exps-logistic
Documentation for the logistic regression MI estimation experiment (exps_logistic)
npx skills add majiayu000/claude-skill-registry --skill exps-logistic --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# exps_logistic - Logistic Regression MI Estimation ## Purpose Measure mutual information (MI) lower bound between CoT rationales and problem parameters using multinomial logistic regression on embeddings. Compares code vs NL representations. ## Key Files | File | Purpose | |------|---------| | `src/exps_logistic/main.py` | Main experiment runner | | `src/exps_logistic/config.py` | Configuration, CLI args, kind presets | | `src/exps_logistic/data_utils.py` | Data loading, gamma label creation | | `src/exps_logistic/classifier.py` | Logistic regression classifier | | `src/exps_logistic/featurizer.py` | Embedding extraction (BERT, TF-IDF) | | `src/exps_logistic/run.sh` | Dev script for single model | | `src/exps_logistic/prod_logistic.sh` | Production script for all models | | `src/exps_logistic/notebooks/generate_plots.py` | Plot generation | ## Kind Presets (in config.py) ```python FG_KINDS = {"add", "sub", "mul", "lcs", "knap", "rod", "ilp_assign", "ilp_prod", "ilp_partition"} CLRS_KINDS = { "activity_selector", "articulation_points", "bellman_ford", "bfs", "binary_search", "bridges", "bubble_sort", "dag_shortest_paths", "dfs", "dijkstra", "find_maximum_subarray_kadane", "floyd_wa
- Purpose
- Key Files
- Kind Presets (in config.py)
- Gamma Labels
- CLI Arguments
- Generated Plots (in src/expslogistic/notebooks/)
- Key Results (Extended Kinds - Jan 9, 2026)
- Running Commands
Dev run (single model) bash src/exps_logistic/run.sh Production run (all models, all seeds) bash src/exps_logistic/prod_logistic.sh Generate plots uv run --no-sync python src/exps_logistic/notebooks/generate_plots.py
What does the exps-logistic skill do?
Documentation for the logistic regression MI estimation experiment (exps_logistic)
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill exps-logistic --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.