Based on the Claude Code source code that leaked unexpectedly last week, Drew Breunig mapped out how the system prompt is assembled: components fall into two categories—always included and conditionally included—and shift based on toggles like output_style, repl_mode, user_type_ant, skills_enabled, and mcp_connected.
Why it matters: The author breaks down the dynamic assembly logic behind Claude Code's system prompt, showing how conditional context engineering works in practice.
Thariq Shihipar, an engineer on Anthropic’s Claude Code team, summed up what the team learned from using hundreds of active Skills internally, sorting them into nine categories: library and API references, product validation, data acquisition and analysis, business process automation, code scaffolding, code quality and review, CI/CD and deployment, operations runbooks, and infrastructure operations.
Why it matters: Anthropic’s internal classification system for hundreds of Skills, along with its writing tips, can be adapted to help teams design their own Skills.
A Martin Fowler team article breaks down context engineering for coding agents, sorting context configuration into reusable prompts (instructions and guidelines), context interfaces (tools, MCP Servers, Skills), and workspace files. It then splits these by "who decides what gets loaded" into three categories: the LLM, the human, and the agent software.
Why it matters: Using Claude Code as an example, this piece walks through how to configure context for coding agents and lays out the trade-offs between loading on demand and building up gradually.
Why it matters: It lays out a complete workflow for systematically validating Codex Skills with evals, from defining success criteria to deterministic checks and scoring.
Claude Code lets the model know which Skills exist by injecting their names and descriptions into the system prompt. When there are too many Skills, or the description fields are too long, the system prompt stops listing them, so the model can't use them — and the prompt also tells the model not to use any Skill that isn't listed.
Why it matters: The author explains why Claude Code doesn't trigger installed Skills, and gives a temporary fix using environment variables that you can apply right away.
Why it matters: The author ported Claude's SKILL.md system to the Codex CLI, with tool mappings and install instructions, so you can judge whether reusing Skills across models is feasible.
The author built the episodic-memory plugin for Claude Code so it can search past session logs. By default, Claude Code deletes the .jsonl session logs under ~/.claude/projects after one month; you can extend retention via cleanupPeriodDays in ~/.claude/settings.json.
Why it matters: The author turned Claude Code's session logs into semantically searchable episodic memory, so readers can judge for themselves how long-term context is preserved across sessions.
Anthropic rolled out its first-party Skills system simultaneously on Claude Code, Claude.ai, and the Claude API, and author Jesse Vincent quickly followed with a new version of Superpowers built on the official Skills.
Why it matters: Drawing on nearly a month of hands-on use, the author compares the official Skills with his own setup and lays out the trade-offs involved in migrating.
Author Jesse Vincent released Superpowers, a set of Skills built on Claude Code's new plugin system. Once installed, it injects a guiding prompt through the session-start hook, prompting Claude to proactively search for and use these Skills.
Why it matters: The author packaged his own coding-agent workflow into an installable Skill plugin, so readers can directly reuse his implementation flow from brainstorming to TDD.