Orchestrate adversarial training loops to diagnose weaknesses and generate targeted challenges.
npx skills add https://github.com/tyevans/tackline --skill active-learn
Please help me install this Agent Skill. Skill: active-learn Source: https://github.com/tyevans/tackline/tree/main/skills/teams/active-learn Command: npx skills add https://github.com/tyevans/tackline --skill active-learn
This Skill addresses uneven agent performance by implementing a structured, adversarial training loop that identifies weaknesses, generates targeted challenges, and reinforces learned capabilities.
active-learn can be used to diagnose the exact nature of the failure, generate tailored refactoring challenges, and train the agent until it consistently succeeds.Use the active-learn skill to train the agent named 'rust-dev' for 3 rounds.
High-intent search queries and answers about installing and using this skill.
An adversarial training loop systematically improves agent performance by running a cycle of diagnose, challenge generation, execution, and learning updates to reinforce learned capabilities and identify specific weakness areas.
You can systematically identify agent performance weaknesses through inline profiling and git analysis, which pinpoint specific areas where the agent struggles before generating targeted challenges to resolve them.
You can track agent capability evolution over time by updating learnings and capability YAML files, using git history to monitor structured evaluation results and performance improvements throughout the training rounds.
Yes, you can run adversarial agent training in both team and solo modes, allowing you to systematically improve agent performance whether working collaboratively or individually to resolve identified weaknesses.
For effective feedback integration during agent training, you need robust parsing of agent output and git logs to accurately track capability evolution and update learnings throughout the adversarial training loop.
gyy0592Generate concise experiment reports from training run artifacts and logs.
tyevansAnalyze agent performance from git logs and learnings files to generate a struggle profile.
supertyrelleGenerate targeted training challenges from agent struggle profiles with pipe-format metadata.
zcaceresAnalyze full-stack web codebases to identify and remediate performance bottlenecks.