Chalk Labs
Need help deploying teach?
Chalk Labs sets up agents like this inside real teams: connected to your tools, tested on your data, with a human in the loop where it matters.
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/dotlas-teach
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
Get the weekly diff
The weekly diff: one email a week with new agent tools, notable updates and grade changes, plus security alerts for tools you follow. Double opt-in, one-click unsubscribe.
About
Reviews
Write a reviewNo reviews yet. Installed it? Reviews come from verified installers only. How reviews work
Permissions: not checked yet.
What's inside
- Skill name
teach- Description
- Teach the user a new skill or concept, within this workspace.
- License
- MIT
- Instructions
- 215 lines in SKILL.md · view on GitHub
Files (6)
- GLOSSARY-FORMAT.md
- LEARNING-RECORD-FORMAT.md
- MISSION-FORMAT.md
- RESOURCES-FORMAT.md
- SKILL.md
- openai.yaml · agents/
SKILL.md (first lines)
The user has asked you to teach them something. This is a stateful request - they intend to learn the topic over multiple sessions. ## Teaching Workspace 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](./MISSION-FORMAT.md). - `./reference/*.html`: A directory of reference materials. These are the compressed learnings from the lessons - cheat sheets, reference algorithms, syntax, yoga poses, glossaries. They are the raw units of learning. They should be beautiful documents which print out well, and are designed for quick reference. - `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](./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](./LEARNING-RECORD-FORMAT.md). - `./lessons/*.html`: A directory of lessons. A **lesson** is a single, self-contained HTML output that teaches one tightly-scoped thing tied to the mission. This is the primary unit of teaching in this workspace. - `./assets/*`: Reusable **components** shared across lessons. See [Assets](#assets). - `NOTES.md`: A scratchpad for you to jot down user preferences, or working notes. ## Philosophy To learn at a deep level, the user needs three things: - **Knowledge**, captured from high-quality, high-trust resources - **Skills**, acquired through highly-relevant interactive lessons devised by you, based on the knowledge - **Wisdom**, which comes from interacting with other learners and practitioners 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. ### Fluency vs Storage Strength You should be careful to split between two types of learning: - **Fluency strength**: in-the-moment retrieval of knowledge - **Storage strength**: long-term retention of knowledge Fluency can give the user an illusory sense of mastery, but storage strength is the real goal. Try to design lessons which build long-term retention by desirable difficulty: - Using retrieval practice (recall from memory) - Spacing (distributing practice over time) - Interleaving (mixing up different but related topics in practice - for skills practice only) ## Lessons A lesson is the main thing you produce — the unit in which knowledge and skills reach the user. Each lesson is one self-contained HTML file, saved to `./lessons/` and titled `0001-<dash-case-name>.html` where the number increments each time. A lesson should be **beautiful** — clean, readable typography and layout — since the user will return to these later to review. Think Tufte. The lesson should be short, and completable very quickly. Learners’ working memory is very small, and we need to stay within it. But each lesson should give the user a single tangible win that they can build on. It should be directly tied to the mission, and should be in the user’s zone of proximal development. If possible, open the lesson file for the user by running a CLI command. Each lesson should link via HTML anchors to other lessons and reference documents. Each lesson should recommend a primary source for the user to read or watch. This should be the most high-quality, high-trust resource you found on the topic. Each lesson should contain a reminder to ask followup questions to the agent. The agent is their teacher, and can assist with anything that’s unclear. ## Assets Lessons are built from reusable **components**, stored in `./assets/`: stylesheets, quiz widgets, simulators, diagram helpers — anything a second lesson could reuse. Reuse is the default, not the exception. Before authoring a lesson, read `./assets/` and build from the components already there. When a lesson needs something new and reusable, write it as a component in `./assets/` and link to it — never inline code a future lesson would duplicate. A shared stylesheet is the first component every workspace earns: every lesson links it, so the lessons look like one consistent course rather than a pile of one-offs. As the workspace grows, so should the component library. ## The Mission Every lesson 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. Lessons will feel too abstract. You will have no way of judging what the user should do next. Missions may change as the user develops more skills and knowledge.
From dotlas/skills, MIT.
More from dotlas/skills
- 0.507score
codebase-integrity
dotlas
Audit the codebase for dependency conflicts, loose types, duplicated patterns, and import boundary violations.
SkillCFreshness: C★ 2Claude Code +9 - 0.507score
tidy-next-codebase
dotlas
Audit a Next.js codebase for stale/dead code and duplication, turn the findings into a fully deterministic cleanup plan, then execute it as a gated…
SkillCFreshness: C★ 2Claude Code +9 - 0.507score
import-skill
dotlas
Import a third-party skill into this owned catalog (dotlas/skills) from a skills.sh URL, a GitHub repo/subdir, or a pasted `npx skills add` command.
SkillCFreshness: C★ 2Claude Code +9 - 0.507score
commit
dotlas
Commit local changes — inspect the working tree, group into atomic conventional commits, and write messages that match the repo's conventions.
SkillCFreshness: C★ 2Claude Code +9 - 0.487score
impeccable
dotlas
Use when the user wants to design, redesign, shape, critique, audit, polish, clarify, distill, harden, optimize, adapt, animate, colorize, extract, or…
SkillCFreshness: C★ 2Claude Code +9 - 0.479score
improve-codebase-architecture
dotlas
Scan a codebase for deepening opportunities, present them as a visual HTML report, then grill through whichever one you pick.
SkillCFreshness: C★ 2Claude Code +9 - 0.477score
grilling
dotlas
Grill the user relentlessly about a plan, decision, or idea.
SkillCFreshness: C★ 2Claude Code +9 - 0.476score
resolving-merge-conflicts
dotlas
Use when you need to resolve an in-progress git merge/rebase conflict.
SkillCFreshness: C★ 2Claude Code +9 - 0.471score
handoff
dotlas
Compact the current conversation into a handoff document for another agent to pick up.
SkillCFreshness: C★ 2Claude Code +9 - 0.350score
vercel-react-best-practices
vercel
React and Next.js performance optimization guidelines from Vercel Engineering.
SkillCFreshness: C★ 2Claude Code +9
Signals
- Stars
- 2
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Jul 23, 2026 (3 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/dotlas/skills
- Website
- www.skills.sh/dotlas/skills
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
More from dotlas
- 0.507score
commit
dotlas
Commit local changes — inspect the working tree, group into atomic conventional commits, and write messages that match the repo's conventions.
SkillCFreshness: C★ 2Claude Code +9 - 0.507score
tidy-next-codebase
dotlas
Audit a Next.js codebase for stale/dead code and duplication, turn the findings into a fully deterministic cleanup plan, then execute it as a gated…
SkillCFreshness: C★ 2Claude Code +9 - 0.507score
import-skill
dotlas
Import a third-party skill into this owned catalog (dotlas/skills) from a skills.sh URL, a GitHub repo/subdir, or a pasted `npx skills add` command.
SkillCFreshness: C★ 2Claude Code +9 - 0.507score
codebase-integrity
dotlas
Audit the codebase for dependency conflicts, loose types, duplicated patterns, and import boundary violations.
SkillCFreshness: C★ 2Claude Code +9 - 0.487score
impeccable
dotlas
Use when the user wants to design, redesign, shape, critique, audit, polish, clarify, distill, harden, optimize, adapt, animate, colorize, extract, or…
SkillCFreshness: C★ 2Claude Code +9 - 0.479score
improve-codebase-architecture
dotlas
Scan a codebase for deepening opportunities, present them as a visual HTML report, then grill through whichever one you pick.
SkillCFreshness: C★ 2Claude Code +9
Done for you
Rather not set it up yourself?
Chalk Labs scopes, builds and runs agents for your team: the task audit, the eval harness, the integrations and the weekly ops report. Fixed scope, priced before we start.
See how we build AI agents →Read the case studies
Chalk Labs is a two-founder AI and growth studio. We build AI agents, CRMs and MVPs for Web3 and AI companies, and have shipped 50+ product and token launches.
- launches shipped
- 50+
- launches shipped
- media placements
- 500+
- media placements
- founders on every account
- 2
- founders on every account
Press we've placed: Forbes · CoinDesk · Decrypt · Cointelegraph · The Block
Rahil Jain, Co-founder, Chalk Labs. Ex-Goldman Sachs engineer. You talk to the person who builds it.
Get teach running in your business
Tell us what you want to automate. A founder replies within one working day with a plan, not a pitch deck.
Cite this page
teach skill. Agent Mart. https://agentmart.dev/skills/dotlas-teach (last updated 2026-10-24)