Automate D&D playtest sessions across PREP, PLAY, and LOG stages.
npx skills add https://github.com/giodl73-repo/QUEST --skill session-runner
Please help me install this Agent Skill. Skill: session-runner Source: https://github.com/giodl73-repo/QUEST/tree/main/.claude/skills/session-runner Command: npx skills add https://github.com/giodl73-repo/QUEST --skill session-runner
Running a full D&D playtest with fidelity, repeatability, and voice-consistency is labor-intensive. This skill automates the end-to-end session lifecycle by acting as a deterministic DM and all PCs, orchestrating PREP → PLAY → LOG and producing canonical session artifacts for auditing and iteration.
Create an adventure module (module.md) and a party directory (personas/parties/...), then run the session-runner to generate the first session log at sessions/S1-log.md.
High-intent search queries and answers about installing and using this skill.
You can automate a D&D playtest by orchestrating the PREP, PLAY, and LOG stages using a compiled module.md and file-based party sheets. The session-runner acts as a deterministic DM and all PCs, generating canonical session logs for reproducibility.
To run an automated D&D playtest, you need a compiled adventure module at module.md, party sheets stored in a personas/parties directory, a sessions directory for outputs, and an executable dice engine for deterministic mechanical resolution.
Per-PC agent-spawn activates at meaningful decision points during AI dungeon master play, preserving character voice and dramatic tension. This Option C mode contrasts with Option A solo-DM mode, where the system deterministically controls all player characters.
Yes, the system generates canonical artifacts like sessions/S{N}-prep.md and sessions/S{N}-log.md, logging detailed state changes, warnings, and manifest events to ensure reproducible logs for cross-session playtesting analysis.
The best way to maintain party memory across multiple D&D playtest sessions is by updating the party shared log during the LOG stage. This ensures ongoing memory and state continuity for subsequent playtest iterations.
A deterministic dice engine ensures reproducible mechanical outcomes for audit and gate analysis, but it removes the randomness of physical dice. You should not use this approach if your playtest requires true random number generation for statistical variance.
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