setup-pstack
Check pstack installation and configure role effort and panel size for Codex and Claude Code.
Skill by MachineLearning-Nerd
Chalk Labs
Need help deploying setup-pstack?
Chalk Labs sets up agents like this inside real teams: connected to your tools, tested on your data, with a human in the loop where it matters.
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/machinelearning-nerd-setup-pstack
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
Get the weekly diff
The weekly diff: one email a week with new agent tools, notable updates and grade changes, plus security alerts for tools you follow. Double opt-in, one-click unsubscribe.
About
Use cases: agent-building
Reviews
Write a reviewNo reviews yet. Installed it? Reviews come from verified installers only. How reviews work
Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/setup-pstack
What's inside
- Skill name
setup-pstack- Description
- Check pstack installation and configure role effort and panel size for Codex and Claude Code. Models always inherit the current chat. Use for setup-pstack or pstack configuration requests.
- License
- MIT
- Instructions
- 42 lines in SKILL.md · view on GitHub
Files (1)
- SKILL.md
SKILL.md (first lines)
# Setup pstack Read ../pstack-harness/SKILL.md. Resolve the installed skills root and the explicit project destination. Run its bundled `pstack.py doctor --project <root>` to check payloads, native agent files, config, and CLI versions. File readiness and live agent loading are distinct: inspect the session's agent catalog before claiming activation. Start a fresh session when agent definitions were newly installed. Every role inherits the current chat's model. The user selects the model in the chat; setup does not assign separate models to workers or reviewers. Two-arm panels still run two independent agents, labeled independent same-model review. Do not change host settings, credentials, global configuration, or permissions. When the user wants effort or panel-size settings, create `.pstack-dual/models.json` in the project with `pstack.py config-init --project <root>` if absent. Use user scope only when requested. Show the resolved role choices before applying preference changes; existing authorization can supply those choices. The schema and example are in ../pstack-harness/references/models.md. Use `inherit-parent` for all model fields. Existing valid configuration files remain compatible: explicit legacy model choices are ignored and reported through `ignored_model` in role resolution. Do not rewrite user configuration merely to remove those choices. Effort and panel size remain configurable independently. Validate with `pstack.py validate-config --file <path>` and resolve representative code, judgment, and panel roles with `pstack.py resolve`; every arm must have `model: null`. Unsupported per-agent effort inherits and is reported. Configuration updates apply to the next resolved delegation, not automatically to agents already running. If the project has no behavioral verification path, offer once to create a [verification skill](../create-verification-skill/SKILL.md). If it has no formal verification skill, separately offer once to create a [formal verification skill](../create-formal-verification-skill/SKILL.md). Generate either only when accepted; declining does not block setup. For an existing formal skill whose covered code changed after its sync commit, point to [formal verification maintenance](../maintain-formal-verification-skill/SKILL.md). These offers do not authorize installing verification tools or changing product code. Report the actual checks, unresolved capabilities, and how to invoke `$poteto-mode` in Codex or `/poteto-mode` in Claude.
From MachineLearning-Nerd/pstack-dual, MIT.
More from MachineLearning-Nerd/pstack-dual
- 0.567score
pstack-harness
MachineLearning-Nerd
Adapt pstack delegation, role configuration, skill paths, verification, and session handling to Claude Code or Codex.
SkillAFreshness: AClaude Code +9 - 0.561score
interrogate
MachineLearning-Nerd
Challenge a code change with independent adversarial reviewers and consolidate evidence-backed findings.
SkillAFreshness: AClaude Code +9 - 0.561score
poteto-help
MachineLearning-Nerd
Explain how to install, configure, or choose a pstack workflow in Claude Code and Codex.
SkillAFreshness: AClaude Code +9 - 0.561score
how
MachineLearning-Nerd
Explain a subsystem's behavior from source, callers, data structures, and runtime evidence.
SkillAFreshness: AClaude Code +9 - 0.556score
pstack-review
MachineLearning-Nerd
Review a branch, working diff, or named files for reachable defects and missing behavioral coverage.
SkillAFreshness: AClaude Code +9 - 0.556score
create-skill
MachineLearning-Nerd
Author or update a portable pstack skill with precise triggers, self-contained resources, and realistic behavioral verification.
SkillAFreshness: AClaude Code +9 - 0.541score
architect
MachineLearning-Nerd
Sketch types, signatures, and module structure before code, then stay in the loop while implementation fills in.
SkillAFreshness: AClaude Code +9 - 0.539score
benchmark-checklist
MachineLearning-Nerd
Vet a perf measurement (limiter, tuning, limits, errors, repeatability, relevance, and whether the work happened) before you report or act on it.
SkillAFreshness: AClaude Code +9 - 0.539score
correct
MachineLearning-Nerd
Find the mistakes agents keep repeating in this repo and make each one impossible.
SkillAFreshness: AClaude Code +9 - 0.536score
automate-me
MachineLearning-Nerd
Use for "automate me", "create/update/refresh my -mode skill", "turn/capture my preferences or working style into a skill", or wanting agents to follow how…
SkillAFreshness: AClaude Code +9 - 0.536score
arena
MachineLearning-Nerd
Spawn N parallel candidates at the same task, pick a base, graft the strongest parts of the losers into it.
SkillAFreshness: AClaude Code +9 - 0.536score
blast-radius
MachineLearning-Nerd
Find what a change could break somewhere else before it ships, beyond the diff, and prove the one fact it's safe because of by running real code instead of…
SkillAFreshness: AClaude Code +9
Signals
- Stars
- 0
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Oct 21, 2026 (10 days ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
More from MachineLearning-Nerd
- 0.567score
pstack-harness
MachineLearning-Nerd
Adapt pstack delegation, role configuration, skill paths, verification, and session handling to Claude Code or Codex.
SkillAFreshness: AClaude Code +9 - 0.561score
how
MachineLearning-Nerd
Explain a subsystem's behavior from source, callers, data structures, and runtime evidence.
SkillAFreshness: AClaude Code +9 - 0.561score
poteto-help
MachineLearning-Nerd
Explain how to install, configure, or choose a pstack workflow in Claude Code and Codex.
SkillAFreshness: AClaude Code +9 - 0.561score
interrogate
MachineLearning-Nerd
Challenge a code change with independent adversarial reviewers and consolidate evidence-backed findings.
SkillAFreshness: AClaude Code +9 - 0.556score
create-skill
MachineLearning-Nerd
Author or update a portable pstack skill with precise triggers, self-contained resources, and realistic behavioral verification.
SkillAFreshness: AClaude Code +9 - 0.556score
pstack-review
MachineLearning-Nerd
Review a branch, working diff, or named files for reachable defects and missing behavioral coverage.
SkillAFreshness: AClaude Code +9
Done for you
Rather not set it up yourself?
Chalk Labs scopes, builds and runs agents for your team: the task audit, the eval harness, the integrations and the weekly ops report. Fixed scope, priced before we start.
See how we build AI agents →Read the case studies
Chalk Labs is a two-founder AI and growth studio. We build AI agents, CRMs and MVPs for Web3 and AI companies, and have shipped 50+ product and token launches.
- launches shipped
- 50+
- launches shipped
- media placements
- 500+
- media placements
- founders on every account
- 2
- founders on every account
Press we've placed: Forbes · CoinDesk · Decrypt · Cointelegraph · The Block
Rahil Jain, Co-founder, Chalk Labs. Ex-Goldman Sachs engineer. You talk to the person who builds it.
Get setup-pstack running in your business
Tell us what you want to automate. A founder replies within one working day with a plan, not a pitch deck.
Cite this page
setup-pstack skill. Agent Mart. https://agentmart.dev/skills/machinelearning-nerd-setup-pstack (last updated 2026-10-24)