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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

019cf599-2f18-7902-a0d1-e1ee6e377647

Created

2026-03-16T07:43:02.940Z

Updated

2026-03-16T08:22:40+00:00

CWD

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

Source

exec

Transcript Source

/Users/gregb/.codex/sessions/2026/03/16/rollout-2026-03-16T18-43-02-019cf599-2f18-7902-a0d1-e1ee6e377647.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 state and find the current to-do list item so I can pick up the next task with the right context before making changes.

I’ve found the repo and confirmed the branch is clean. Next I’m reading the backlog and the project checks so I can choose the actual next open task rather than guessing.

I’ve got the backlog structure now. I’m extracting the open items with line numbers so we can work strictly in list order and make sure the chosen task has a clear verification path.

The first two unchecked entries are project-level exit criteria, and the next one is explicitly blocked on external ICD availability. I’m reading the nearby TODO context and history so I can select the first actionable engineering item rather than a blocked/meta item.

The first actionable backlog item is the optical-prescription one, so I’m checking how much optics infrastructure already exists before I commit to that scope. If it’s tractable, I’ll carry it through; if it turns out blocked by larger missing subsystems, I’ll make the smallest honest closure move and document why.

There’s very little optical machinery today, which is actually promising: the repo uses a small optics dataclass everywhere, so we can likely upgrade that contract in one place and keep the rest of the pipeline intact. I’m looking at the build targets next so the new verification lands in the normal `make`/test path instead of becoming an orphaned script.

I’ve confirmed the build already has a pattern for dedicated policy gates and generated reports. The cleanest way to close this item is to add an optical-prescription gate plus manufacturable prescription artifacts, then wire that into `make check`/`make all` so it stays enforced.

Plan for this chunk:
1. Add a released optical-prescription model and regression gate with ray tracing and manufacturable fields from config.
2. Wire the new gate into the normal build/tests, update reports/artifacts, and close the matching TODO/history entry.
3. Run the relevant tests plus full `make check`, rebuild/review outputs and images, then commit, push, and deploy to `merah`.

I’ve got a concrete implementation shape now. I’m starting with the optics core and then I’ll fan the new config-aware loader out through the scripts that currently still build the old first-order object.

I’ve landed the optics core. Before I wire the gate around it, I’m doing a quick trace sanity pass so we don’t propagate a bad convention through the rest of the repo.

I’m wiring the config-aware loader through the build paths now so the new prescription data becomes part of the released artifacts, not just a standalone optics unit test.

The code paths are wired. I’m running the optics and packaging tests first so I can catch any API mismatches before kicking off the full repo checks.

The focused tests are moving cleanly through the optics, packaging, and thermal paths. I’m letting the heavier launch/interference cases finish before I broaden out to `make check`.

The focused suite passed, so I’m syncing the TODO ledger now before the full repo gate. That should stamp the closure timestamp correctly and keep `make check` from failing on backlog history drift.

`make check` is through the unit suite and into the generated-report gates. So far the new optics work hasn’t broken the broader geometry/thermal/manufacturing checks, which is a good sign.

I’ve got the new gate green. I’m reading the generated optics report and opening the sanity plots now to make sure the artifacts say what the code claims, not just that the tests are happy.

The layout plot looks physically sensible. The focal-plane plot technically exists, but it’s hiding the spot spread because it uses absolute image height, so I’m tightening that sanity plot now to show the actual ray spread around each chief ray.

I made one last small sanity-plot improvement, so I’m re-running the full `make check` to reconfirm the final tree rather than leaning on the earlier pass.

I’m on the full website-smoke path now because deployment should prove the published tree still builds cleanly with the new optics artifacts. This one is longer since it rebuilds the whole release bundle, including the stakeholder site.

The site build is still progressing through the full release pipeline. I’ve already reviewed the optics report plus both sanity plots, and the build now just needs to finish the heavier artifact-generation steps before I can push and deploy.

The full site path has moved past `make check` and into the artifact rebuild (`run_cases` onward). I’m keeping it going so the site smoke and deploy steps are based on a fresh release bundle rather than earlier cached outputs.

The render stage is active now, which is actually useful for the sanity review: I’ll have fresh imagery from the released geometry before pushing the site.

The stowed render set is finishing now. Once the site smoke exits cleanly, I’ll do one quick visual pass on the fresh hero render and then move straight into git/push/deploy.

I’m at the commit stage now: tests and site smoke are green, the new optics artifacts look sane, and the remaining steps are just recording the work, pushing it, and doing the manual `merah` deploy.

The commit is on `origin/main`. I’m using the already smoke-tested `outputs/site` tree for the manual deploy so we don’t waste another full rebuild before syncing to `merah`.

The deploy command is still in flight with no errors yet. It’s likely sitting in the `rsync` transfer, so I’m waiting it through rather than restarting anything.

Implemented the next actionable TODO item by replacing the first-order optics-only path with a released conic prescription and a regression gate. The main work is in [src/optics/cassegrain.py](/Users/gregb/Documents/devel/aao-mechd/src/optics/cassegrain.py), the new gate [scripts/check_optics_prescription.py](/Users/gregb/Documents/devel/aao-mechd/scripts/check_optics_prescription.py), and the manufacturing/report wiring in [scripts/build_manufacturing_package.py](/Users/gregb/Documents/devel/aao-mechd/scripts/build_manufacturing_package.py), [scripts/run_cases.py](/Users/gregb/Documents/devel/aao-mechd/scripts/run_cases.py), [config/telescope.yaml](/Users/gregb/Documents/devel/aao-mechd/config/telescope.yaml), and [TODO.md](/Users/gregb/Documents/devel/aao-mechd/TODO.md). I also added coverage in [tests/test_optics_policy.py](/Users/gregb/Documents/devel/aao-mechd/tests/test_optics_policy.py), [tests/test_optics_cassegrain.py](/Users/gregb/Documents/devel/aao-mechd/tests/test_optics_cassegrain.py), and [tests/test_manufacturing_package.py](/Users/gregb/Documents/devel/aao-mechd/tests/test_manufacturing_package.py).

Verification is clean: `make check` passed, `make website-smoke` passed, and I reviewed the generated optics artifacts in [outputs/optics_policy/report.md](/Users/gregb/Documents/devel/aao-mechd/outputs/optics_policy/report.md), [outputs/optics_policy/layout.png](/Users/gregb/Documents/devel/aao-mechd/outputs/optics_policy/layout.png), [outputs/optics_policy/focal_plane.png](/Users/gregb/Documents/devel/aao-mechd/outputs/optics_policy/focal_plane.png), [outputs/manufacturing/optical_prescription.md](/Users/gregb/Documents/devel/aao-mechd/outputs/manufacturing/optical_prescription.md), [outputs/site_policy/report.md](/Users/gregb/Documents/devel/aao-mechd/outputs/site_policy/report.md), and [outputs/blender_renders/hero_iso.png](/Users/gregb/Documents/devel/aao-mechd/outputs/blender_renders/hero_iso.png). The optics report shows EFL/F-number on target with full traced field throughput, and the images looked sane. I committed and pushed this as `c087bc8` (`Add conic optics prescription gate`), and deployed the current smoke-tested site to `merah`.