muratcankoylan

A comprehensive collection of Agent Skills for context engineering, harness engineering, multi-agent architectures, and production agent systems. Use when building, optimizing, evaluating, or debugging agent systems that require effective context management…

This skill should be used for project-level decisions about LLM-powered systems: whether an LLM is the right primitive for the task at hand, the shape of a multi-stage batch or agent pipeline, token and cost estimation, choosing between single-agent and…

This skill should be used when writing, enhancing, or evaluating the launch prompt for a long-running autonomous agent or a parallel multi-agent orchestration attacking a hard problem: pseudo-formal task briefs that define terms and an exact success predicate…

This skill should be used when the harness, scaffold, workflow, or optimizer itself is the optimization target: recursive self-improvement (RSI) loops, meta-harnesses, self-improving harnesses that mine their own failures and propose bounded edits,…

This skill should be used for book-to-SFT pipelines: ePub extraction, literary segmentation, author-voice dataset construction, style-transfer training, LoRA workflows, and model evaluation for voice replication.

This skill should be used for personal operating-system workflows: content creation, voice consistency, relationship lookup, meeting preparation, weekly review, goal tracking, personal brand management, and network management.

Ensure thorough validation, error recovery, and transparent reasoning in research tasks with multiple tool calls

Debug and optimize AI agents by analyzing reasoning traces, context degradation, tool confusion, instruction drift, repeated task failures, and performance regressions.

End-to-end system for creating supervised fine-tuning datasets from books and training style-transfer models. Covers text extraction, intelligent segmentation, synthetic instruction generation, Tinker-compatible output, LoRA training, and validation.

Design and evaluate context compression strategies for long-running agent sessions. Use when agents exhaust memory, need to summarize conversation history, or when optimizing tokens-per-task rather than tokens-per-request.

Build evaluation frameworks for agent systems. Use when testing agent performance, validating context engineering choices, or measuring improvements over time.

Design tools that agents can use effectively, including when to reduce tool complexity. Use when creating, optimizing, or reducing agent tool sets.

Set up and end-to-end test the peoplereadme CLI (init, validate, schema changes). Use when verifying changes to src/peoplereadme/, schemas/persona.schema.json, or persona package scaffolding.

How to test the peoplereadme CLI end-to-end, including the fidelity harness (eval/calibrate) with a synthetic persona fixture and live LLM calls.

Use this skill when the user asks to "analyze my content", "learn my writing style", "research competitors", "find content angles", "improve my blog", "write like me", "embody my brand voice", or mentions content strategy, voice analysis, competitive…

Generates a BACKPRESSURE.md file for any project. Analyzes codebase and PRD to produce deterministic self-verification checks that AI coding agents run on their own output.