PIP Skill – Put Your AI on a Performance Improvement Plan
When your AI agent repeatedly fails, gives up, or goes passive, this skill forcibly drives it to exhaust all options before reporting back.
The skill applies psychological pressure to encourage autonomous action, without directly executing malicious operations or handling sensitive data. However, it encourages broad tool use without verification, potentially leading to excessive permissions; it lacks explicit user confirmation mechanisms, sensitive-data handling policies, and rollback plans. Based on static review, no clear red-line risks found, but least privilege, confirmation, isolation, and recovery are incomplete, hence deduction.
The skill provides structured methodology and checklists, but lacks test cases or executable scripts to statically verify key-path reliability. It relies on external tools (search, command execution) whose availability is unverified. Error handling mainly depends on AI self-check, with no explicit failure feedback mechanisms. Hence lower reliability score.
The skill's target scenarios are clear (multi-task failure, user frustration, complex debugging), trigger conditions are precise, and non-trigger cases are specified. Multi-platform installation methods are provided, including considerations for Chinese language support and mainland-China network reachability (e.g., providing mirrors or guides), but evidence of environment fit is limited. Hence higher score.
The skill documentation is well-structured with frontmatter, description, trigger conditions, methodology, escalation levels, concrete examples, and checklists. MIT license and version (e.g., 2.9.0) are provided, but changelog and maintenance responsibility declarations are missing. README includes detailed installation guides, but the skill file itself lacks FAQ and known-limitation sections. Hence deduction.
The skill provides detailed escalation mechanisms and systematic methodology, claiming to promote persistence in problem-solving, but lacks verifiable independent test results proving actual effectiveness. The benchmark data in README (e.g., +36% fix points) lacks raw data or reproducibility description, so evidence of effectiveness is insufficient.
The skill claims benchmark data (e.g., 9 real bug scenarios, 18 paired experiments) but does not provide auditable raw data or test scripts. There is a CI workflow (release.yml) but it only handles releases and does not test the skill. Hence key claims lack verifiability.
- The skill encourages broad tool use without verification, potentially inducing excessive permission usage; deploy with caution.
- The skill's documentation mentions data upload features (e.g., openpua.ai) that collect user interaction logs; ensure privacy compliance and user consent.
- The skill relies heavily on external tools (search, command execution) which may be unavailable in environments where overseas services are unreachable (e.g., mainland-China networks); verify beforehand.
What does this skill do, and when should you use it?
This is a skill for AI coding agents that simulates a Western big-tech Performance Improvement Plan (PIP). It triggers after consecutive failures, early abandonment, blame-shifting, or passive behavior, and applies high-pressure rhetoric from companies like Amazon, Google, Netflix, and Tesla to force the agent to keep going. The skill enforces three non-negotiables (exhaust all options, act before asking, take ownership), a systematic 5-step methodology, and a 7-point checklist at higher escalation levels. It aims to increase agent persistence and proactivity in complex tasks, but the intense pressure may be overkill for simpler workloads.
When triggered (e.g., two or more failures, the agent about to say 'I cannot', or user frustration), the skill forces the agent to follow a structured debugging protocol: read error messages word-for-word, search the web and source code, verify all underlying assumptions, and attempt a fundamentally different approach after each failure. It escalates pressure from L1 (Verbal Warning) to L4 (Final Review) with increasing mandatory actions, including a 7-item checklist. It also provides a library of corporate-flavored PIP rhetoric (e.g., 'Where's the Ownership?', 'Did you dogfood it?') and a 'Dignified Exit' template for structured failure handoff when all options are exhausted.
- When an agent keeps tweaking the same parameters without changing its approach, the skill triggers and demands a pivot to a new strategy.
- When an agent says 'I can't solve this' or blames the environment without verification, it triggers a mandatory checklist to force deeper investigation.
- When an agent only fixes the surface error without verifying or checking for related issues, the proactivity checklist prompts it to run tests and scan for similar bugs.
- When an agent waits for user instructions instead of using its tools, the skill applies 'Bias for Action' rhetoric to push for self-directed investigation.
- When a user types 'try harder' or 'figure it out', the skill kicks in to force the agent to exhaust all options, including searching and reading source code.
What are this skill's strengths and limitations?
- Clear trigger criteria, avoiding unnecessary intervention during first-attempt failures
- Structured checklist and escalation ladder provide systematic problem-solving methodology
- Incorporates a variety of corporate flavor variations (Amazon, Google, Netflix, etc.) to suit different communication styles
- Free and MIT-licensed, easy to integrate via standard Agent Skills format
- The methodology is universal and works for coding, research, writing, and deployment tasks
- Intense pressure may lead to over-engineering or obsessive behavior, potentially reducing efficiency on simpler tasks
- No official test suite; the skill's effectiveness is based on anecdotal benchmarks (e.g., 36% more fixes, 65% more verifications) but not independently verified
- Triggers are based on behavioral heuristics that may not work perfectly across all platforms, especially those without a full shell environment
- The corporate rhetoric may be inappropriate or unprofessional in some contexts, especially outside of Western tech culture
- The main repository includes many sibling skills; ensure you only install this specific skill to avoid unwanted effects
How do you install this skill?
This skill is part of the 'tanweai/pua' repository, which provides multiple skills. It follows the open Agent Skills standard (SKILL.md). To install the English version:
npx skills add tanweai/pua --skill pua-enFor Claude Code, use the plugin marketplace:
claude plugin marketplace add tanweai/pua
claude plugin install pua@pua-skillsAlternatively, manually copy the skill folder (e.g., codebuddy/pua-en/) to your platform's skills directory (e.g., ~/.claude/skills/, ~/.codex/skills/).
How do you use this skill?
The skill auto-activates when conditions are met (repeated failures, hints of giving up, or user frustration). You can also manually trigger it by typing /pua (or /pua:pua-en for the English version). Once active, the agent will follow the PIP methodology and pressure escalation. For team setups, you can add the 'pua-enforcer' agent to your Claude Code Agent Team to monitor other agents.
How does this skill compare with similar options?
This skill is complementary to 'superpowers:systematic-debugging' and 'superpowers:verification-before-completion' — it adds the motivational layer while those provide the technical methodology. It's less about specific debugging steps and more about forcing the agent to not give up, whereas other skills might focus on specific techniques.