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Bring your own model.

Caladia doesn’t ship with an embedded LLM, API key, or vendor account. The repo ships a portable authoring skill — a markdown spec any sufficiently capable LLM can follow to produce a valid .cala. Paste the skill into Claude, GPT, Gemini, or a local model; give it the source material; get JSON back.

1. The authoring skill

The skill is a roughly 400-line markdown document that specifies:

  • What Caladia is, in one paragraph.
  • The required JSON output format — literal field names, value constraints, version pins.
  • A schema reference: every top-level array, every node type, every distribution shape.
  • Conversion rules: how to map common source structures (Gantt rows, slide bullets, MSP XML elements) into Caladia’s graph.
  • Output rules: JSON only, no prose, no markdown fences.

The skill lives at docs/caladia-authoring-skill.md in the Caladia source. Copy, fork, or version it with your projects. MIT licensed.

2. The flow

Three steps, run in your model of choice:

  1. Paste the skill as the system prompt or the first user message.
  2. Paste the source artifact — an MS Project XML export, a copy-paste of a Gantt spreadsheet, the text content of a slide deck, or a plain-text description of the workflow.
  3. Get JSON back. Save it as plan.cala, drag onto Caladia, eyeball the imported graph.
source → LLM + skill → .cala → Caladia source .mpp / .xlsx / .pptx LLM + skill .cala JSON Caladia canvas
The skill is the link between an LLM and Caladia. Drop in any model on the left; the right side is unchanged.

3. What you can import

Four source shapes the skill handles:

  • MS Project XML — the export format from MSP and most enterprise PM tools that can read MSP. The skill maps tasks to activities, summary tasks to sub-systems, predecessor links to FS/SS/FF/SF edges with lags.
  • Gantt-shaped spreadsheets — an Excel sheet with task names, durations, dependencies, optionally resources. Copy-paste the cells or save as .csv and paste. The skill handles common conventions but works best when the columns are labelled.
  • Slide decks — PowerPoint or Google Slides exported as text. The skill reads numbered process steps, swimlane diagrams, decision-flow slides. Works well for documented processes; less well for free-form architecture decks.
  • Plain text descriptions — “Build takes 3 days. Test takes 2 days. Both feed into Review which has a 70% pass rate.” The skill maps this directly. Good for prototyping a plan you haven’t formalised yet.

4. Quality — what to check

An LLM with the skill produces valid JSON most of the time. Correct JSON is the harder question. Two things to eyeball after every import:

  • Dependencies. The biggest failure mode is missed edges. If the source said “Build and Test feed Review” and only one dep landed, the schedule is wrong. Spot-check by tracing a few critical paths on the canvas.
  • Distributions. A source that gives single-point durations will produce single-point distributions (or no distribution at all). The skill won’t invent uncertainty. After import, decide which activities deserve a triangular or PERT-beta and add those manually — see Distributions.

Treat the import as a starting point and refine in the canvas.