22 plugins from public Claude Code marketplaces. Each one bundles skills, slash commands, agents, hooks or MCP servers that install together.
Showing 22 plugins that match these filters.
claude-code-workflowswshobson/agents1
trailofbitstrailofbits/skills1
awesome-journal-skillsbrycewang-stanford/Awesome-Journal-Skills1
social-media-research-skillsScrapeCreators/social-media-research-skills1Submit the repo with .claude-plugin/marketplace.json and we list every plugin.
Submit marketplaceRequire a human approval signal before an AI agent can post PR reviews, comments, merges, or writes to CI configuration. Joins protect-mcp and signed-audit-trails in the governance category; composes with protect-mcp for runtime enforcement.
Audits code for unit and decimal-scaling bugs and annotates it with dimensional comments recording the units of each value. Use when someone asks to audit or review code for unit confusion, decimals or scaling mismatches, WAD/RAY scale errors, or the wrong token's decimals; to annotate units in a codebase; or to perform a dimensional analysis of a DeFi protocol, offchain code, or other arithmetic-heavy codebase. Catches formulas that combine values carrying different units or scales.
Twelve-skill bundle covering the The Yale Law Journal (YLJ) manuscript lifecycle for Articles, Essays, Features, student Notes & Comments, and the online YLJ Forum.
academic
Practical social media research workflows powered by ScrapeCreators: find outlier posts, mine comments, analyze transcripts, research competitors, and tear down ad libraries.
The Chinese teachers pack (教师): lesson plans in the 教案 format aligned to the subject's core competencies (marked to confirm against the current curriculum standard), 主题班会 plans, parent-meeting speeches and materials (家长会), warm and specific end-of-term comments (期末评语), and open-class preparation with the 说课稿, written in Chinese
Rigorous engineering process for AI coding agents: implement-spec, decompose-spec, orchestrate-build, build-memory, synthesize-spec, reconcile-build, self-review, review-pr, address-pr-comments, agent-docs, refresh-repo-docs
A skill for anyone needing legal documents translated into publication-ready English. Hand it a Word file in any language; get back an English .docx with formatting preserved, delivering notably higher quality than e.g. Legora’s DeepL tool. Unique features: * Skill has built-in English general lexicons and per-language sub-lexicons (phrasebooks) covering M&A, IP, IT/SaaS, finance, tax, litigation, employment and more. Full sub-lexicon coverage for 11 languages including Spanish, German, French and Chinese; others translate really well too. * Track changes read correctly when accepted or rejected, and typos are collapsed. * Definitions reordered alphabetically. * Headers, footers, footnotes and comments translated. * Translates to UK English by default, US English on request. * Quality gates throughout. * Takes minutes rather than seconds and is larger than simpler skills, but overall quality is higher. US English default version here: https://lawve.ai/en/skills/en-us-legal-translation-
A skill for anyone needing legal documents translated into publication-ready English. Hand it a Word file in any language; get back an English .docx with formatting preserved, delivering notably higher quality than e.g. Legora’s DeepL tool. Unique features: * Skill has built-in English general lexicons and per-language sub-lexicons (phrasebooks) covering M&A, IP, IT/SaaS, finance, tax, litigation, employment and more. Full sub-lexicon coverage for 11 languages including Spanish, German, French and Chinese; others translate really well too. * Track changes read correctly when accepted/rejected, and typos are collapsed. * Definitions reordered alphabetically. * Headers, footers, footnotes and comments translated. * Translates to US English by default, UK English on request. * Quality gates throughout. * Takes minutes rather than seconds and is larger than simpler skills, but overall quality is higher. UK English default version here: https://lawve.ai/en/skills/legal-translation-uk-englis
Turn a lawyer's bounded expertise, workflow, review standard, or legal judgment pattern into a safe, inspectable legal AI skill. Use when a user provides legal memos, comments, checklists, negotiation notes, review habits, compliance playbooks, or professional feedback and wants a reusable Lawve-style skill. Do not impersonate the lawyer or claim to reproduce a person; distill only authorized, source-bounded capability.
12 working methods for realtime collaboration: collaborative-editing-models, comments-and-annotations, conflict-resolution-ux, live-cursors-and-selection, and more
Reviews whether an AI-assisted Word or DOCX workflow preserves the original, tracked changes, comments, numbering, sections, edit identity, document structure, save/verify cycles, and professional review. Use before relying on a DOCX that an AI tool read, edited, redlined, reformatted, or generated. Do not use as a legal review of the document's substance or as proof that every rendering defect was detected.
Fetch Bilibili (哔哩哔哩) video comments for UP主 self-analysis. Collect, download, export, and analyze comments on a Bilibili video (BV号 / URL / UID), producing JSONL + summary.json suitable for further analysis (sentiment, keywords, audience trends). Read-only; does not post/edit/delete.
Edit Overleaf projects live alongside human collaborators. Bundles the overleaf-mcp-rt MCP server (self-hosted Overleaf and overleaf.com) with skills for setup, safe co-editing, LaTeX workflow and review comments. Use when asked to read, write, review or compile anything on Overleaf.
productivityWrite like a person in everything the agent emits: plain words, concrete claims, no AI tells. Always on for chat replies, status updates, close-outs, review comments, and commit messages; owns the repo's single merged tell catalog behind a pointer.
Let AI agents edit Word documents without breaking them: fill in forms, update tables, restyle, write reports, and redline with tracked changes and comments.
Code review process — reading order, findings, severity, scope and the approval decision. Load when reviewing a diff or PR, writing review comments, or deciding whether to approve or request changes.
Review a code change for correctness, security, maintainability, and test adequacy, and reach a defensible verdict. Use when reviewing a pull request, diff, patch, or AI-generated change; when scoping an oversized review into reviewable units; when writing or triaging review comments; when assigning reviewers or setting review policy; or when investigating why a defect escaped review. Covers change sizing and attention budgets, risk-tiered checklists, severity-labeled comments, verdict and residual-risk records, review metrics that resist gaming, and the measured limits of checklists and AI reviewers. Do not use for implementing the requested change, root-cause debugging of a failing test or incident (use systematic-debugging), security assessment of a whole system (use security-audit-methodology), designing new code securely (use secure-software-engineering), architecture review across systems (use software-architecture), or reverse-engineering an unfamiliar codebase (use software-arc
Interact with Atlassian Jira from the terminal: search issues with JQL, view details, create issues, add comments, count matches with fast approximate-count, list projects, discover valid transitions, and change status. Includes a full JQL language reference (functions, operators, history predicates, date expressions, saved filters, performance tuning) plus REST auth/pagination guidance. Use when the user mentions Jira, a ticket key (e.g. PROJ-123), asks about issues, bugs, tasks, projects, or sprint work, or needs to write, debug, or optimize JQL queries. Do not use for GitHub or GitLab issue tracking, Jira site administration, or generic ticketing systems.
Manage Linear teams, projects, cycles, issues, comments, workflow state, and documents from a terminal through Linear's public GraphQL API. Use when a user asks to list, search, inspect, create, update, move, or comment on Linear work, or to find Linear documents. Do not use to embed a live agent inside Linear or to build an MCP integration.
Learn how a GitHub user reviews code from up to a year of their real review comments, then review a change as that person — in chat only, never posted to GitHub.
