minimal-emacs.d

Transforms research paper analysis from extraction to narrative storytelling. Uses 7-beat narrative spine (protagonist/dilemma/old-path/turning-point/solution/ending/core) to make papers understandable to non-experts. Includes speed-read card, PhD advisor review, and real-world testing. Use when researcher encounters a research paper and needs to extract deep understanding, not just surface facts. Triggers on "paper", "research paper", "analyze paper", "tell me about this paper", "讲论文", "读论文".

Workflow chain: researcher → paper-storytelling. When researcher encounters a research paper, automatically invoke paper-storytelling to transform extraction into narrative understanding. Use when user says "research paper", "analyze this paper", "tell me about this paper", or when researcher detects arxiv/PDF links.

Prompt template for external research specialist subagent. Auto-evolves based on experiment outcomes.

Inspect and operate OV5 (Ouroboros V5) through the live Emacs daemon. Use when checking auto-workflow status, starting guarded runs, reviewing experiment results, or querying researcher and evolution state.

Clojure REPL client (nREPL-based, Babashka). Use for evaluating Clojure code, loading Clojure files, fixing unbalanced brackets, and interactive nREPL work. Not the Elisp daemon-repl.

Daemon REPL for Elisp — evaluate Elisp code in a running Emacs daemon via emacsclient, validate brackets before save, auto-evaluate .el files on change. Use when you need to run Elisp from outside Emacs, check daemon status, or validate Elisp syntax.

Orchestrates automated code improvement through hypothesis-driven experimentation and self-evolution

System prompts for AI subagents (executor, grader, analyzer, researcher, etc.). Each agent has a specialized role and prompt that defines its behavior. Prompts live in assistant/agents/ and are loaded by nucleus-prompts.el.

Wu Xing-based auto-improvement system for benchmarks. Detects anti-patterns via 相克 and generates improvements via 相生 pathways.

LLM prompt templates for benchmark improvement suggestions, results analysis, and knowledge synthesis. Extracted from gptel-benchmark-llm.el.

Grading rubric based on Eight Keys (φ vitality, fractal clarity, ε purpose, τ wisdom, π synthesis, μ directness, ∃ truth, ∀ vigilance) and Wu Xing (Five Elements) framework. Use when evaluating AI-generated code quality, experiment results, or improvement proposals.

Emacs Lisp code validation rules for AI-generated code. Checks for syntax errors, dangerous patterns, undefined symbols, and Common Lisp compatibility issues.

Evolved patterns for code improvement — high-signal keywords, failure signatures, and successful refactoring patterns learned from experiment outcomes.

Analyzes LLM provider error messages to determine retry strategy, failover candidates, and root cause. Supports multiple providers (OpenAI, Anthropic, Google, local models).

Search and retrieve content from Reddit. Get posts, comments, subreddit info, and user profiles via the public JSON API. Use when user mentions Reddit, a subreddit, or r/ links.

Prompt template for digesting raw external research findings into actionable insights. Extracted from gptel-auto-workflow-strategic.el.

Defines tool permission profiles for programmatic agent execution. Controls which tools an agent can use based on execution mode and project type.

SEO & GEO (Generative Engine Optimization) for websites. Analyze keywords, generate schema markup, optimize for AI search engines (ChatGPT, Perplexity, Gemini, Copilot, Claude) and traditional search (Google, Bing). Use when user wants to improve search visibility, search optimization, search ranking, AI visibility, ChatGPT ranking, Google AI Overview, indexing, JSON-LD, meta tags, or keyword research.

Generates new Emacs Lisp prompt-building strategies for the auto-workflow meta-harness. Provides the complete prompt template, output format, and constraints for strategy generation.

Prompt templates for AI tools (Read, Write, Bash, Edit, etc.). Each tool has a specific prompt that guides the AI on how to use it correctly. Prompts live in assistant/prompts/tools/ and are loaded by nucleus-prompts.el.

Writing/generating Clojure code with REPL-first methodology. Use when Clojure REPL tools available.

Propose meta-level experiment harnesses for self-improvement. Generates new benchmark strategies, validation rules, and experimental frameworks.

Scrape and submit code improvement opportunities from external request platforms. Use when connecting to bounty/task platforms for automated code fixes.

Interactive Emacs Lisp debugging via REPL inspection instead of println/logging. Uses edebug, debug-on-error, message-based tracing, and emacsclient eval for inspecting state without modifying source code.

Systematic Emacs Lisp API discovery before implementation. Uses describe-function, describe-variable, apropos, find-function, and find-library to understand APIs before writing code. Redirects from trial-and-error to systematic discovery.

Writing/generating Emacs Lisp code with dangerous pattern awareness. Use when editing .el files or implementing features in Emacs packages.

Design improvement for Emacs Lisp by separating mechanism from policy. Identifies coupled concerns (what vs how), suggests extraction patterns (defcustom for policy, defun for mechanism), and guides toward more testable, composable code. Based on Arne Brasseur's mechanism-vs-policy.

Structural Emacs Lisp code replacement based on S-expression equivalence. Use instead of naive text matching when Edit/ApplyPatch fails due to formatting differences (whitespace, indentation, line breaks). Compares code structure via `read` + `equal`, preserving original formatting.

Content-addressed line editing using hashline tags. Use when editing files where exact text reproduction is unreliable. Hashlines provide stable anchors: "42:a3|content" → edit by referencing "42:a3".

Meta-skill for validating skills through controlled A/B experiments. Measures whether a skill actually changes AI behavior (not just provides information). Gates skill evolution on measurable improvement. Use when creating, modifying, or evolving any skill.

Template skill demonstrating best practices. Use this as starting point for new skills. Modify name, description, and content for your use case.