audit gates, and HTML integrity checks. Battle-tested through 21+ batches and 12 bug categories from the March 2026 incidents.

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。

Safe i18n: multi-pass batching, parallel-per-language dispatch, 8 audit gates, HTML integrity. Use for translating or mass-converting hardcoded strings to t() calls.

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。

An agent skill that uses BlurHash, the compact image placeholder representation algorithm by Wolt, to generate and decode tiny hash strings that represent blurred previews of images. Enables instant placeholder rendering in web and mobile applications while full images load.

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。