15 plugins from public Claude Code marketplaces. Each one bundles skills, slash commands, agents, hooks or MCP servers that install together.
Showing 15 plugins that match these filters.
slushpileVonTerraProject501c3/slushpile1
lawve-ailawve-ai/awesome-legal-skills1
humanizer-itilyautov/humanizer-it1
augustus24601/Augustus1Submit the repo with .claude-plugin/marketplace.json and we list every plugin.
Submit marketplaceScientific paper writing, source-traced review, and de-AI rewriting for top-tier journals (ApJ/MNRAS/PRD/JCAP-class) and NSF/NIH proposals. Ships 12 skills and 40 tools under one typed standard: scientific-integrity blockers that cannot be waived as style preferences, a zero-target L0 lexicon, ranked advisories, explicit measurement states so missing calibration never reads as clean, claim-first rewriting gated on hard scientific-fidelity eligibility, a mechanical length budget with a machine-built removal map and a required-shrink gate, corpus-calibrated salience-hierarchy, domain-register (with an exhaustive zero-hit vocabulary audit), collocation, and discourse-texture (given/new cohesion, epistemic hedging) axes, each judged against the field's own per-section distribution at the unit it was calibrated at, a residue axis that gates the trace an edit leaves, A-R review with narrative-spine and adversarial-escalation dimensions, registry-verified bibliographies (CrossRef, DataCite, a
writingTen skills and eight agents that run a job application and feed what the review finds back into your profile. The skills: onboard, search and score roles, find a referrer and draft the ask, interview for undocumented experience, build the resume and letter, adversarially review them, de-slop the prose, restyle the templates, report the queue and its calibration, and answer what to run next.
productivityGenerate a spreadsheet of test entries — newly designated names from OFAC, OFSI, and EU sanctions lists plus deliberate variations of those names — to validate that a sanctions screening system catches fresh designations and is tuned to the right fuzziness threshold. Use this whenever the user asks for sanctions list update test data, screening regression test data, screening QA, fuzzy match calibration, or wants to verify their screening lists are current. Trigger even if the user doesn't say 'screening' explicitly — phrases like 'test my sanctions list', 'check our SDN coverage', 'is my list up to date', or 'build me a regression set from the latest designations' should also invoke this skill.
Kills AI smell in Italian text. Hard bans, deterministic scanner, 3 modes (rewrite / audit / targeted fix), voice calibration, quad-pass audit, anglo-calque suppression, return of living Italian. | Elimina l'odore di IA dal testo italiano: ban assoluti, scanner deterministico, 3 modalità, calibrazione della voce, audit quad-pass, soppressione dei calchi dall'inglese.
writingUse when analyzing, selecting, validating, or communicating models for insurance, actuarial, financial-risk, or other consequential uncertain outcomes. Covers regression, generalized linear models, frequency-severity, panel and longitudinal data, survival, time series, credibility, reserving, tail risk, calibration, and model governance. Do not use for generic software forecasting, ordinary SaaS financial models, or credentialed actuarial, investment, legal, or regulatory advice without the relevant specialist review.
Clean, profile, validate, reshape, and document messy tabular, text, JSON, and relational data through an evidence-first, reproducible workflow. Use when preparing data for analysis, reporting, modeling, ingestion, migration, or matching, including AI-suggested repair, entity-match review, score calibration limits, and reversible repair ledgers. Do not use for statistical modeling, dashboard design, or operating a named data platform; route those tasks to data-scientist, data-engineering, or the relevant tool skill.
Design, integrate, evaluate, self-host, and troubleshoot typed System One decision models including TypeSafe Jev, Convai Innovations Laya, CLM, and experimental Strands Decider. Use for Choice/Score/Noul judgments and experimental Jev image/Box queries inside deterministic software, app-control loops, routing, ranking, guardrails, calibration, semantic code linting and post-edit feedback, DevOps decision support, confidence-based escalation, or private open-model inference. Do not use for syntactic/style linting, exact policy or authorization, open-ended generation, or generic LLM serving without a bounded decision contract.
Operate and evaluate TabPFN for local tabular classification, regression and dataset-specific fine-tuning: checkpoint selection, weight licensing, environment setup, training-context leakage, calibration, resource budgeting and reproducible inference artifacts. Use for TabPFN package or checkpoint workflows. Do not use for generic model selection, operating TabICL, or language-model fine-tuning; use ml-engineering, the selected tool, or unsloth respectively.
Design and improve human annotation programs for machine-learning training, validation, and evaluation data, including task and label schemas, active acquisition, annotator guidance, calibration, disagreement, adjudication, provenance, and cost-quality stopping. Do not use for general statistical inference, data storage pipelines, model training, or annotation-interface implementation; route those concerns to data-scientist, data-engineering, ml-engineering, or product-design-and-ux.
Find, build, evaluate, and hill-climb decision-model systems. Apply composition, decision theory, calibration and outcome evaluation across software, business and everyday decisions. TypeSafe Jev is the default hosted exemplar. Independent of TypeSafe.
Review communication drafts or prepare difficult conversations. Provides email refinement, tone calibration, roleplay practice, and presentation feedback.
Prepare Simulink models for production code generation, including single-precision conversion, optimizing generated embedded code for runtime, memory, and MISRA compliance, configuring Embedded Coder deployment settings, and customizing A2L calibration files
developmentQuality-gate any text artifact through 5 role-conditioned LLM deliberators (Skeptic, Voice & Identity, Evidence & Calibration, Strategy & Stakes, Adjudicator). 2-round async + cross-read. Returns SHIP/REVISE/HOLD + revision brief. v0.1.1 adds 5 deliberator-as-Skills for interactive single-perspective review in any APM harness alongside the existing Claude Code slash commands + MCP server.
Remove documented AI-writing patterns from generated and existing text, with register awareness and voice calibration
Real-time Workshop dashboard with agent signals, honesty calibration, and cost-aware repo or connected desk launch profiles.
