pstack/skills/setup-pstack/SKILL.md
Configure which models pstack uses per role. Detects your available models and writes an always-applied rule that overrides the skill defaults. Use for /setup-pstack, "configure pstack models", or changing pstack's model choices.
npx skillsauth add cursor/plugins setup-pstackInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Write ~/.cursor/rules/pstack-models.mdc, an always-applied rule that sets pstack's model per role. The skills read it and fall back to their inline defaults when a line is absent, so this is an override layer, not a requirement.
Enumerate the model slugs you can pass to a Task subagent in this session; that is the dependable source. If Cursor also exposes a models API or CLI that lists the user's entitled models, prefer it for completeness. If you cannot detect any, ask the user to paste the slugs they have access to. Never write a slug you have not confirmed is available.
The default role-to-model mapping is the rule shape shown in step 5 below. If ~/.cursor/rules/pstack-models.mdc already exists, read it and treat its values as the current choices. Otherwise start from those defaults.
Show every role with its current model, marking any whose model is not in the detected set as needing a choice. Ask whether to accept as-is or change specific roles, offering the detected models as the options. Prefer AskQuestion over free text. For panel roles (how critics, arena runners, architect runners, interrogate reviewers) the value is a list, and one subagent runs per model, so the list length sets the count.
Every slug written must be in the detected set. If a chosen slug is not available, stop and ask again. A rule pointing at a model the user cannot use breaks every delegation that reads it.
Write ~/.cursor/rules/pstack-models.mdc with alwaysApply: true and one line per role, using the same labels poteto-mode uses. Overwrite the whole file so re-runs stay idempotent. Shape:
---
description: pstack per-role model choices (overrides skill defaults)
alwaysApply: true
---
# pstack model configuration. One line per role. Delete a line to fall back to the skill default.
feature, refactoring: composer-2.5-fast
bug-fix, perf-issue: gpt-5.5-high-fast
judgment and prose: claude-opus-4-8-thinking-xhigh
how explorer: composer-2.5-fast
how explainer: claude-opus-4-8-thinking-xhigh
how critics: claude-opus-4-8-thinking-xhigh, gpt-5.5-high-fast, composer-2.5-fast
why investigators: composer-2.5-fast
why synthesizer: claude-opus-4-8-thinking-xhigh
reflect tooling: composer-2.5-fast
reflect judgment, divergent, synthesizer: claude-opus-4-8-thinking-xhigh
arena runners: claude-opus-4-8-thinking-xhigh, gpt-5.5-high-fast, composer-2.5-fast
architect runners: claude-opus-4-8-thinking-xhigh, gpt-5.5-high-fast, composer-2.5-fast
interrogate reviewers: claude-opus-4-8-thinking-xhigh, gpt-5.5-high-fast, composer-2.5-fast
Tell the user the rule was written and that it applies to new sessions. Re-running this skill updates it.
testing
Apply to multi-step work (sweeps, migrations, runs of similar edits) and to how you stack commits and PRs. Break work into small units that each end in a verifiable state, check each before the next, and order delivery so the sequence proves itself to a reviewer.
development
Design an auditable playbook when no narrower one fits: a large migration, an ambitious multi-part change, or work a human reviews after stepping away. Scales rigor to the task, runs a hypothesis loop, and logs decisions via show-me-your-work. Use for /figure-it-out, 'figure it out', a large migration, or when no narrower playbook applies.
tools
Use for 'why does X work this way', 'why we picked Y', design rationale, regressions, postmortems, or data-backed thresholds. Discovers available MCPs and queries each evidence category (source control, issue tracker, long-form docs, real-time chat, infrastructure observability, error tracking, product analytics warehouse) in parallel, then returns a cited read on decisions and tradeoffs. Use how for runtime behavior.
data-ai
Spawn three parallel review subagents over the active transcript, surface learnings, and route each to a concrete edit on an existing skill. Use when the user says reflect.