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Take the next task on the to-do list and work on it. Confirm that all tests pass. Review your work for sanity (e.g. by looking at images and considering what to see there as well as reading reports) and add any new tests that would be needed for those sanity checks. When you have reached a suitable stopping point, git add, commit and push; then deploy to merah.

ID

019cf659-4cf3-7480-9234-57386edda11c

Created

2026-03-16T11:12:53.501Z

Updated

2026-03-16T11:44:09+00:00

CWD

/Users/gregb/Documents/devel/aao-mechd

Source

exec

Transcript Source

/Users/gregb/.codex/sessions/2026/03/16/rollout-2026-03-16T22-12-53-019cf659-4cf3-7480-9234-57386edda11c.jsonl

Codex Home

/Users/gregb/.codex

<permissions instructions>
Filesystem sandboxing defines which files can be read or written. `sandbox_mode` is `danger-full-access`: No filesystem sandboxing - all commands are permitted. Network access is enabled.
Approval policy is currently never. Do not provide the `sandbox_permissions` for any reason, commands will be rejected.
</permissions instructions>

# AGENTS.md instructions for /Users/gregb/Documents/devel/aao-mechd

<INSTRUCTIONS>
## Skills
A skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and file path so you can open the source for full instructions when using a specific skill.
### Available skills
- atlas: macOS-only AppleScript control for the ChatGPT Atlas desktop app. Use only when the user explicitly asks to control Atlas tabs/bookmarks/history on macOS and the "ChatGPT Atlas" app is installed; do not trigger for general browser tasks or non-macOS environments. (file: /Users/gregb/.codex/skills/atlas/SKILL.md)
- doc: Use when the task involves reading, creating, or editing `.docx` documents, especially when formatting or layout fidelity matters; prefer `python-docx` plus the bundled `scripts/render_docx.py` for visual checks. (file: /Users/gregb/.codex/skills/doc/SKILL.md)
- gh-address-comments: Help address review/issue comments on the open GitHub PR for the current branch using gh CLI; verify gh auth first and prompt the user to authenticate if not logged in. (file: /Users/gregb/.codex/skills/gh-address-comments/SKILL.md)
- gh-fix-ci: Use when a user asks to debug or fix failing GitHub PR checks that run in GitHub Actions; use `gh` to inspect checks and logs, summarize failure context, draft a fix plan, and implement only after explicit approval. Treat external providers (for example Buildkite) as out of scope and report only the details URL. (file: /Users/gregb/.codex/skills/gh-fix-ci/SKILL.md)
- jupyter-notebook: Use when the user asks to create, scaffold, or edit Jupyter notebooks (`.ipynb`) for experiments, explorations, or tutorials; prefer the bundled templates and run the helper script `new_notebook.py` to generate a clean starting notebook. (file: /Users/gregb/.codex/skills/jupyter-notebook/SKILL.md)
- openai-docs: Use when the user asks how to build with OpenAI products or APIs and needs up-to-date official documentation with citations (for example: Codex, Responses API, Chat Completions, Apps SDK, Agents SDK, Realtime, model capabilities or limits); prioritize OpenAI docs MCP tools and restrict any fallback browsing to official OpenAI domains. (file: /Users/gregb/.codex/skills/openai-docs/SKILL.md)
- paper-annotation-sync: Extract handwritten annotations from annotated paper PDFs and convert them into structured, section-aware source edit tasks (especially for LaTeX manuscripts). Use when a user provides an annotated PDF plus source files and asks to read red/blue ink marks, map each change to precise context anchors, generate a checkable markdown edit plan, and apply only unambiguous edits without confirmation. (file: /Users/gregb/.codex/skills/paper-annotation-sync/SKILL.md)
- pdf: Use when tasks involve reading, creating, or reviewing PDF files where rendering and layout matter; prefer visual checks by rendering pages (Poppler) and use Python tools such as `reportlab`, `pdfplumber`, and `pypdf` for generation and extraction. (file: /Users/gregb/.codex/skills/pdf/SKILL.md)
- playwright: Use when the task requires automating a real browser from the terminal (navigation, form filling, snapshots, screenshots, data extraction, UI-flow debugging) via `playwright-cli` or the bundled wrapper script. (file: /Users/gregb/.codex/skills/playwright/SKILL.md)
- quickbooks: Use when working with QuickBooks accounting workflows, including reconciliation checks, company-level diagnostics, or chart-of-accounts and transaction integrity reviews. (file: /Users/gregb/.codex/skills/quickbooks/SKILL.md)
- screenshot: Use when the user explicitly asks for a desktop or system screenshot (full screen, specific app or window, or a pixel region), or when tool-specific capture capabilities are unavailable and an OS-level capture is needed. (file: /Users/gregb/.codex/skills/screenshot/SKILL.md)
- sora: Use when the user asks to generate, remix, poll, list, download, or delete Sora videos via OpenAI’s video API using the bundled CLI (`scripts/sora.py`), including requests like “generate AI video,” “Sora,” “video remix,” “download video/thumbnail/spritesheet,” and batch video generation; requires `OPENAI_API_KEY` and Sora API access. (file: /Users/gregb/.codex/skills/sora/SKILL.md)
- speech: Use when the user asks for text-to-speech narration or voiceover, accessibility reads, audio prompts, or batch speech generation via the OpenAI Audio API; run the bundled CLI (`scripts/text_to_speech.py`) with built-in voices and require `OPENAI_API_KEY` for live calls. Custom voice creation is out of scope. (file: /Users/gregb/.codex/skills/speech/SKILL.md)
- spreadsheet: Use when tasks involve creating, editing, analyzing, or formatting spreadsheets (`.xlsx`, `.csv`, `.tsv`) using Python (`openpyxl`, `pandas`), especially when formulas, references, and formatting need to be preserved and verified. (file: /Users/gregb/.codex/skills/spreadsheet/SKILL.md)
- transcribe: Transcribe audio files to text with optional diarization and known-speaker hints. Use when a user asks to transcribe speech from audio/video, extract text from recordings, or label speakers in interviews or meetings. (file: /Users/gregb/.codex/skills/transcribe/SKILL.md)
- skill-creator: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations. (file: /Users/gregb/.codex/skills/.system/skill-creator/SKILL.md)
- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: /Users/gregb/.codex/skills/.system/skill-installer/SKILL.md)
### How to use skills
- Discovery: The list above is the skills available in this session (name + description + file path). Skill bodies live on disk at the listed paths.
- Trigger rules: If the user names a skill (with `$SkillName` or plain text) OR the task clearly matches a skill's description shown above, you must use that skill for that turn. Multiple mentions mean use them all. Do not carry skills across turns unless re-mentioned.
- Missing/blocked: If a named skill isn't in the list or the path can't be read, say so briefly and continue with the best fallback.
- How to use a skill (progressive disclosure):
  1) After deciding to use a skill, open its `SKILL.md`. Read only enough to follow the workflow.
  2) When `SKILL.md` references relative paths (e.g., `scripts/foo.py`), resolve them relative to the skill directory listed above first, and only consider other paths if needed.
  3) If `SKILL.md` points to extra folders such as `references/`, load only the specific files needed for the request; don't bulk-load everything.
  4) If `scripts/` exist, prefer running or patching them instead of retyping large code blocks.
  5) If `assets/` or templates exist, reuse them instead of recreating from scratch.
- Coordination and sequencing:
  - If multiple skills apply, choose the minimal set that covers the request and state the order you'll use them.
  - Announce which skill(s) you're using and why (one short line). If you skip an obvious skill, say why.
- Context hygiene:
  - Keep context small: summarize long sections instead of pasting them; only load extra files when needed.
  - Avoid deep reference-chasing: prefer opening only files directly linked from `SKILL.md` unless you're blocked.
  - When variants exist (frameworks, providers, domains), pick only the relevant reference file(s) and note that choice.
- Safety and fallback: If a skill can't be applied cleanly (missing files, unclear instructions), state the issue, pick the next-best approach, and continue.
</INSTRUCTIONS>

<environment_context>
  <cwd>/Users/gregb/Documents/devel/aao-mechd</cwd>
  <shell>zsh</shell>
  <current_date>2026-03-16</current_date>
  <timezone>Australia/Sydney</timezone>
</environment_context>

Take the next task on the to-do list and work on it. Confirm that all tests pass. Review your work for sanity (e.g. by looking at images and considering what to see there as well as reading reports) and add any new tests that would be needed for those sanity checks. When you have reached a suitable stopping point, git add, commit and push; then deploy to merah.

I’m going to inspect the repo to find the to-do list item, understand the current branch state, and identify the relevant test/deploy workflow before I touch code.

This repo has a project-specific `telescope-design` skill, so I’m loading that guidance before choosing the backlog item and implementation path.

I’ve got the repo workflow now. Next I’m reading the live backlog and the surrounding design files so we can pick the actual first open item, not just the first mention.

The first two unchecked lines are project-wide exit criteria, and the first domain task after a launcher-ICD blocker is the optical obscuration/spider/baffle item. I’m treating that as the next actionable task and inspecting the current optical/geometry code around those features now.

I’ve confirmed the geometry already contains several baffling parts, but the optical policy still only proves conic prescription and basic vignetting. I’m drilling into the export/build code to line up a proper optical obscuration + direct-path stray-light check with the released geometry, which looks like the real gap behind this TODO item.

I’ve narrowed the change set: add an explicit optical-train obscuration/stop model, drive it from the released geometry, and make `optics-policy` prove both pupil blockage budget and direct-path rejection. I’m doing one quick measurement pass on the current geometry now so I can set sane verification thresholds before editing files.

I’ve got the geometry measurements and the current design supports a reasonable obscuration budget target around 23%. I’m editing the optics model and policy check now to add the explicit stop/vane model, its new report plots, and the regression coverage around those sanity checks.

The first patch missed the current file layout, so I’m re-reading the exact insertion points and applying the optics-model changes in smaller chunks to keep this safe.

I’ve finished the core optics-policy changes. Next I’m wiring in focused tests for the new obscuration and direct-path logic before I run the repo checks and regenerate artifacts.

The code and tests are patched in. I’m running the optics-focused tests first so we can catch any model/report issues before I kick off the broader repo checks and artifact rebuilds.

The optics-policy build is green, so I’m reading the generated summary/report and opening the new pupil/layout images for a sanity pass before I decide whether this closes the TODO item cleanly.

The new optics report looks sane: the pupil image matches the released half-spider geometry, and the keepout sweep is blocked for every sampled ray. I’m updating the backlog/history to mark this item closed, then I’ll run the wider repo gates and site build before committing and deploying.

I’ve marked the backlog item closed and I’m syncing the TODO history ledger before the repo-wide gates run, so the larger build doesn’t fail on process drift.

The ledger is synced now. I’m running the repo’s fast gate next, and if that stays green I’ll move straight into the full site/release build that also regenerates the stakeholder artifacts we need for deploy.

The fast gate is still running through the repo’s generated checks. I’ve already seen the optics and structural geometry stages stay green; I’m letting the remaining policy/test steps finish before I move on to the heavier site build.

The fast gate is clean: 291 tests passed and the policy gates stayed green. I’m running the full website smoke path now, which rebuilds the released artifacts/site and gives us the deployable output plus a final end-to-end sanity check.

The full site build is in the render-and-packaging phase now. Everything upstream of that has stayed green, so I’m just waiting for the stakeholder artifacts and smoke checks to finish cleanly before I push and deploy.

The full release/site path is green now, including `site-policy`. I’m doing one last visual pass on a rebuilt render before I stage and push, just to make sure the broader artifact set still looks coherent after the optics work.

I’ve got the working tree down to the seven expected source/config files. The only noisy one is `config/todo_history.json`, so I’m checking that diff now to make sure it’s just the intended ledger update and not accidental churn before I commit.

The only remaining step on the code side is a quick whitespace/sanity diff check before I commit. After that I’ll add, commit, push, and then rsync the freshly built site to merah in the order you asked.

The commit is in. I’m pushing `main` now, and once that’s up I’ll deploy the already-smoke-tested site from `outputs/site` to merah.

The push is through. The deploy is in flight now; rsync is just taking its time with the rebuilt site tree, and I’m waiting for the remote confirmation before I wrap up.

Implemented the next actionable TODO item by adding an explicit optical stop/obscuration model driven from the released geometry, extending `optics-policy` to check pupil blockage and direct-path keepout rejection, adding the new pupil sanity plot, and closing the backlog item in [TODO.md](/Users/gregb/Documents/devel/aao-mechd/TODO.md). The core changes are in [cassegrain.py](/Users/gregb/Documents/devel/aao-mechd/src/optics/cassegrain.py), [check_optics_prescription.py](/Users/gregb/Documents/devel/aao-mechd/scripts/check_optics_prescription.py), and the new verification settings in [telescope.yaml](/Users/gregb/Documents/devel/aao-mechd/config/telescope.yaml). I also added regression coverage in [test_optics_cassegrain.py](/Users/gregb/Documents/devel/aao-mechd/tests/test_optics_cassegrain.py) and [test_optics_policy.py](/Users/gregb/Documents/devel/aao-mechd/tests/test_optics_policy.py).

Sanity review looked good in [report.md](/Users/gregb/Documents/devel/aao-mechd/outputs/optics_policy/report.md), [pupil.png](/Users/gregb/Documents/devel/aao-mechd/outputs/optics_policy/pupil.png), [layout.png](/Users/gregb/Documents/devel/aao-mechd/outputs/optics_policy/layout.png), and [hero_iso.png](/Users/gregb/Documents/devel/aao-mechd/outputs/blender_renders/hero_iso.png). The optics report shows `pupil_obscuration_fraction = 0.218393` and the keepout direct-path sweep found `0` clear rays to the detector.

`make check` passed with 291 tests. `make website-smoke` passed. I committed and pushed `413982f` (`Add optical stop and obscuration checks`) to `origin/main`, and deployed the rebuilt stakeholder site to merah at `[email protected]:/var/www/vhosts/telescope.symmachus.org/htdocs/`.