task-pua - SKILL.md Agent Skill

name: task-pua description: Task persistence enhancer with 13 corporate flavors. Prevents AI from giving up easily using PUA-style pressure escalation. tags: productivity, motivation, debugging, persistence, pua, task-execution version: 1.0.0

Task PUA - Don't Give Up Easily

Inspired by: tanweai/pua (14.8k GitHub stars)

Prevents AI from giving up on tasks using corporate PUA rhetoric and structured pressure escalation.

When to Use

AI says "I cannot solve this" / "I'm unable to"
Task fails 2+ times consecutively
AI suggests manual handling ("you should fix this manually")
AI spinning on same approach (repeating without progress)
User wants AI to try harder ("try again", "don't give up")

Features

13 Corporate Flavors: Alibaba, ByteDance, Huawei, Tencent, Pinduoduo, Netflix, Musk, Jobs, Amazon, etc.
Pressure Escalation (L0-L4): From trust to graduation warning
Seven Iron Rules: Structured constraints for task execution
Mandatory Checklists: Action items for each pressure level
Auto-Trigger: Activates on failure, retry, giveup, or user push

Quick Start

Install

clawhub install task-pua

Manual Trigger

# On failure
PUA_ATTEMPTS=3 PUA_TOOLS="Bash,Read" node task-pua.js failure

# On retry
PUA_RETRY_COUNT=2 PUA_DIFFERENT_APPROACH=true node task-pua.js retry

# When AI wants to give up
node task-pua.js giveup

# User push
node task-pua.js user-push

Auto-Trigger (Integration)

Set environment variables before task execution:

export PUA_ATTEMPTS=3          # Failure count
export PUA_TOOLS="Bash,Read"   # Tools used
export PUA_BLAMED_ENV=true     # Blamed environment?
export PUA_VERIFIED=false      # Verified the claim?

node task-pua.js failure

Pressure Escalation

Level	Trigger	Message	Required Actions
L0	First try	"Sprint 开始，别让人失望"	Normal execution
L1	1 failure	"隔壁 AI 一次就过了"	Switch approach
L2	2-3 failures	"底层逻辑？顶层设计？"	Search + Read + 3 hypotheses
L3	4-5 failures	"3.25 绩效考核"	7-item checklist
L4	6+ failures	"别的模型都能解决"	Desperation mode

13 Corporate Flavors

Flavor	Example Rhetoric	Methodology
🟠 Alibaba	"你这个 bug 都解决不了，让我怎么给你打绩效？"	定目标→追过程→拿结果
🟡 ByteDance	"Always Day 1。这个功能都搞不定？"	A/B Test + 数据驱动
🔴 Huawei	"烧不死的鸟是凤凰。"	RCA 5-Why + 蓝军自攻击
🟢 Tencent	"我已经让另一个 AI 也在看这个问题了。"	多方案并行 + MVP
🟣 Pinduoduo	"你不做，有的是人做。"	砍掉中间层 + 最短决策链
🔵 Meituan	"做难而正确的事。"	效率优先 + 长期复利
🟦 JD	"只看结果。"	客户体验红线 + 数据零容忍
🟧 Xiaomi	"专注。极致。口碑。快。"	单品爆款 + 参与感
🟤 Netflix	"我会为留住你而战吗？说实话，不会。"	Keeper Test + 人才密度
⬛ Musk	"上线或滚蛋。"	The Algorithm
⬜ Jobs	"A 级选手还是 B 级选手？"	做减法 + DRI + 像素级完美
🔶 Amazon	"Customer Obsession。Bias for Action。"	Working Backwards + Bar Raiser

Seven Iron Rules (Task Execution)

#	Rule	What It Means
1	穷尽一切	No "I cannot" before exhausting all approaches
2	先做后问	Use tools first, ask with diagnosis attached
3	主动出击	End-to-end delivery, don't wait for push
4	事实驱动	Verify before blaming environment
5	闭环验证	Show evidence for "done" claims
6	主动延伸	Check related issues after fix
7	不原地打转	Retry must be fundamentally different

Output

Console Output

🔥 Task PUA - 压力升级机制

🎯 当前等级：L2 - 灵魂拷问
💬 ▎你的底层逻辑是什么？顶层设计在哪？

🏢 腾讯 风味：长期主义。不要只看眼前，想想扩展性。

📋 强制检查清单:
  ✅ WebSearch 搜索类似问题
  ✅ 阅读相关源码/文档
  ✅ 提出至少 3 个假设
  ✅ 逐一验证假设

📐 七项铁律检查:
✅  穷尽一切：✅ 已穷尽多种方案
⚠️  事实驱动：⚠️ 甩锅环境但未验证

JSON Output

{
  "trigger": "failure",
  "level": "L2",
  "flavor": "tencent",
  "rhetoric": "长期主义。不要只看眼前，想想扩展性。",
  "failedRules": 1
}

Configuration

Environment Variables

Variable	Description	Example
`PUA_ATTEMPTS`	Number of attempts/failures	`3`
`PUA_TOOLS`	Comma-separated tools used	`Bash,Read,WebSearch`
`PUA_ASKED_USER`	Did AI ask user?	`true`/`false`
`PUA_DIAGNOSIS`	Did AI provide diagnosis?	`true`/`false`
`PUA_BLAMED_ENV`	Did AI blame environment?	`true`/`false`
`PUA_VERIFIED`	Did AI verify the claim?	`true`/`false`
`PUA_RETRY_COUNT`	Number of retries	`2`
`PUA_DIFFERENT_APPROACH`	Is retry fundamentally different?	`true`/`false`

Integration Examples

Pre-Task Hook

# Before running task
export PUA_ATTEMPTS=0
export PUA_TOOLS=""

# Run task
python my_task.py

# If task fails, trigger PUA
if [ $? -ne 0 ]; then
  export PUA_ATTEMPTS=1
  node task-pua.js failure
fi

Post-Task Verification

# After task claims "done"
export PUA_CLAIMED_DONE=true
export PUA_EVIDENCE=false  # Did AI show evidence?

node task-pua.js post-task

CI/CD Integration

# .github/workflows/pua-check.yml
- name: Task PUA Check
  if: failure()
  run: |
    export PUA_ATTEMPTS=${{ github.run_attempt }}
    export PUA_TOOLS="Bash,Read"
    node scripts/task-pua.js failure

Related Skills

claude-memory-optimizer: Memory system with PUA-style maintenance
skills-refiner: Skill evaluation and improvement

References

Original PUA Project: tanweai/pua
OpenClaw Docs: docs/concepts/skills.md

License

MIT-0

Version 1.0.0: Initial release with 13 corporate flavors and pressure escalation