بنقرة واحدة
teach
Teach the user a new skill or concept, within this workspace.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
القائمة
Teach the user a new skill or concept, within this workspace.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Grilling session that challenges your plan against the existing domain model, sharpens terminology, and updates documentation (CONTEXT.md, ADRs) inline as decisions crystallise. Use when user wants to stress-test a plan against their project's language and documented decisions.
Find deepening opportunities in a codebase, informed by the domain language in CONTEXT.md and the decisions in docs/adr/. Use when the user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more testable and AI-navigable.
Review the changes since a fixed point (commit, branch, tag, or merge-base) along two axes — Standards (does the code follow this repo's documented coding standards?) and Spec (does the code match what the originating issue/PRD asked for?). Runs both reviews in parallel sub-agents and reports them side by side. Use when the user wants to review a branch, a PR, work-in-progress changes, or asks to "review since X".
When the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," or "hypothesis." For tracking implementation, see analytics-tracking.
When the user wants to generate, iterate, or scale ad creative — headlines, descriptions, primary text, or full ad variations — for any paid advertising platform. Also use when the user mentions 'ad copy variations,' 'ad creative,' 'generate headlines,' 'RSA headlines,' 'bulk ad copy,' 'ad iterations,' 'creative testing,' or 'ad performance optimization.' This skill covers generating ad creative at scale, iterating based on performance data, and enforcing platform character limits. For campaign strategy and targeting, see paid-ads. For landing page copy, see copywriting.
When the user wants to optimize content for AI search engines, get cited by LLMs, or appear in AI-generated answers. Also use when the user mentions 'AI SEO,' 'AEO,' 'GEO,' 'LLMO,' 'answer engine optimization,' 'generative engine optimization,' 'LLM optimization,' 'AI Overviews,' 'optimize for ChatGPT,' 'optimize for Perplexity,' 'AI citations,' 'AI visibility,' or 'zero-click search.' This skill covers content optimization for AI answer engines, monitoring AI visibility, and getting cited as a source. For traditional technical and on-page SEO audits, see seo-audit. For structured data implementation, see schema-markup.
| name | teach |
| description | Teach the user a new skill or concept, within this workspace. |
| disable-model-invocation | true |
| argument-hint | What would you like to learn about? |
The user has asked you to teach them something. This is a stateful request - they intend to learn the topic over multiple sessions.
Treat the current directory as a teaching workspace. The state of their learning is captured in this directory in several files:
MISSION.md: A document capturing the reason the user is interested in the topic. This should be used to ground all teaching. Use the format in MISSION-FORMAT.md.GLOSSARY.md: A glossary of terminology related to the topic. All workspace files should adhere to this terminology. Use the format in GLOSSARY-FORMAT.md.RESOURCES.md: A list of resources which can be explored to ground your teaching in contextual knowledge, or to acquire knowledge and wisdom. Use the format in RESOURCES-FORMAT.md../learning-records/*.md: A directory of learning records, which capture what the user has learned. These are loosely equivalent to architectural decision records in software development - they capture non-obvious lessons and key insights that may need to be revised later, or drive future sessions. These should be used to calculate the zone of proximal development. They are titled 0001-<dash-case-name>.md, where the number increments each time. Use the format in LEARNING-RECORD-FORMAT.md.To learn at a deep level, the user needs three things:
Before the RESOURCES.md is well-populated, your focus should be to find high-quality resources which will help the user acquire knowledge. Never trust your parametric knowledge.
Some topics may require more skills than knowledge. Learning more about theoretical physics might be more knowledge-based. For yoga, more skills-based.
Every teaching session should be tied into the mission - the reason that the user is interested in learning about the topic.
If the user is unclear about the mission, or the MISSION.md is not populated, your first job should be to question the user on why they want to learn this.
Failing to understand the mission will mean knowledge acquisition is not grounded in real-world goals. Exercises will feel too abstract. You will have no way of judging what the user should do next.
The user should always feel as if they are being challenged 'just enough'. The scope of the topic being taught should feel extremely tight, should be directly tied into their mission.
The user may specify an exact thing they want to learn. If they don't, figure out their zone of proximal development by:
learning-recordsA user may tell you that they already know about that topic. If so, record it in their learning-records.
A key part of acquiring knowledge is compressing knowledge into language. Once a term is known and understood, it can be used and combined in new ways to make more complex terms easier to understand.
Building the glossary should be done once you feel confident that the user understands the term. Glossaries should use a strict format, and use as concise a definition as possible.
Knowledge and skills usually need to be taught as a 1-2 punch. You teach the knowledge first, then get the user to practice the skills via exercises.
Knowledge should first be gathered from trusted resources, then taught to the user via HTML explainers. These explainers should be beautiful, adhere to the glossary, and be saved to the local file system where they can be reviewed later.
Explainers should be littered with citations - links to external resources to back up any claim made.
Explainers should be as interactive as possible, with "try this" callouts to let the user try the knowledge.
You should make opening the HTML explainer as easy as possible for the user, ideally with a CLI command they can run.
Once the user has read the knowledge, allow them to ask questions about it. Answer their questions directly, and amend the explainer if needed (or produce another one).
At this point, you can amend the glossary if it appears clear they understand a term.
Skills should be taught through interactive exercises. There are several tools at your disposal:
Each exercise should be based on a feedback loop, where the user receives feedback on their performance. This feedback loop should be as tight as possible, giving feedback immediately.
Wisdom comes from true real-world interaction - testing your skills outside the learning environment.
When the user asks a question that appears to require wisdom, your default posture should be to attempt to answer - but to ultimately delegate to a community.
A community is a place (online or offline) where the user can test their skills in the real world. This might be a forum, a subreddit, a real-world class (budget permitting) or a local interest group.
You should attempt to find high-reputation communities the user can join. If the user expresses a preference that they don't want to join a community, respect it.