القائمة
How to run Python scripts and install packages in the deep-cuts project
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Experimental protocol for Deep Cuts research, prototypes, model evaluations, threshold tuning, ablations, metric comparisons, and claims about accuracy or quality. Use before running or interpreting experiments so bots preserve train/validation/test boundaries, avoid leakage, compare against baselines, and report results honestly.
Guidelines for creating, updating, reorganizing, and reviewing Deep Cuts documentation in the project wiki, including page taxonomy, lifecycle status, protected pages, proposal handling, and link verification.
Pattern for multi-agent collaboration sessions in the deep-cuts fam — forge-first coordination over the botfam substrate (Gitea issues/PRs as the coordination plane, the unified `botfam wait` wake loop, bare-actor worktrees), with IRC opt-in for design sprints, plus session-log conventions
Draft, structure, and finalize a Deep Cuts blog-series post so it conforms to the post template, house voice, and cross-post link rules. Use when writing, drafting, assembling, or editing a "Deep Cuts" blog post (the rlupi.com series), preparing a post for publishing, or adding front matter to one. Covers the template, front matter schema, voice, agent crediting, and the link linter.
Checklist and guide for adding a new analysis pass to the trait-based modular pipeline
Safe pattern for adding SQLite schema migrations in the deep-cuts Rust/rusqlite_migration stack
| name | using-python |
| description | How to run Python scripts and install packages in the deep-cuts project |
Always use the project virtualenv — never the system Python or python3 directly.
tools/.venv/bin/python your_script.py
tools/.venv/bin/python tools/my_script.py
tools/.venv/bin/pip install some-package
tools/.venv/bin/pip list
umap-learnnumpymatplotlibscikit-learnmir_eval — MIR evaluation metrics (SALAMI-standard boundary/segment scoring)