superpowers
claudecodexCore skills library for Claude Code: TDD, debugging, collaboration patterns, and proven techniques
15 skills · hooks
What ran? Whatran records what an AI agent actually ran during a scientific analysis as a checkable, result-centric Analysis Graph, then packages the final reported path so it reruns and checks on a fresh clone and shows itself on GitHub Pages.
Not in a marketplace yet, so it loads for one session with --plugin-dir.
Plugin whatran in qchiujunhao/whatran. Commands read from the repo's manifest — 12 days ago.
This plugin runs code on your machine — its hooks fire automatically on events like tool use. The exact commands are listed below; read them before installing.
package-analysisTurn a completed scientific or data analysis that reports findings into a repository that reruns the result path from declared inputs, checks the regenerated outputs against committed references on a fresh clone, and shows the result on a generated page. Use whenever an agent is finishing such an an
record-analysisRecord what actually ran during a scientific or data analysis as a checkable, result-centric Analysis Graph, while the analysis happens. Use whenever you run computations that produce scientific results (data processing, statistics, figures, models; any Python/R/shell/HPC/Galaxy analysis), whenever
python3 "${CLAUDE_PLUGIN_ROOT}/hooks/observe.py" --session-start
python3 "${CLAUDE_PLUGIN_ROOT}/hooks/observe.py"
python3 "${CLAUDE_PLUGIN_ROOT}/hooks/observe.py"
python3 "${CLAUDE_PLUGIN_ROOT}/hooks/observe.py"
python3 "${CLAUDE_PLUGIN_ROOT}/hooks/observe.py" --stop
Problems found in this repo's manifests
Core skills library for Claude Code: TDD, debugging, collaboration patterns, and proven techniques
15 skills · hooks
RVF format for portable agent memory, session persistence, and cross-platform transfer
2 skills · 1 command · 1 agent
Data engineering for Apache Airflow and Astronomer. Author DAGs with best practices, debug pipeline failures, trace data lineage, profile tables, migrate Airflow 2 to 3, and manage local and cloud deployments.
35 skills · hooks
HADS (Human-AI Document Standard) — semantic tagging convention for docs that work for both humans and AI models — plus the grounded-vault pattern for provenance-linked, drift-checked knowledge stores.
2 skills
Atlan data catalog plugin for Claude Code. Search, explore, govern, and manage your data assets through natural language. Powered by the Atlan MCP server with semantic search, lineage traversal, glossary management, data quality rules, and more.
1 skill · 1 MCP server
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.
1 skill · 3 commands · 1 agent
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