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Skill evidence / concentrate-forces
Skill profile

concentrate-forces

集中兵力:多件事同时争抢时间、注意力或预算时,列出全部待办,选定唯一主攻目标并公开锁定,彻底解决、验证后再转向下一个。当待办过多、推进分散、"每件都做了一点但都没做完"、需要决定先做什么时触发;只有一个任...

What this skill does for you

集中兵力:多件事同时争抢时间、注意力或预算时,列出全部待办,选定唯一主攻目标并公开锁定,彻底解决、验证后再转向下一个。当待办过多、推进分散、"每件都做了一点但都没做完"、需要决定先做什么时触发;只有一个任务、任务彼此独立可并行、用户明确要求同时推进时不触发。 English: Concentrate forces. When several tasks compete for time, attention, or budget, enumerate them, lock onto a single main target, finish and verify it completely, then move on. Trigger when priorities sprawl, effort is scattered, or you must decide what to do first; skip when there is one task, tasks are independent and parallel, or the user explicitly wants them advanced together.

Quoted from the skill description in the pinned source

Who made it

Publisherhughyau
Repositoryqiushi-skill
Installs297as of Sep 27, 2026

Use it with Edge

Set up Edge for me: read getedge.cc/SKILL.md and follow it. Then use Edge to load the concentrate-forces skill from hughyau/qiushi-skill.

Paste it into Claude, ChatGPT, Codex or Cursor.

Or read the skill source first.

Security evidence

Security state at 2026-09-30: reviewed. State definitions. This records static evidence for this revision; live load eligibility is checked again.

3 static scans, 0 counted findings. The listed scanners recorded the results shown for this package revision.

ScannerDateResultFindings
Edge static checks2026-09-27Pass0
Cisco skill-scanner2026-09-27Pass0
Semgrep Edge rules2026-09-27Pass0

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
sha256:1e79285d39a3b0cc6cf4a0f4719196d4c5490ca0459b6fa2e4b07096194731f6
Licence
MIT