| name | start-learn |
| description | Entry point to learn-skills. Asks one question — what to learn — then auto-detects Scenario A (understand existing code or topic) or Scenario B (learn from scratch) and acts accordingly. Scenario A: selects and runs the right dialogic skill, declaring the choice. Scenario B: creates a teaching workspace with CURRICULUM.md, HTML lessons, glossary, and resources. Use when the user says "help me start", "I want to learn", "where do I begin", "start-learn", or when invoked without a specific methodology in mind. |
Start Learn
Speak to the user in their language; these instructions are in English for the agent.
Single-question entry point. One ask — then act. No interview, no menu.
Quick start
User: /start-learn
Agent: What do you want to learn?
(Give me a file/PR to understand, or a topic you want to learn from scratch.)
The agent reads the answer, infers Scenario A or B, declares the assumption, and proceeds. If wrong, the user corrects in one word.
Auto-detection
Read FLOWS.md for the full detection logic, skill-mapping table (Scenario A), and step-by-step workspace protocol (Scenario B).
Summary:
- Scenario A — user mentions existing code, file, PR, or wants to deepen a topic with a codebase already in mind → select the right dialogic skill, declare the choice + a one-line reason, and run it. The user can redirect at any time.
- Scenario B — user names a topic they want to learn from zero → create a teaching workspace, generate CURRICULUM.md, produce HTML lessons on demand.
Always declare the assumption:
"Starting from the hypothesis that you're learning X from scratch — correct me if wrong."
Workspace (Scenario B only)
See WORKSPACE.md for the full directory structure, file purposes, and workspace detection rule.
The workspace path is chosen by the user in plain text (no tool). Access in subsequent sessions: cd into the folder. Detection signal for all workspace-aware skills: presence of CURRICULUM.md in cwd.
Baseline handoff
In both scenarios, state the inferred level explicitly at the end:
"I'll carry this baseline (estimated: X) to /assess when you run it later — progress will be measurable."
Anti-patterns
- Don't ask more than one question upfront — the whole point is zero boilerplate; if you need more context, ask after you've started, not before.
- Don't decide silently — always declare the assumed scenario before acting; one wrong assumption costs one correction, not a lost session.
- Don't dispatch silently — auto-selecting the methodology is the behaviour (ADR 0004), but always declare the choice + a one-line reason before running, because an undeclared switch hides the meta-decision the user has the right to override.
- Don't make the user choose between named methodologies — the audience doesn't know the skills by name; offering "explain-and-check or ask-me-questions?" asks for an expert choice they can't make. Pick one, run it, let the user redirect.
- Don't auto-dispatch in Scenario B — selection-and-run applies to Scenario A methodologies; Scenario B still ends with "say 'next lesson' when ready" so the user paces content delivery.
- Don't generate all lessons upfront — one lesson at a time, on demand, so each can adapt to progress recorded in
learning-records/.
- Don't invent resource URLs — populate RESOURCES.md via web search; if unavailable, name sources without URLs and flag "verify the link."
Governing principles (this skill satisfies all five)
- Agent withholds — withholds content (lessons deliver only when requested; sessions make the student speak first). Selects the methodology on the user's behalf (ADR 0004) — meta-choice, not content.
- Student speaks first — all choices (workspace path, curriculum approval, lesson request) originate from the user.
- Artefact is the judge — Scenario A: reads real code/docs before proposing. Scenario B: every lesson cites a primary source.
- Source fidelity — web search before content; no parametric knowledge asserted without citation. Policy: ../../docs/sources.md.
- Exit is a transfer test — Scenario A: user's confirmation of the next skill is the exit. Scenario B: each lesson ends with a practice section the user completes.
