pstack-claude is a set of opinionated coding-agent skills and workflows ported from Cursor to Claude Code, Codex, Pi, and other agent harnesses. It guides agents toward concise, simple, and verified code by routing goals to suitable workflows and supporting subagent tools. The catalogue entries are the project's skills, agents, hook, and plugin implementations for these agent environments.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add michael-denyer/pstack-claude --skill thermosgit clone --depth 1 https://github.com/michael-denyer/pstack-claudeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/michael-denyer/pstack-claude/thermos)<a href="https://agentmods.dev/skills/michael-denyer/pstack-claude/thermos"><img src="https://agentmods.dev/badge/skills/michael-denyer/pstack-claude/thermos/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/michael-denyer/pstack-claude/thermos"><img src="https://agentmods.dev/badge/skills/michael-denyer/pstack-claude/thermos.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00039 | $0.00385 |
| Opus 5.5 | $0.00016 | $0.00154 |
| Sonnet 5.5 | $0.00008 | $0.00077 |
| Haiku 4.5 | $0.00004 | $0.00038 |
Grade A, and why
thermos scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- thermos — 92% identical, 11 lines differ
What it actually says
Thermos
On Codex, read the platform mapping, including its per-skill notes, before following this skill.
On GitHub Copilot, read the platform mapping, including its per-skill notes, before following this skill.
Run the two thermo review passes as async background subagents in parallel, then synthesize their results.
Workflow
- Determine the review scope from the user request, PR, current branch, or relevant changed files.
- Gather the diff and any file/context excerpts needed for reviewers to evaluate the change without guessing.
- Launch both subagents in the same message with
run_in_background: true:subagent_type: "pstack:thermo-nuclear-review-subagent"for bugs, breakages, security, devex regressions, feature-flag leaks, and other branch-audit risks.subagent_type: "pstack:thermo-nuclear-code-quality-review-subagent"for maintainability, structure, file-size growth, spaghetti, abstractions, and codebase-health risks.
- Pass each subagent the same scoped diff/file context and ask it to return prioritized findings with file references and evidence.
- After both finish, synthesize the results with findings first, deduplicated across reviewers. Weight overlapping findings more heavily, resolve disagreements with your own judgment, and keep summaries brief.
If individual background summaries are already visible to the user, do not restate them wholesale. Surface the unified verdict, the highest-signal findings, and any remaining uncertainty.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 25 lines · 39 tokens per session scan A 7936698b2925
thermos is a skill published in the GitHub repository michael-denyer/pstack-claude (2,366 stars, last pushed today), licensed MIT. It adds 39 tokens to every session and 385 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-13.
Other skills, from other repositories
code-review-excellence
Master effective code review practices to provide constructive feedback, catch bugs early, and foster knowledge sharing while maintaining team morale. Use when reviewing pull requests, establishing review standards, or mentoring developers.
multi-reviewer-patterns
Coordinate parallel code reviews across multiple quality dimensions with finding deduplication, severity calibration, and consolidated reporting. Use this skill when organizing multi-reviewer code reviews, calibrating finding severity, or consolidating review results.
ln-33-plan-reviewer
Reviews a concrete implementation plan for missing decisions, feasibility and risk before execution; read-only.
ln-52-delivery-reviewer
Reviews a completed change for acceptance, regressions and release risk; read-only, not a whole-codebase audit.
ln-41-surgical-change-implementer
Implements one scoped product feature or fix using existing contracts; not upgrades, performance tuning or establishing cross-cutting technical foundations.
ln-42-dependency-upgrader
Upgrades dependencies in reversible batches with version-specific compatibility and regression checks.