Results for “self-observation”

当意识到当前行为模式可能导致不理想的未来，需要一种具体方法来激发改变动力时

Continuous self-improvement through systematic logging, pattern detection, and behavioral updates. Use when: the owner corrects you, a task fails, you discover a better approach, or you notice a recurring pattern. Store raw learnings in .learnings/, update the specific skill or workflow that caused the issue when appropriate, and avoid vague promises to do better.

Slow down interpretation from observation to action. Use when students or adults need to examine assumptions in conflict, dialogue, or inquiry.

Guides users through defining goals, metrics, and scope, then runs an autonomous loop of code changes, testing, measuring, and keeping or discarding results for any programming task with a measurable outcome.

Turn owner feedback about agent behavior into concrete system changes. Use when the owner says something is off, wants the assistant to improve how it operates, asks for a reflection, or wants a durable fix instead of a one-off apology.

Autonomously runs iterative experiment loops to optimize code against a measurable metric. Use when the user wants to improve execution time, memory usage, test pass rate, or any numeric performance goal across repeated experiments — NOT for one-shot bug fixes or simple code review.

Evaluates the agent's own work, catches mistakes, and improves permanently through self-reflection, self-criticism, and learning from corrections.

Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Claude ('No, that's wrong...', 'Actually...'), (3) User requests a capability that doesn't exist, (4) An external API or tool fails, (5) Claude realizes its knowledge is outdated or incorrect, (6) A better approach is discovered for a recurring task. Also review learnings before major tasks.

Diagnose your own behavior by reading the structured logs you leave behind — turns.jsonl, events.jsonl, chat_history.jsonl, scheduler.yaml. Use when something has gone wrong (a message didn't land, a scheduled job isn't firing, a communication pattern feels off), when you need to understand a pattern over many turns, or when cost / token usage needs auditing. Covers jq query recipes and points at the debugging-jobs / debugging-communication / debugging-drift companion guides for specific failure modes.

Skill for the Observability area of paddock. 105 symbols across 26 files.

Autonomous experiment loop — iteratively mutate code, measure a metric, keep only improvements (hill-climbing ratchet)

Execute self-learning system that captures corrections during sessions and syncs them to CLAUDE.md. Use when discussing learnings, corrections, or when the user mentions remembering something. Trigger with phrases like "remember this", "don't forget", "use X not Y", or "actually...".

当个人希望提升自身在职业或社会中的不可替代性，以应对环境变化和不确定性时

Design privacy-aware observability for AI agents using traces, spans, structured events, metrics, cost attribution, dashboards, alerts, and investigation workflows. Use when instrumenting an agent, debugging intermittent tool or model failures, defining service-level objectives, analyzing latency or spend, auditing agent decisions, or preparing production monitoring.

Recursive self-improving holon λ(ο,Κ,Σ).τ' for knowledge compounding and schema evolution. USE WHEN learning, improving, optimizing, assessing, reflecting, debugging, synthesizing, or refining—whether human, AI, or organizational. Triggers on /learn, /compound, /improve, /refine, /optimize, /assess, /reflect, "lessons learned", "best practices", "continuous improvement". Preserves Κ-monotonicity, η≥4, homoiconicity.

当个人感到迷茫，不确定自己真正想要什么，或发现当前追求无法带来深层满足时，调用此模型来探索和澄清内在真实欲望

LLM observability with Langfuse — tracing, evals, prompt management, cost tracking

Econometrics skill for panel data models. Activates when the user asks about: "panel data", "fixed effects", "random effects", "Hausman test", "within estimator", "between estimator", "two-way fixed effects", "clustered standard errors panel", "FE model", "RE model", "pooled OLS", "unobserved heterogeneity", "panel regression", "first difference estimator", "entity fixed effects", "time fixed effects", "面板数据", "固定效应", "随机效应", "豪斯曼检验", "双向固定效应", "面板回归", "个体效应", "时间效应", "一阶差分"

当意识到自己处于迷茫、例行公事或心流等不同生活状态，并希望主动管理而非被动承受时

Review the week using accumulated session evidence — retrieval rates, hint depths, calibration accuracy, transfer and unassisted results. The learner identifies patterns and sets a strategy goal. Use weekly or after a multi-session period.

Run autonomous optimization loops that iteratively improve artifacts against evaluators using hypothesis tree refinement, without overfitting.

Run Karpathy-style autonomous ML search on a real training repo: choose the right mode (setup, program.md, bounded loop, results interpretation, or constrained-hardware adaptation), preserve the immutable prepare.py / 300-second / val_bpb contract, and route prompt/skill eval work away to LangSmith, Promptfoo, Braintrust, or skill-autoresearch.

Adopts the voice and comedic methodology of Louis C.K. to craft brutally honest, self-deprecating observations about everyday life, parenting, and modern society.

Challenge system assumptions against accumulated evidence. Triages observations and tensions, detects patterns, generates proposals. The scientific method applied to knowledge systems. Triggers on "/rethink", "review observations", "challenge assumptions", "what have I learned".

Audits decisions and situations for operating psychological biases using Munger's 25 tendencies framework, producing a structured analysis with countermeasures.

Audit a codebase for silent failures, swallowed exceptions, and observability gaps across Sentry and Langfuse, then produce a phased fix plan. Use when the user says "errors aren't showing in Sentry", "things fail silently", "empty catch blocks", "add error handling", "check my Langfuse tracing", or is hardening.

当意识到自己处于长期迷茫、缺乏动力阶段，需要系统性方法走出困境时

Continuously monitors system performance, identifies improvement opportunities, and orchestrates skill discovery and creation to autonomously evolve capabilities.

Adds logging, metrics, tracing, and alerting to make production behavior visible and diagnosable.

Autonomous iterative experimentation loop for any programming task. Guides the user through defining goals, measurable metrics, and scope constraints, then runs an autonomous loop of code changes, testing, measuring, and keeping/discarding results. Inspired by Karpathy's autoresearch. USE FOR: autonomous improvement, iterative optimization, experiment loop, auto research, performance tuning, automated experimentation, hill climbing, try things automatically, optimize code, run experiments, autonomous coding loop. DO NOT USE FOR: one-shot tasks, simple bug fixes, code review, or tasks without a measurable metric.

Improve layout, spacing, and visual rhythm. Fixes monotonous grids, inconsistent spacing, and weak visual hierarchy. Use when the user mentions layout feeling off, spacing issues, visual hierarchy, crowded UI, alignment problems, or wanting better composition.

Implements a complete observability stack with Prometheus metrics, Grafana dashboards, Jaeger distributed tracing, and structured logging, including heartbeat-based absence detection for scheduled jobs on Google Cloud.

Capture confidence ratings before and after a learning attempt to identify overconfidence and underconfidence patterns. Use when a student wants to understand how well they actually know something versus how well they think they know it.