「traction」の検索結果

Audit commercial lease compliance systems -- CAM reconciliation accuracy (pro-rata share, caps, admin fees), lease abstraction completeness, critical date tracking and deadline alerting, clause compliance monitoring (insurance certificates, permitted use, co-tenancy).

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Audit e-discovery and litigation document review systems -- data collection pipelines (PST, MBOX, SharePoint, Slack), document processing (OCR via Tesseract/ABBYY, metadata extraction, deduplication), Technology Assisted Review (TAR 1.0/2.0/CAL with recall/precision tracking).

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Audit contract management codebases for clause extraction accuracy, obligation tracking completeness, risk scoring model quality, renewal management, SLA monitoring, liability exposure, force majeure handling, and IP assignment detection..

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Transform reference photographs and visually dense images into sparse, recognizable, high-saturation flat-vector-style illustrations through semantic compression rather than literal tracing. Use when the user asks for photo-to-flat illustration, photo-to-vector-style redraw, 照片转扁平插画, 无渐变高饱和插画, iconification, visual simplification, composition-preserving abstraction, a consistent minimal illustration set, or revisions that remove gradients, glow, texture, blur, clutter, text, logos, numbers, or UI. Default to human-perceived semantic complexity 6 out of 10 or lower, fixed-HSB solid fills, crisp boundaries, and no gradients or light-halo effects.

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Extract, suggest, and sync tags and categories for blog posts across all major CMS platforms. Supports WordPress REST API, Shopify GraphQL, Ghost Content API, Strapi REST/GraphQL, and Sanity GROQ. Generates tag suggestions from content analysis (keyword frequency, heading extraction, semantic grouping), enforces minimum post-count thresholds to prevent thin tag archives, and syncs taxonomy via authenticated API calls. Use when user says "tags", "categories", "taxonomy", "tag suggestions", "sync tags", "WordPress tags", "Shopify tags".

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Create and manage writing personas with NNGroup 4-dimension tone framework (Funny-Serious, Formal-Casual, Respectful-Irreverent, Enthusiastic-Matter-of-fact). Personas define readability targets, sentence length distribution, vocabulary tier, contraction frequency, and summary box label. Used by blog-write and blog-rewrite to enforce consistent voice. Use when user says "persona", "voice", "tone", "writing style", "brand voice", "create persona", "use persona".

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Restructure code while preserving behavior. Use for extraction, consolidation, ownership moves, or cleanup where verification must bracket structural edits.

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Translates Snowflake dbt SQL models to Standardized BigQuery SQL. Handles SQL compilation, Jinja macro placeholder masking, BigQuery Translation Service migration workflows, AST-based config transformations, explicit type casting, JSON extraction standardization, and deduplication. Use when migrating Snowflake dbt pipelines or models to Google Cloud BigQuery. Don't use for generic BigQuery queries or non-Snowflake SQL migrations.

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Refactor messy Pharo code safely through the genie MCP tools, working from the live image (no files). Use for renames, method extraction, moving behavior, or general cleanup of existing code.

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Independent senior-engineering quality auditor — architecture, ownership, domain integrity, abstraction, and maintainability. Use as `--gate` inside the ck:cook pipeline (blocks a phase on BLOCKER/HIGH/current-change MEDIUM findings) or call directly with `--audit`, `--diff`, `--changed` on any repository, directory, or file. Never writes tests and never edits production code — it returns structured findings only; ck:cook or ck:fix apply the fix.

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。

Independent senior-engineering quality auditor — architecture, ownership, domain integrity, abstraction, and maintainability. Use as `--gate` inside the ck:cook pipeline (blocks a phase on BLOCKER/HIGH/current-change MEDIUM findings) or call directly with `--audit`, `--diff`, `--changed` on any repository, directory, or file. Never writes tests and never edits production code — it returns structured findings only; ck:cook or ck:fix apply the fix.

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Design Agentforce conversations that span multiple turns without losing context: session variable scoping, conversation memory, clarifying-question patterns, topic-to-topic (now subagent) handoff, and the right abstractions for accumulating state across turns. NOT for deciding the topic boundaries themselves or out-of-scope behavior — use agentforce/agent-topic-design. NOT for single-turn agent actions and their input/output contracts — use agentforce/agent-actions.

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Use when configuring or operating the native Salesforce Data Export Service (Setup → Data Export) — weekly or monthly CSV export, attachment inclusion, file-size split, the 48-hour download window, and the gap between this free utility and the paid Salesforce Backup and Restore product. Triggers: 'data export service', 'weekly export', 'export attachments', 'data export 48 hour download', 'data export missing objects', 'is weekly export a backup', 'bulk api 2.0 query job export', 'data loader extract batch', 'process-conf.xml export', 'incremental export systemmodstamp', 'export inventory retention owner'. NOT for the paid Salesforce Backup and Restore product (use that as a separate tool), NOT for Bulk API extraction (use data/bulk-api-patterns), NOT for HA/DR architecture (use architect/ha-dr-architecture).

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Team-wide charter and history optimization through skill extraction

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Use automatically for PDF tasks, including Chinese requests like 查看PDF, 读取PDF, PDF里写了什么, 总结PDF, 分析PDF, 提取PDF文字, 识别PDF内容, or questions about any .pdf file. Prefer text extraction with pdfplumber/pypdf first, then render pages with Poppler when layout, images, tables, stamps, watermarks, or scanned content matter.

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Prevents overengineering when designing, implementing, refactoring, or reviewing software. Use when the user asks to implement a feature, add an abstraction, refactor code, review a design, simplify existing code, or make an architectural decision. Also triggers when the user asks if code is too complex, whether to abstract something, or how to structure something. Do not use for deployment, infrastructure configuration, or debugging runtime errors unrelated to code structure.

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Problem redefinition before problem solving. Takes a stated problem, extracts its premises, then spawns reframers applying different lenses (problem-vs-symptom, scope-shift, stakeholder-shift, level-of-abstraction, time-horizon, inversion, category-shift, constraints-shift) in isolation, and synthesizes the alternatives into a report with an orchestrator recommendation. Produces feedback only — no code, no tickets, no artifacts.

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Design task-local harnesses, eval gates, and reusable skill extraction for Claude dynamic workflow mode and other adaptive agent harnesses.

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[DEPRECATED - use continuous-learning-v2] Legacy v1 stop-hook skill extractor. v2 is a strict superset with instinct-based, project-scoped, hook-reliable learning. Do not invoke v1; route continuous learning, session learning, and pattern extraction requests to continuous-learning-v2.

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Automates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, test web applications, or extract information from web pages.

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Convert unstructured documents into LLM-ready structured data. Supports PDF, Word, PPT, and images; extracts paragraphs, formulas, tables, charts, and other elements in one step; generates up to 8 levels of headings; and outputs Markdown organized in reading order. Useful for field extraction, comparison and validation, knowledge retrieval, and intelligent Q&A.

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Compare two local documents and convert differences into LLM-ready Markdown in one synchronous call. Supports PDF, Word, PPT, and image documents accepted by Calliper, and returns a structured markdown diff table suitable for change review, clause comparison, downstream extraction, and rule-based validation.

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AI-powered revenue intelligence: sales call insight extraction, content-to-revenue attribution, and multi-source client reporting. Use when asked to analyze sales calls, build revenue attribution models, or generate client reports.

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Measurement-driven code refactoring — profile before changing, measure after, keep only if metrics improve. Covers complexity reduction, extraction patterns, and bulk refactoring for mechanical changes across many files.

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。