claude

Learning

Learning resources, skill development, educational content creation and knowledge management.

31 on GitHubby Daniel Rosehillv1.0.0MITupdated yesterdayResearch & Learning
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Plugin learning from marketplace danielrosehill in danielrosehill/Claude-Code-Plugins. Commands read from the repo's manifest — today.

What it installs

No skills, commands, agents, hooks or MCP servers were found in the standard plugin folders.

View source on GitHub blog.danielrosehill.com

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Career

claude

Career planning — log roles, compare offers, track applications, salary benchmark, with planning/job-search/salary variants.

55 skills · 27 commands · 2 agents

31

Product research and procurement skills with a particular focus on technical specification conformance — does this part actually meet the requirement, and can you show your working. Marketplace-agnostic: spec definition from a vague want or a photograph, annotated search vocabulary, versioned spec-as-code, manufacturer datasheet retrieval, cross-region part-number equivalence, conformance evaluation matrices, bills of materials and RFQ packages. Plus the surrounding process: intake, research, compare, evaluate, recommend, market-check, spec-driven market landscape surveys, live price/delivery scanning through your own VPN egress, preference memory and hardware-rig profiling. Hands off to the marketplace-specific plugins (shopping, israel-shopping, amazon, aliexpress-israel-skills) once a question becomes about a particular shop.

14 skills · 34 commands · 7 agents

31

Smart home — Home Assistant, Snapcast multi-room audio, Plex media server ops, with HA/audio/media-server variants.

3 skills · 47 commands · 5 agents

31

Audio production — normalize, VAD, transcribe, diarize, podcast assembly, with engineering/podcast/transcript variants.

17 skills · 34 commands · 1 agent

31

Image production — editing, format conversion, batch ops, and filesystem organisation by resolution, aspect ratio, orientation, format, EXIF time, camera, plus dedupe and metadata scrubbing.

29 skills · 19 commands

31

PR and media monitoring — scan coverage, summarize press, draft responses, comms strategy, with monitoring/response/strategy variants.

1 skill · 28 commands · 17 agents

31

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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.

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Runs a six-phase research workflow from source bundle to publish-ready output. Use when researching an unfamiliar domain or compiling materials into one reference. Not for quick lookups or single-file reads.

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Explicit 8-step empirical-analysis pipeline in the traditional Python econometrics stack (pandas + statsmodels + linearmodels + pyfixest + rdrobust + econml + causalml): cleaning -> variable construction -> Table 1 -> diagnostics -> estimation (OLS/IV/DID/RDD/PSM/SCM/DML/Causal Forest) -> robustness -> mechanism/heterogeneity/mediation -> publication-ready tables & figures. Includes epidemiology and ML-causal modes.

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View, search, download, and read academic papers from arXiv. Supports API queries, web scraping via Actionbook, and HTML paper reading via ar5iv.

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Eighteen-skill bundle covering the 《经济研究》 manuscript lifecycle: workflow router, topic selection, introduction, literature review, theory & hypotheses, data & sample, modern identification (DID/IV/RDD/DML), mechanism (post-江艇 2022), heterogeneity, robustness, tables / figures, policy implications, abstract, house style, reviewer-lens self-audit, reproducibility, submission preflight, and R&R rebuttal — plus a runnable Stata + Python code library.

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claude & codex plugins185 plugins