Enforce proof-based verification and root-cause analysis for agentic coding tasks.
npx skills add https://github.com/uladzemer/fable-thinking --skill fable-thinking
Please help me install this Agent Skill. Skill: fable-thinking Source: https://github.com/uladzemer/fable-thinking/tree/main Command: npx skills add https://github.com/uladzemer/fable-thinking --skill fable-thinking
This skill addresses the common agentic failure where smaller models (Opus, Sonnet, Haiku) drift from goals, skip verification, or patch symptoms instead of root causes, leading to wasted tokens and rework.
Instruct your agent to read the fable-thinking skill at the start of your session to enforce frontier-level rigor on your current task.
High-intent search queries and answers about installing and using this skill.
To prevent agentic coding drift during complex debugging, enforce operational discipline protocols like goal anchoring and proof-based verification. This ensures models verify hypotheses with external evidence before claiming completion, avoiding superficial symptom patching.
Proof-based verification for multi-file refactoring is a self-verification protocol requiring agents to validate changes with external evidence. It enforces root-cause analysis over superficial patching, ensuring reliable outcomes on non-frontier models.
Yes, operational discipline calibration adapts its verification intensity based on task size. It scales from trivial edits to complex refactoring, applying appropriate rigor to prevent wasted tokens and rework across varying agentic coding complexities.
AI agents skip verification and patch symptoms due to lacking operational discipline on non-frontier models. Enforcing strict root-cause analysis and proof-based verification protocols forces the agent to validate hypotheses with external evidence before completing tasks.
Yes, you can transfer frontier-level coding discipline to smaller models like Sonnet or Haiku by applying operational discipline procedures. This enforces rigorous self-verification and root-cause analysis, mitigating common failure modes like goal drift and superficial patching.
Forcing rigorous verification on non-frontier models increases token consumption per task due to strict adherence to proof-based procedures. However, this trade-off prevents wasted tokens from rework caused by goal drift, superficial patching, and patch-cascade failures.
uladzemerEnforce evidence-based verification and root-cause analysis for agentic coding tasks.
tomgunReproduce bugs with regression tests and apply minimal root-cause fixes.
NagyViktVerify command outputs and test results before claiming task completion.
jimmymalhanExecute script-based tasks with enforced checkpoints and fail-fast error reporting.
