practice-cognition
实践认识论:把方案、假说或判断放回实践中检验,按「实践 → 认识 →...
What this skill does for you
实践认识论:把方案、假说或判断放回实践中检验,按「实践 → 认识 → 再实践」螺旋迭代,并在开始前写明循环终止条件。当你提出了假说要验证、一次尝试失败要复盘再来、发现自己只想不做或只做不想时触发;一次性输出、答案可直接查阅、还没形成假说(先调查)时不触发。 English: Practice-cognition loop. Test a hypothesis or plan in practice, learn from the result, and iterate in a rising spiral with an explicit stop condition. Trigger when a plan needs validation, an attempt failed and must be retried, or you notice thinking without doing (or doing without thinking); skip for one-shot outputs, lookup questions, or before any hypothesis exists.
Quoted from the skill description in the pinned sourceWho made it
Use it with Edge
Set up Edge for me: read getedge.cc/SKILL.md and follow it. Then use Edge to load the practice-cognition skill from hughyau/qiushi-skill.
Paste it into Claude, ChatGPT, Codex or Cursor.
Or read the skill source first.
Security evidence
| Scanner | Date | Result | Findings |
|---|---|---|---|
| Edge static checks | 2026-09-27 | Pass | 0 |
| Cisco skill-scanner | 2026-09-27 | Pass | 0 |
| Semgrep Edge rules | 2026-09-27 | Pass | 0 |
Static scans check the code, not how well the skill works.
Scanner scope, raw findings and mirrored provider records
Edge static checks (Edge-run): edge-static/1.0.1. Scope: Static patterns for remote execution, credentials with network sends, obfuscation, prompt overrides, hidden Unicode, binaries and persistence. Revision: 3d36c1471081d0cedce248836522c6e845f9b516. Raw findings: . Counted findings: .
Cisco skill-scanner (Edge-run): cisco-skill-scanner/2.1.0. Scope: Local static, YARA, pipeline and behavioral analyzers; no LLM or AI Defense analysis. Revision: 3d36c1471081d0cedce248836522c6e845f9b516. Raw findings: 1. Counted findings: .
Semgrep Edge rules (Edge-run): semgrep/1.178.0+edge-rules.c15016a36fd3+offline-v1. Scope: Six Edge-authored static rules with Semgrep CE; offline execution and metrics disabled. Semgrep-maintained rules excluded. Revision: 3d36c1471081d0cedce248836522c6e845f9b516. Raw findings: . Counted findings: .
Source and licence
- Repository
- https://github.com/hughyau/qiushi-skill
- Pinned skill file
- View source at revision
- Revision
- 3d36c1471081d0cedce248836522c6e845f9b516
- Package hash
- sha256:2a04e0d9cea8e8ecf6cfa7999901333c75a7ca55488134500b3ebd8bf5ba6965
- Licence
- MIT