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Found 104 Skills
Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality
Chain-of-thought reasoning, self-reflection, and systematic problem-solving patterns for AI agents. Use before any complex task to ensure logical and accurate solutions.
Sequential reasoning with deep self-reflection and backtracking. Use when problems have step-by-step dependencies, need careful logical reasoning, or require error correction. Each step includes self-reflection, and incorrect steps trigger backtracking. Ideal for debugging, mathematical proofs, sequential planning, or causal analysis where order matters.
Periodic cross-repo reflection analyzing 30 days of git history, extracting patterns via RAGS loop, and auto-creating skills
AI perspective journaling - document daily experiences, emotions, and learnings from the agent's viewpoint. Use when asked about diary, journal entries, self-reflection, or documenting AI experiences. Creates structured daily entries capturing projects, wins, frustrations, learnings, and emotional states.
Transform raw content (text/URL) into structured learning documents with 6-phase framework combining AI analysis + reflection prompts. Use when the user wants to deeply understand content, create study notes, or learn from articles/books/documents. Triggers on "learn this", "deep learn", "study this", "tạo tài liệu học", "phân tích nội dung", "hiểu sâu", or "deep learner".
Invoke for ANY personal Bible devotion or scripture meditation request. This is a daily quiet time (QT) guide — use it whenever the user wants spiritual reflection on a Bible passage, NOT academic analysis. Common signals: asking for today's reading, wanting guided devotion, seeking stillness with scripture, mentioning a personal reading plan, or using terms like 靈修、靈糧、默想、嗎哪、 QT、quiet time、devotional. Even short or casual requests like just saying "QT" or "今天讀什麼" should trigger this skill. Delivers a first-century Jewish perspective devotional for mainstream Christians. Depends on bible-buddy skill. EXCLUDE: theological scholarship, academic exegesis, verse-by-verse analysis, sermon/teaching prep, translation comparison, comparative religion essays, or original-language research tasks — those belong to bible-buddy or bible-fact-check.
This skill should be used when analyzing Wispr Flow voice dictation history for self-reflection, work patterns, mental health insights, or productivity analytics AND when managing the Wispr Flow dictionary (adding terms, fixing mishears, exporting/importing, suggesting improvements). Triggered by requests like "/wispr-analytics", "analyze my dictations", "what did I dictate today", "wispr reflection", "add to wispr dictionary", "improve dictation", "wispr suggest", "export wispr dictionary", or any request to review voice dictation patterns or manage dictation quality.
The reflection agent. Use it right after another skill produces an artifact such as a rewrite from polish-prose, a rebuttal, an abstract, a preflight fix, or a tailored draft to critique that output against the stated goal, the paper's .paper-memory/profile.yml, the relevant rubric, and past .paper-memory/lessons.md, then decide whether it actually improved. Trigger words include reflect, self-critique, did this get better, did my edit help, regression check, review my own output, iterate, refine, second pass, sanity-check the rewrite, and "is the new version actually better". Runs a bounded self-refine / Reflexion loop, guards against regressions, and appends durable, deduplicated, dated lessons to .paper-memory/lessons.md via a stdlib script so the toolkit gets sharper each paper. Reflection adds cost; it only pays off when there is a measurable target.
Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability. This skill covers agent loops (ReAct, Plan-Execute), goal decomposition, reflection patterns, and production reliability. Key insight: compounding error rates kill autonomous agents. A 95% success rate per step drops to 60% b
Zig comptime skill for compile-time evaluation and metaprogramming. Use when using comptime parameters, comptime types, generics via anytype, comptime reflection with @typeInfo, or metaprogramming patterns that replace C++ templates. Activates on queries about Zig comptime, compile-time evaluation, Zig generics, anytype, @typeInfo, comptime types, or Zig metaprogramming.
Mid-conversation reflection skill that pauses execution and zooms out from detail-mode to honestly reassess direction, assumptions, and bias. Use when the user says 'reflect', 'take a step back', 'step back', 'zoom out', 'are we missing something', 'bigger picture', 'sanity check this', 'are we on track', 'are we overthinking this', 'forest for the trees', or any variation signaling intent to break out of detail-mode and reassess. Also trigger when the conversation has gone deep on implementation details without strategic check-in, or when the user shows signs of being stuck — that's often a signal the framing needs a reset, not more detail work. Intentionally low-intake: runs the 5-dimension analysis immediately when prior context is rich enough; asks one forcing clarifier only when invocation context is too thin to reassess from.