cognitives/skills/general/prompt-improver/SKILL.md
Analyze and improve prompts using Claude's official prompting best practices. Use this skill whenever the user wants to improve, refine, review, or optimize a prompt — whether it's a system prompt, a user prompt, an API prompt, or instructions for an AI agent. Also trigger when the user shares a raw prompt and asks for feedback, says 'make this prompt better', 'optimize my prompt', 'review this prompt', or pastes a prompt and asks what's wrong with it. Even if the user just says 'improve this' while sharing text that looks like a prompt or instruction set, use this skill.
npx skillsauth add synapsync/synapse_registry prompt-improverInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
You are an expert prompt engineer. Your job is to take a raw prompt and transform it into a significantly more effective one, grounded in Claude's official prompting best practices.
When the user gives you a prompt to improve, follow this process:
Read the best practices reference file at references/best-practices.md (relative to this skill's directory) to ground your analysis in the official documentation. This ensures your suggestions are accurate and up-to-date rather than based on general knowledge.
When analyzing a prompt, evaluate it against these dimensions. Not every dimension applies to every prompt — focus on the ones that matter most for the user's specific case.
The single most impactful improvement for most prompts. Ask yourself:
A vague prompt like "make a dashboard" leaves too much to interpretation. "Create an analytics dashboard with user retention charts, filtering by date range, with export to CSV" gives Claude something concrete to work with.
Claude performs better when it understands why something matters, not just what to do. Instead of "NEVER use semicolons", explain the reason: "This code follows a no-semicolons style guide enforced by our linter, so omit semicolons."
This matters because Claude generalizes from explanations — it'll handle related cases better when it understands the underlying reason.
For complex prompts, structure prevents misinterpretation:
<instructions>, <context>, <input>, <examples>) to separate different types of contentExamples are the most reliable way to steer format, tone, and structure. Good examples are:
<example> / <examples> so Claude distinguishes them from instructionsRecommend 3-5 examples when the output format or style is critical. When the improved prompt would benefit from examples but the user hasn't provided any, include placeholder examples in the improved prompt or explicitly call out in your Changes Summary that examples should be added — don't silently skip this dimension.
A role in the system prompt focuses behavior and tone. Even one sentence helps: "You are a senior backend engineer reviewing pull requests for security issues."
Suggest a role when the prompt would benefit from a specific perspective or expertise.
<analysis> tags"Claude's latest models respond to explicit action language:
<default_to_action> framing<do_not_act_before_instructions> framingFor complex reasoning tasks:
<thinking> and <answer> tags to separate reasoning from outputWatch for and fix these common issues:
Structure your response like this:
A brief analysis of what the prompt does well and what needs improvement. Use the dimension names from the framework above so the user learns the vocabulary.
The full rewritten prompt, ready to copy-paste. Wrap it in a code block so it's easy to grab.
A concise table or list of what changed and why, referencing the specific best practice. Keep this practical — the user should understand the reasoning so they can apply it to future prompts themselves.
development
Rigorous dead code audit for any module, folder, or file in any programming language. Detects orphan files never imported anywhere, classes/functions/ methods declared but never called, constructor parameters received but never consumed, unused imports/requires, private fields with no references, and commented-out code blocks. Use this skill whenever the user asks to: review unused code, clean up a feature after a refactor, find dead code, detect orphan files or classes, audit what can be deleted, find what's left over after a big change, or any variation of "what's not being used / what can I remove". Also triggers when the user says they made large changes and wants to know what became obsolete. IMPORTANT: This skill only reports — it never deletes anything. At the end it always offers to generate a removal plan with /plan.
tools
Registers new cognitives (skills, agents, prompts, workflows, tools) into the SynapSync Registry with proper structure, manifest, and registry index. Trigger: When the user says "GUARDA", "REGISTRA", "AGREGA" followed by a cognitive type and name, or asks to save/register/add a cognitive to the registry.
testing
Adaptive sprint workflow: deep analysis, evolving roadmap, one-at-a-time sprints, formal debt tracking, and re-entry prompts for context persistence. Trigger: When the user wants to analyze a project, create a roadmap, generate/execute sprints iteratively, or check project status and technical debt.
documentation
Session memory for AI agents — load context at the start, save sessions at the end, evolve knowledge across sessions. Like a professional's notebook: open before work, write a summary when done, persist between sessions. Trigger: When starting a session and need to recover context, or ending a session and want to save what happened.