12 plugins from public Claude Code marketplaces. Each one bundles skills, slash commands, agents, hooks or MCP servers that install together.
Showing 12 plugins that match these filters.
tale-modealicicek/tale-mode1
agent-powerupsyeaight7/agent-powerups1Submit the repo with .claude-plugin/marketplace.json and we list every plugin.
Submit marketplaceStudy 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.
Converts books, documentation folders, and source collections (PDF, EPUB, DOCX, HTML, Markdown, RST, AsciiDoc, RTF, MOBI/AZW) into structured agent skills: a master SKILL.md with core frameworks and a topic index, on-demand chapter files, a glossary, a patterns file, and a decision cheatsheet. Ships 4 stdlib-only tools (multi-format extractor with invisible-Unicode sanitization, four-family generated-skill validator covering frontmatter/safety/budget/index, pre- and post-flight token budget estimator with a worth-converting verdict, and a plugin emitter that wraps a compiled skill as an installable claude-skills package behind a rights gate), 5 references citing 7-8 sources each, a cs-book-to-skill agent, and /cs-book-to-skill + /cs-book-to-plugin commands. Derived from virgiliojr94/book-to-skill (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.
Disciplined multi-source meta-research for high-stakes questions — the heavyweight alternative to the fast research router. 9-phase pipeline (reframe into falsifiable hypotheses, plan, capability discovery, parallel sub-agent fan-out, score & triangulate, synthesize + adversarial pass, verify, refresh targets). Triangulates every thesis against >=3 independent differently-typed sources; per-source files with verbatim quotes; never fabricates a citation. Auditable, reusable folder + delta-update refresh protocol. Contributed via PR #851.
Personal Claude power-user coach. Delivers a personalized, ranked cheat-code glossary on first activation, then surfaces at most one tip per turn when it would genuinely improve the next attempt. Ships cheat-codes glossary, coaching-rules decision tree, three stdlib Python tools, cs-claude-coach agent, and /cs-claude-coach command.
Docs-anchored grilling session — interrogates a plan against the project's existing language (CONTEXT.md) and recorded decisions (docs/adr/), updating those files inline as terminology and decisions crystallise. Derived from Matt Pocock's MIT-licensed grill-with-docs with stdlib validators (CONTEXT.md linter, ADR scanner, glossary-code consistency), reference docs, cs-grill-with-docs agent, and /cs-grill-with-docs command.
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.
Drive the Pi coding agent from Claude Code — model-agnostic delegation and code review. /pi:review with structured findings, /pi:adversarial-review, /pi:rescue, /pi:parallel-rescue, racing/fallback across providers, incremental review.
Adversarial multi-model code review and model dispatch. Orchestrates Claude, Codex CLI, and Gemini CLI for cross-examined code review with 5 specialized reviewers.
Apache Magpie — adversarial review: runs other models' CLIs (Codex, Copilot, Antigravity, Pi, Grok, Goose, Claude) read-only over a change before its PR is created, and merges their findings. Runs from the installed plugin, so no repository needs a copy.
Discipline mode (staged planning, receipts, verify-against-source, adversarial review) + plan/kickoff commands + a plan-reviewer agent + an always-on SessionStart discipline injector + a Stop-hook autonomous loop (Layer 1, a deterministic gate) that can arm itself from a committed, content-hash-trusted .claude/tale-mode.json during a kickoff phase + a fail-closed plan-mode auto-approve for provably read-only shell (no more permission-dialog walls during investigation).
productivityQuality gates for spec-driven development: adversarial plan verification and structured code review.
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