# Report Publish > Publish a finished report as a page a named set of people can read and ask questions about. Covers gathering recipients and a cap, building the one archive the reading room expects, the size discipline for a large bundle, and the mint-then-upload-then-share procedure. Use when someone wants to hand a finished report to specific people, not for sharing a notebook or a chart in the thread. - Skill: `pymc-labs/report-publish` (Agent Skill) - Install (CLI): `npx skillmds@latest add pymc-labs/report-publish` - Raw SKILL.md: https://api.skillmd.com/api/skills/pymc-labs/report-publish/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Docs & Writing - Author: pymc-labs (https://skillmd.com/u/pymc-labs) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/pymc-labs/report-publish --- # report-publish You are publishing a finished report as a reading room: a page where the people you name can read the report and ask an agent questions about it, grounded in the analysis behind it. ## 1. When to use this Use this when someone has a finished report and wants specific people to be able to read it and ask follow-up questions about the numbers in it. This is not for sharing an interactive notebook or dashboard — that is a different tool. It is not for posting a chart or a table directly in the conversation either. It is specifically for a finished, standalone report that a named audience should be able to sit with and interrogate on their own time. ## 2. What to gather first Before you build anything, gather: - **A short slug.** Lowercase letters, numbers and hyphens only — it becomes part of every recipient's link. - **A human title** for the report. - **One entry per recipient**, each with a name and a label naming who they are (an email, a role, a handle — whatever tells the two of you apart later). - **A dollar cap** for the whole report's questions. This cap covers every recipient's questions together, not one cap each — plan it accordingly. - **Which agent should answer questions.** This defaults to the agent already configured for the workspace; you may name a different one. Either way, the reader gets a variant of that agent with the same voice and knowledge, minus every integration it would otherwise have — a reader-facing agent never reaches into the workspace's chat tools or its own extra connections. ## 3. Build the archive Everything the reading room serves comes from ONE gzip archive. The report PDF sits at its root. The analysis directories sit beside it, at the same level. Every path inside the archive must be relative — no leading slash, no parent-directory reference. An absolute path or a relative path that climbs out of the archive root gets the whole upload refused, so build the archive from inside the directory you're compressing, not by adding files with their full path baked in. The layout, as a tree: ``` report.pdf the report itself, at the archive root README.md how the answering agent should answer, and how to rebuild the report manifest.json links each figure to its render script, its table, its model and its source data data/ the source data as it was loaded, before any joins prep/ scripts that join and clean data/ into the tables below models/ the fitted model(s): scripts, summaries, diagnostics, full trace tables/ the per-figure and headline numbers, already computed figures/ one render script (and rendered image) per figure typst/ the report's source — edit and rebuild from here ``` Write a real `README.md` and a real `manifest.json`, not placeholders — these two files are what make the answers good, because the answering agent reads them first, before anything else in the bundle. A `manifest.json` a reader never sees but an answering agent needs is the difference between "why is this number what it is" getting a sourced answer and getting a guess. Give the README the answering persona and any report-specific notes; give the manifest a real chain from each figure to its table, its model and its source data, plus a run order for the scripts. ## 4. Size discipline There is a hard cap on the archive's size. If your bundle does not fit under it, drop things in this order: 1. **Raw posterior draws and trace files** — the biggest single space cost, and the least likely to be asked for directly. 2. **Intermediate caches** — anything a script recomputes cheaply and does not need to ship pre-built. 3. **Anything regenerable from a script that is already in the archive** — if `models/fit.py` produces it and `models/fit.py` is in the bundle, the output doesn't also need to be. Keep the tables, the manifest and the source data no matter what — those are exactly what a "why is this number what it is" question resolves against, and dropping them breaks the whole point of the reading room. If the archive still does not fit after dropping the three categories above, subset the source data (a sample, a recent window, an aggregate) rather than dropping the tables or the manifest. ## 5. The procedure 1. Call the publish tool with the slug, title, recipients, cap and agent choice from step 2. It returns a one-time upload URL and one link per recipient. 2. PUT the archive to the upload URL with a single curl command: ```bash curl -sS -X PUT --data-binary @bundle.tar.gz "" ``` Never put the archive's contents through a tool argument — a tool call truncates large content, and a multi-megabyte archive will not survive the round trip. The upload URL is what carries the bytes; the tool call only ever carries small values. 3. Relay each person their own link. The links are per-recipient and must not be swapped or shared — sending Ada's link to Ben means Ben reads Ada's report under Ada's name, and sending one link to everyone collapses the isolation the reading room is built around entirely. ## 6. Afterwards Each recipient gets their own conversation with the answering agent, and the cap you set covers all of them together, not one each. A recipient can ask the answering agent to revise the report; the agent rebuilds it and uploads the new version, which replaces the old one in every recipient's viewer. Deleting the report revokes every link at once and cannot be undone — a recipient who needs access again after that would need a fresh publish and a new link. ## 7. What not to do - Do not put client data in a repository. The archive replaces that path entirely — there is no reason for a client's data to live anywhere else. - Do not reuse one link for several people. Every recipient gets their own. - Do not publish without a cap. An uncapped report has no limit on what reader questions can spend. - Do not paste the archive into a tool argument. Mint the upload URL, then curl the file to it.