Qiushi-Skill is a collection of agent skills that turn dialectical materialist and practical philosophy into methods for investigating problems, identifying their central contradiction, testing conclusions, and continuing work. It is intended to guide AI agents through analysis and task execution. The catalogue entries are its skills, commands, hook, plugin, and agent components.
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.
git clone --depth 1 https://github.com/HughYau/qiushi-skillWrote 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/commands/hughyau/qiushi-skill/practice-cognition)<a href="https://agentmods.dev/commands/hughyau/qiushi-skill/practice-cognition"><img src="https://agentmods.dev/badge/commands/hughyau/qiushi-skill/practice-cognition/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/commands/hughyau/qiushi-skill/practice-cognition"><img src="https://agentmods.dev/badge/commands/hughyau/qiushi-skill/practice-cognition.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 | $0.00022 | $0.00117 |
| Opus 5 | $0.00011 | $0.00059 |
| Sonnet 5 | $0.00004 | $0.00023 |
| Haiku 4.5 | $0.00002 | $0.00012 |
Grade A, and why
practice-cognition 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 9d 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.
What it actually says
读取并遵循 skills/practice-cognition/SKILL.md。
待验证的方案或问题： $ARGUMENTS
输出要求：
- 先声明当前所处阶段。
- 写出假说、验证方式和完成信号。
- 区分已确认事实、待验证假说和下一轮动作。
- 如果不适合进入实践循环，明确说明原因。
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.
- 9d ago First seen · 17 lines · 22 tokens per session scan A 0b775cc5ad1e
practice-cognition is a command published in the GitHub repository HughYau/qiushi-skill (3,769 stars, last pushed 3d ago), licensed MIT. It adds 22 tokens to every session and 117 once invoked, about $0.0001 per session on Opus 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 2026-08-30.
Other commands, from other repositories
OPSX: Explore
Enter explore mode - think through ideas, investigate problems, clarify requirements.
OPSX: Propose
Propose a new change - create it and generate all artifacts in one step.
OPSX: Propose
Propose a new change - create it and generate all artifacts in one step.
OPSX: Archive
Archive a completed change in the experimental workflow.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.