50 plugins from public Claude Code marketplaces. Each one bundles skills, slash commands, agents, hooks or MCP servers that install together.
Showing 50 plugins that match these filters.
claude-code-plugins-plusjeremylongshore/tons-of-skills-marketplace1
pm-skillsphuryn/pm-skills1Submit the repo with .claude-plugin/marketplace.json and we list every plugin.
Submit marketplaceModern Python development with Python 3.12+, Django, FastAPI, async patterns, and production best practices
9 project management skills with 15 Python tools: pm-skills fork-orchestrator with agentic delivery loop (deterministic 8-lane router, Jira MCP snapshot bridge to Kanban flow metrics + Monte Carlo forecasts, delegation-governance gate, /cs-pm + /cs-grill-pm + /cs-pm-loop), senior PM, scrum master, Jira expert, Confluence expert, Atlassian admin, template creator, meeting analyzer, team communications. Bundled Atlassian Remote MCP.
Internal BizOps domain. v2.8.0 ships 7 skills: orchestrator + process-mapper (BPMN/bottleneck/cycle-time, Lean+TOC) + vendor-management (scorecard+SLA+3rd-party risk, NIST SP 800-161/ISO 27036) + capacity-planner (Erlang-C queueing math for ops teams, NOT engineering) + internal-comms (ADKAR+Kotter 8-step, NOT marketing) + knowledge-ops (SOP+runbook+KB hygiene with 5W2H, context: fork) + procurement-optimizer (UNSPSC spend categorization + supplier consolidation). Orchestrator uses context: fork to route inquiries via Matt Pocock grill discipline (one question per turn, recommended answer, canon-cited challenge). Every SKILL.md ships a Forcing-question library section. 18 stdlib Python tools, 24+ reference docs. Distinct from business-growth (external sales) and c-level-advisor (strategic).
Enterprise / cross-functional Research Operations domain — the managed counterpart to the academic research/ domain. v2.9.0 ships 5 skills: orchestrator (context: fork), clinical-research (protocol synopsis, endpoint selection, sample-size/power for means/proportions/survival, phase-gate feasibility), research-finance (R&D budgeting with F&A split, burn/runway, capitalize-vs-expense routing, portfolio ROI), market-research (TAM/SAM/SOM top-down and bottoms-up, survey sampling with FPC, Kotler segmentation), and product-research (goal-matched study design, method-based saturation, insight synthesis flagging single-source anecdotes). Hard rules: outputs are estimates routed to a named clinical or finance owner, market sizes show method + assumptions, product insights require recurrence across independent participants. 24 stdlib Python tools, 12 reference docs. Distinct from ra-qm-team, finance, research/grants, product-team, and marketing-skill.
Build, launch, grade, and schedule Claude Managed Agents (CMA) in your own Anthropic account — a plugin re-implementation of Anthropic's launch-your-agent reference skill (Apache-2.0; independent, not a fork). Every session starts with a goal (./my-agent/goal.json, opt-in SessionStart hook, driven by /cs-goal); loop_compiler.py compiles it into a bounded grade->iterate loop (max_iterations 1..20), a recurring cron scheduled-deployment loop, or a single-pass interview->stage->launch workflow. 6 skills: orchestrator (context: fork goal router) + interview + stage-launch + grade-iterate + run-without-you + wrap-up. 18 stdlib-only deterministic scaffolders (no network/API calls; live launches emitted as BYOK curl that never prints the key), 4 agents, 8 commands, hooks, 5 references, 4 assets. Validators enforce CMA limits. Distinct from engineering/agent-harness (generic domain loop) and write-a-skill (authors Claude Code skills, not CMAs).
Systems programming with Rust, Go, C, and C++ for performance-critical and low-level development
25-skill pentest pack with engagement governance, network/code/dependency scans, OWASP Top 10 mapping, and exec-readable reporting. Heavy-hitter compliant; chain-of-custody attestable.
Cedar policy enforcement + Ed25519 signed receipts for every Claude Code tool call. First cryptographic governance plugin — decisions are policy-gated before they run and every decision produces a tamper-evident receipt verifiable offline.
Go-to-market skills for PMs: GTM strategy, growth loops, GTM motions, beachhead segments, and ideal customer profiles.
Require 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.
Study companion for the Deep Learning textbook by Goodfellow, Bengio & Courville (MIT Press, 2016), free to read at deeplearningbook.org. Twenty chapter files, a glossary, patterns and a cheatsheet index the whole book, and a delta reference dates it against 2026 practice with per-claim confidence levels: double descent qualifying the U-curve, AdamW splitting weight decay from L2, transformers displacing Chapter 10's recurrence, diffusion growing out of Chapter 18's score matching. Four stdlib tools make it executable — a prerequisite-closed reading-path planner that refuses goals the book does not cover, a training diagnostic running Chapter 11's rules in priority order so a NaN is never reported as overfitting, a capacity planner that ranks 'shrink the model' last when overparameterized, and a parameter/FLOP/activation-memory calculator that refuses a stack whose shapes do not connect. A companion, not a compilation: the book is copyrighted, so nothing here reproduces its text.
Get a defense supplier ready for a CMMC 2.0 Level 2 assessment. Three deterministic stdlib scripts: an SPRS scorer that weights all 110 NIST SP 800-171 Rev 2 requirements per 32 CFR 170.24 and blocks gaps that can never go on a POA&M; a POA&M closeout tracker that checks every deferred gap against the 180-day Conditional status deadline and the annual affirmation calendar; and a scope classifier that sorts the asset inventory into the 170.19 categories and blocks on assets missing from the SSP or cloud providers holding CUI without FedRAMP. Ships cs-cmmc-assessor agent, /cs-cmmc-readiness and /cs-sprs-score commands, 3 cited references and a readiness sign-off.
Ship a new language without the usual breakage. Three deterministic stdlib scripts: a translation-file checker for JSON/ARB (i18next, ICU), .properties, .strings, Android strings.xml and gettext .po that blocks on missing keys, renamed placeholders, ICU syntax errors, plural/select without other, zero branches that never match and plural categories the target locale needs, from the embedded Unicode CLDR 49 table (ru few/many, ar six forms); a hardcoded-string scanner for React, Vue, Svelte and HTML that flags text and user-facing attributes outside t(), concatenated translations, locale-less toLocaleString and '$' + amount; and a pseudo-locale generator (en-XA accented and expanded, ar-XB right-to-left) that keeps placeholders and ICU syntax intact. Ships cs-i18n-engineer agent, /cs-i18n-check and /cs-pseudo-locale commands and 3 cited references.
Company Architect: builds a business from scratch as an OKF (Open Knowledge Format) bundle — a tree of versionable .md files with a frontmatter type, links forming a graph, and reserved index.md/log.md, readable by humans and agents alike. Guides the founder through a 12-phase interview (foundation, strategy, market, financial, sales, marketing, product, operations, tech, people, legal, governance), one phase at a time, and generates conformant markdown concepts. 3 stdlib tools: scaffold_bundle (bundle scaffold), okf_linter (validates type/reserved files/links), index_generator (regenerates the index.md files). Standalone-installable; also bundled in c-level-skills. In English.
Active coding discipline enforcer based on Karpathy's 4 principles: surface assumptions, simplify, make surgical changes, define verifiable goals. Ships 4 Python tools (complexity_checker, diff_surgeon, assumption_linter, goal_verifier), a review agent, /karpathy-check command, and pre-commit hook. All stdlib-only.
Turn any domain folder of skills into a bounded agentic loop: a manifest builder inventories a domain's skills/tools/checks, a goal compiler turns a goal into a verifiable task plan (refusing vague goals with forcing questions), and a JSON-backed loop controller drives execute->verify->close with retry caps, controller-run verification (no verification theater), human escalation on exhausted budgets, and a close gate that refuses while any task is unverified or unwaived. Ships 3 stdlib Python tools, 18 committed per-domain harness manifests + JSON schema, 3 references citing the 2024-2026 agent-harness canon (Anthropic long-running harnesses, verifier's law, SWE-agent, Ralph loop, Cognition), harness-runner agent + /cs-harness command. Use when an agent or subagent should pick up a goal, define its tasks, complete and verify them, and close the loop.
Conversation-handoff document generator. Compacts the current session into a markdown handoff for a fresh agent — references existing artifacts (PRDs, plans, ADRs, issues, commits) by path/URL instead of duplicating them. Derived from Matt Pocock's MIT-licensed handoff with: (1) 3 stdlib Python tools (template generator tailored to 5 next-session emphases, artifact deduplicator across 5 categories of duplication, skill recommender matching content to 14 skills in this repo), (2) 4 references citing 7-8 sources (handoff structure, deduplication discipline, next-session skill matching, companion tooling), (3) cs-handoff-author persona agent + /cs-handoff slash command. Matt's no-duplication discipline + mktemp convention preserved verbatim per MIT.
Pressure-test a business idea before you build it. Convenes a 5-angle adversarial panel — The Critic (what kills this?), The Champion (the 10x upside?), The Analyst (does the logic hold?), The Investigator (what does the market say?), The Customer (would I actually pay?) — fired in parallel as independent reviewers, then a Judge synthesizes one GO / RESHAPE / KILL verdict with explicit confidence and the cheapest 48-hour test to de-risk it. Never averages the scores: a weighted synthesizer with demand/fatal-flaw/logic veto gates produces the call, backed by deterministic stdlib tools.
Google NotebookLM browser-automation skill. 4 actions (read/extract, add-source, Studio outputs, create notebook). Screenshot-first + find-before-click + fire-and-notify async discipline. Path-B from megaprompt 03.
Knowledge base compiled from OpenAI's Spinning Up in Deep RL (MIT, Joshua Achiam) by engineering/book-to-skill. A resident core carries the RL optimization problem, the model-free taxonomy, the policy-gradient template with its five valid weights, the safe-step family (VPG to TRPO to PPO) and the overestimation family (DDPG to TD3 and SAC); 20 on-demand chapters cover key concepts and MDPs, the algorithm taxonomy and model bias, the policy gradient derivation with the log-derivative trick and EGLP lemma, Achiam's researcher essay, the key-papers topic map, the exercises including the silent DDPG broadcasting bug, the benchmark parity disclosure, one chapter per algorithm, and the logger/MPI/ExperimentGrid utilities. Ships a glossary, a patterns file with 16 techniques, a decision cheatsheet with thresholds, a cs-spinning-up-deep-rl agent and /cs-spinning-up-deep-rl. Structured study notes, not a reproduction of the source.
