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Found 20 Skills
Trigger: Call this skill when you need to collect opinions from multiple parties, integrate fragmented feedback into an actionable plan, or bring the plan back to real users/executors for validation. Common trigger signals include stakeholder input, user feedback, opinion summarization, alignment and verification. English: Trigger when input must be gathered from many people, synthesized into a clearer plan, and returned to the affected users or executors for validation. Use this skill for a collect-synthesize-validate loop.
Asks for user feedback after each task or cron job completion and runs a recursive learning flow. If output is good, asks what was good until 10 approvals; if needs improvement, asks why/how/what via multiple choice plus optional examples, uses web search and iterative thinking to resolve, and caps iterations by severity (slight 5, medium 10, severe 20). Keeps feedback non-intrusive. Use when completing discrete tasks or cron jobs for the user.
A method for iteratively improving text instructions for agents (skills / slash commands / task prompts / CLAUDE.md sections / code generation prompts) by having unbiased executors run them, then evaluating from both perspectives (executor self-report + instruction-side metrics). Repeat until improvement plateaus. Use immediately after creating or significantly revising a prompt or skill, or when you suspect the reason an agent isn't behaving as expected is due to ambiguity in the instructions.
Orchestrate copy exploration. Brief, generate 5 distinct approaches, adversarial review, iterate to 90+ composite, present catalog, user selects, execute.
Iteratively improve any output until measurable criteria are met. Use when the user wants to refine existing work against specific standards — whether it's code, prose, data, config, or any other artifact. Triggers on phrases like "improve this", "make it better", "iterate", "refine", "keep improving", "not good enough yet", "optimize this", "polish this", "tighten this up", or when the user provides criteria and wants repeated improvement until they're satisfied. Also use when the user gives feedback on output and expects you to keep refining, even if they don't say "improve" explicitly.
A methodology for iteratively improving agent-facing text instructions (skills / slash commands / task prompts / CLAUDE.md sections / code-generation prompts) by having a bias-free executor actually run them and evaluating two-sidedly (executor self-report + instruction-side metrics). Keep iterating until improvements plateau. Use it right after creating or substantially revising a prompt or skill, or when you want to attribute an agent's unexpected behavior to ambiguity on the instruction side.
Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.
Reflect on previus response and output, based on Self-refinement framework for iterative improvement with complexity triage and verification