# Lambda Map > Build or incrementally update a λambda course map — the marks-bound routing table that makes /lambda sessions target assessed problems. Use as "/lambda-map " when materials first arrive and again each week as new sheets drop. Indexes question files (solutions are consulted as grounding only, never copied) and writes courses//map.md into the vault. - Skill: `abaj8494/lambda-map` (Agent Skill) - Install (CLI): `npx skillmds@latest add abaj8494/lambda-map` - Raw SKILL.md: https://api.skillmd.com/api/skills/abaj8494/lambda-map/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: abaj8494 (https://skillmd.com/u/abaj8494) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/abaj8494/lambda-map --- # λambda map builder (schema: 1) The map is what separates λambda from a generic AI tutor: every MCQ a session asks traces to a real assessed problem and the marks it carries. This skill builds that map from the learner's **own** course materials — the map derives from their institution's copyrighted content, so it lives in their private vault and is never redistributed. ## Usage `/lambda-map [more paths...]` Paths may be folders or files: lecture PDFs, tutorial/problem-set PDFs, past exam papers, quiz exports, notebooks, **lecture transcripts** (`.txt`/`.md`/`.vtt` dumps of what was actually said). Transcripts are an emphasis signal, not a question source: past papers show what an earlier offering examined, but the transcript shows what *this* offering's lecturer stressed, skipped, or hinted at ("this will be on the exam", "we won't cover the proof"). When transcripts are present, weight the map's marks-bound routing toward the current offering's emphasis and note explicit verbal exam hints in the relevant map rows. File transcripts in the `## Sources` ledger like any other input. ## Sparse sources (school-shaped courses) Not every course arrives university-shaped. A school student may have only a scope & sequence, a textbook, and a couple of practice tests — no outline, no tutorial sets, no marks schedule. The map builds anyway, by substitution: - **Scope & sequence / syllabus → units.** Its rows become the map's `##` sections; the textbook's chapter structure is the fallback when even that is missing. - **Textbook exercises → drills.** Chapter exercise sets fill the Drill column (`Ch 11 Ex 11C Q4–9` is a perfectly good drill pointer). - **Practice tests / past papers → assessed targets.** However few; a single practice test still anchors marks-bound ordering. - **No marks data at all?** Leave the marks empty and order sections by the scope & sequence — routing degrades to curriculum order, and the map upgrades in place the day a practice paper arrives (weekly-update path below). Never fabricate marks or assessment weights. A sparse map that says less is better than a confident map that lies. ## Procedure 1. **Inventory and classify.** List candidate files; classify each as questions or solutions (`*solution*`, `*answers*`, `*marking*`, keys). The learner's own submitted work (assignments, graded quizzes, past answers) may be indexed for ROUTING — what the course asks, where the marks sit — but is NEVER mastery evidence: it may be AI-assisted or otherwise not the learner's own, and the map must not seed or imply any mastery state from it. Beliefs about the learner update only on what λ observes in its own sessions. Solutions are **grounding, not content**: read them to confirm marks, intended method, and concept labels — never copy solution text, steps, or final answers into the map. The map must read cleanly as if built from questions alone. 2. **Index per assessment surface.** For each question file, extract a per-question index: question number, marks (if stated), one line on what it asks (the task, not the topic label), and the concept(s) it exercises. For large collections, fan out subagents per file/folder and merge. 2b. **Extract the notation register.** While indexing, record the course's own symbol conventions: what each recurring symbol denotes *in this course's materials* (e.g. which letter the course uses for a parameter, an ordering, a message, a mean), and any known clash with common textbook conventions. The map gains a `## Notation` section: ```markdown ## Notation (course register — teach and quiz in THIS) | Symbol | Course meaning | Clash warning | |---|---|---| | $\mu$ | Poisson mean (this course) | many texts write $\lambda$ | ``` Only record symbols actually observed in the materials — never infer a convention from a textbook. Weekly updates extend this table as new sheets introduce symbols. 3. **Reverse-index by concept.** For each lecture/chapter-level concept, list every drill (tutorial/problem-set question) and every assessed target (exam/quiz question with marks) that exercises it. 4. **Weight it.** Open the map with a `> [!important] Marks at stake` callout: which concepts carry the most assessed marks, which appear in the most recent / most format-relevant papers, and any concept that appears in exactly one exemplar (flag it — thin coverage means the drills are precious). 5. **Write `courses//map.md`** in the vault. Open it with a `skills:` frontmatter block declaring the skills a `/lambda` session on this course must load — this is the course's composition manifest, read in the session protocol's Step 0. Every course gets `lambda-core` (default) and, if quantitative, `lambda-notation` (the `$`/LaTeX rules) and `lambda-draw` (figures); add `lambda-math` for arithmetic-heavy courses (simplest-form discipline), and the field skill for the subject as it arrives (`lambda-science`, `lambda-english`, `lambda-humanities`): ```markdown --- skills: [lambda-core, lambda-notation, lambda-draw, lambda-math] --- # routing map (schema: 1) Built from question files only; no solutions opened. Source roots: > [!important] Marks at stake > ## | Concept | Drill | Assessed target | |---|---|---| | | | | ``` End the map with a `## Sources` ledger: one line per indexed file with the date it was indexed. This is what makes weekly updates cheap. If the syllabus states prerequisites, open the map with an `assumes: , ` line naming substrate atoms (SPEC "The substrate") — declared dependencies, NOT map rows: they carry no drills or marks, and sessions re-verify them by fast lane at need. Never probe them at map-build time; the register also grows lazily from descent events, so an empty or absent line is fine. 6. Report: units mapped, questions indexed, the top-3 marks concentrations, and any blind spots (concepts with no drill or no assessed exemplar). ## Weekly updates — courses arrive incrementally A real student does not hold the full term's corpus on day one; tutorial sheets and solutions land week by week. Design for that: - **Re-running the command is the update mechanism.** If `courses//map.md` already exists, read it and its `## Sources` ledger first; index only files not in the ledger; append the new questions to the right unit tables; recompute the marks-at-stake callout; add the new files to the ledger. Never rebuild from scratch — existing rows and their wording stay put. - **Thin early-term coverage degrades gracefully.** When few or no assessed surfaces exist yet, weight rows by lecture emphasis instead of marks and tag them `no assessed exemplar yet` — sessions then anchor to drills alone, and the tags disappear as past papers and quizzes enter the ledger. - In the update report, call out which previous blind spots the new week's material just covered. ## Conventions - Marks in brackets everywhere: `Q3 [5]`. - One-line descriptions state the *task* ("derive the conditional of a bivariate Gaussian given evidence"), not a topic label. - Callout hygiene: every callout line, including the `[!type]` line, starts with `> `. - If the vault is a git repo, commit: `λ-map: — questions`.