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Collect posts from public Facebook Pages or personal profile timelines as structured data. Capture text, author details, reactions, comments and share counts, plus media information, with date options and pagination for larger collections.
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Sort open GitHub issues by category, risk, inactivity, and possible duplication. Examine selected reports in detail, identify missing information, and prepare comments, labels, or closures for user approval before taking action.
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Review code changes and pull requests for correctness, security, performance, and test quality. The skill helps prioritize issues, write specific and constructive feedback, and create shared review checklists and standards for a development team.
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Manage GitHub pull requests, which propose code changes, and issues through a central MCP server. Create and update records, add comments, request reviewers, check changed files and automated checks, and merge pull requests.
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Reduce GitHub API calls during issue monitoring by scanning basic issue details first, then fetching full descriptions and comments only for selected issues. Filters use assignees, status labels, and title patterns to narrow the list.
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Handle pull request review feedback by collecting comments, grouping requests, planning fixes, and validating changes before replying. The workflow covers team members and trusted GitHub automation, with individual replies and resolution of addressed discussion threads.
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Implement and test Go codemods, automated migration routines used by gh aw fix. The guide covers targeted configuration changes, preserving comments and document content, safe repeated execution, and checks for registration and execution order.
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Test whether user input becomes HTML markup in pages or notification emails instead of appearing as plain text. Use distinctive test markers to document reflected or stored injection, assess visible effects, and distinguish HTML injection from JavaScript execution.
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Draft comments on LinkedIn posts from their URLs, with one to three options in your voice and a suggested reaction. Review drafts before publishing through Publora, or prepare a reshare with optional commentary.
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Plan weekly LinkedIn content, draft posts and replies, review wording, and publish after approval. This collection of twelve skills also covers profile improvements, audience analysis, and team posting programs, with manual publishing available without setup.
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Draft a LinkedIn post from a topic and audience, choosing among 20 opening and structure formulas by the response you want. Includes founder-focused angles, wording cleanup, optional visuals, and scheduling through Publora after approval.
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Review LinkedIn posts and comments for artificial wording and mechanical sentence patterns. Get proportionate edits with explanations, audit a finished draft without rewriting it, or build a voice profile from previous posts.
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Review code changes and pull requests for correctness, security, performance, and test quality. The skill helps prioritize issues, write specific and constructive feedback, and create shared review checklists and standards for a development team.
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Revise a LaTeX academic manuscript using internal review comments, annotations, or tracked changes. Compare original passages with proposed revisions, approve each change individually, and receive a summary of applied and skipped items.
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Revise a LaTeX academic manuscript from referee reports, approving changes comment by comment. The skill separates requests for additional analysis from text edits and prepares a revision plan or a revised manuscript with a detailed response letter.
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Coordinate research, drafting, citation and data verification, AI review, revision, and final formatting through connected skills. Start from a research topic, an existing manuscript, or reviewer comments, with progress tracking and user checkpoints.
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Simulate academic peer review through five reviewer roles tailored to the paper's field. Receive separate critiques, an editorial decision, and a prioritized revision roadmap, or check whether a revised manuscript addresses earlier comments.
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Review a branch against main and clean up unnecessary AI-generated code, including excess comments, unusual defensive checks, type workarounds, and deep nesting. Make focused edits that match surrounding code while preserving behavior unless fixing a clear bug.
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Evaluate whether a creator reaches the intended market using public profiles, comments, and available audience data. Get an evidence table, an audience fit rating, and a sponsorship recommendation with a stated confidence level.
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Investigate product demand through Reddit, social posts, transcripts, and comments. Get sourced customer pain points, buying signals, existing alternatives, and objections, plus messaging angles and product tests grounded in the language people actually use.
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Run a repository's lint review, an automated check of pull request changes, and report every finding without editing code. Findings can be printed locally or posted as individual GitHub comments, with a fallback for lines outside the diff.
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Build two to four audience profile cards from follower characteristics, needs, content preferences, and channel information. Use account data, comments, and messages to guide content choices, prioritize outreach channels, and plan how to validate the profiles.
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Turn audience comments into reply templates, handling guidelines, and future content ideas. For negative incidents, assess severity and draft responses or statements, shared talking points, actions to avoid, and recommended response timing.
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Coordinate investigation, fixes, tests, and commits for bugs, Claude Code hooks, dependency errors, and pull request feedback. Route work to specialized skills and agents, with human review checkpoints, diagnosis-only options, and a handoff record.
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