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Found 345 Skills
Convert Markdown documents to professionally typeset PDF files with reportlab. Handles CJK/Latin mixed text, fenced code blocks, tables, blockquotes, cover pages, clickable TOC, PDF bookmarks, watermarks, and page numbers. Supports multiple color themes (Warm Academic, Nord, GitHub Light, Solarized, etc.) and is battle-tested for Chinese technical reports. Use this skill whenever the user wants to turn a .md file into a styled PDF, generate a report PDF from markdown, or create a print-ready document from markdown content — especially if CJK characters, code blocks, or tables are involved. Also trigger when the user mentions "markdown to PDF", "md2pdf", "any2pdf", "md转pdf", "报告生成", or asks for a "typeset" or "professionally formatted" PDF from markdown source.
Automatically creates user-facing changelogs from git commits by analyzing commit history, categorizing changes, and transforming technical commits into clear, customer-friendly release notes. Use this skill whenever the user mentions changelogs, release notes, version updates, "what changed", product updates, app store descriptions, or needs to summarize recent development work for non-technical audiences.
Facilitates the final step of a proven customer-interview method: distilling everything a round of interviews produced (GOALS.md, HYPOTHESES.md, QUESTIONS.md, and a directory of per-interview debriefs) into a single FINAL-REPORT.md the whole company can use. Top: a summary as brief as possible without losing salient information. Below: numbered findings (F1, F2, …) tagged validated / disproved / directional / watch / untested, every one citing debriefs and quoting customers verbatim, plus per-area briefs that marshal the evidence for ideal-customer definition, positioning, pricing, marketing & sales, and product priorities. Load when the user says 'write up what we found from the interviews,' 'summarize the interview results for the team,' or 'turn the interviews into a report.' Do NOT load for updating hypotheses from interviews (the synthesis step), for recording one conversation (the debrief step), or for actually doing the positioning, ideal-customer, or pricing work the report feeds.
Query and summarize CawPlan QA Insights A3 TestRail execution progress for a Version or Run: execution rate, pass quality, deduplicated failure/blocker queue, and per-Ticket progress tables. Use when: A3 execution progress, test execution summary, TestRail run progress, execution summary, failure list, pass rate, untested count, status counts, custom status, 查询A3执行进度、查询Version测试进度、查询TestRail Run进度、执行摘要、失败列表、通过率、未执行统计、全status统计。 NOT for: importing test cases, creating TestRail Plans/Runs, filing defects (use `cawplan-defect-ticket`), or release risk assessment.
Search and read the official Slack platform documentation at docs.slack.dev. Use this skill to answer conceptual or how-to questions about Slack features. You can also use it to look up, fetch, or summarize specific guide pages from provided docs.slack.dev links.
Apply the skills collection's UPGRADE_NOTES.md after an upgrade. Re-syncs installed tracker and browser-provider descriptors while preserving local edits, reports custom-provider gaps, checks pipeline config and installed artifacts, and summarizes exactly what changed.
Analyze local ChatLab chat records via clb CLI. Used when users ask external Agents to review conversations, find evidence, summarize topics, compare members, or analyze specified person or group relationships based on imported ChatLab data.
Guidance for detection engineering in Microsoft Sentinel — building, testing, deploying, and maintaining analytics rules, hunting queries, and SOAR automation. Covers the Content Hub solution model, MITRE ATT&CK mapping, scheduled vs near-real-time (NRT) vs Fusion vs anomalies analytics, KQL detection patterns (joins, summarize, bin, materialize), entity mapping and incident enrichment, custom detections from Defender XDR vs Sentinel-only, automation rules, playbooks (Logic Apps), watchlists, threat intel matching, content as code with Azure DevOps / GitHub repositories integration, and detection lifecycle (validate → tune → version). WHEN: Sentinel analytics rule, KQL detection, MITRE mapping, Sentinel content hub, scheduled analytics, NRT rule, hunting query, Sentinel automation rule, Logic App playbook, custom detection, repositories Sentinel CI/CD, detection-as-code, watchlist, threat intel matching analytics, fusion alerts, anomalies, incident enrichment, entity mapping. DO NOT USE for Sentinel architecture/onboarding (use sentinel), Defender XDR custom detections only (overlap—use the side that owns the data), or generic KQL training.
Tests map primitive with additional operation arguments