coolguyandClaude Opus 4.7
b0b385591b
scripts/librarian_survey.py walks docs/archive/*.md and dispatches every entry without a research counterpart through the same prompt assembly as librarian_promote.py. Outputs land under runs/tmp/librarian-survey-<timestamp>/, classified into: <stem>.md draft, ready to copy into docs/research/ <stem>.SKIP.txt LLM's one-line "not promotable" reason <stem>.ERROR.txt CLI stderr if the call itself failed Counterpart detection uses exact stem match plus a tail-match heuristic so research notes that intentionally drop a domain prefix still suppress their archive. Verified against the current tree: docs/archive/deep-cfr-opponent-policy-network-divergence-* is correctly recognized as already covered by docs/research/opponent-policy-network-divergence.md. --dry-run lists candidates and suggested research targets without calling the LLM. --max N caps processed archives per run, useful as a cost guard. Sequential dispatch; one LLM call per archive. Plan updated to mark survey mode complete. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
coolrl-lost-cities
Focused Lost Cities extraction from the legacy coolrl repository.
The current implementation starts with the classic two-player card game:
- classic 5-expedition rules by default
- Python/Cython game engine
- env wrapper
- random, passive-discard, and safe-heuristic bots
- core rule, scoring, mask, env, canonical-state, bot, and GUI smoke tests
Training code, Deep CFR, learned-policy evaluation, GUI, and web client are intentionally outside the first port.
Development
uv run pytest tests/games/classic
uv run lost-cities-classic
For future GUI work, install the optional GUI dependencies:
uv sync --extra gui
Run the classic pygame GUI:
uv run lost-cities-classic-gui --mode pvc --bot safe-heuristic
The GUI uses the in-process Cython game engine.
Basic Usage
from coolrl_lost_cities.games.classic import GameState, build_bot, classic_config
state = GameState.new_game(classic_config(seed=1))
bot = build_bot("random", seed=1)
while not state.terminal:
state.apply_action(bot.act(state))
print(state.total_score(0), state.total_score(1))
See classic port notes for the current direction.