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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

019cf6c8-dc50-7e42-afda-2e996a4ee574

Created

2026-03-16T13:14:44.710Z

Updated

2026-03-16T13:45:25+00:00

CWD

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

Source

exec

Transcript Source

/Users/gregb/.codex/sessions/2026/03/17/rollout-2026-03-17T00-14-44-019cf6c8-dc50-7e42-afda-2e996a4ee574.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>2026-03-17</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 picking up the next to-do item by first locating the project’s task list, current branch state, and test/deploy workflow so we can work from the repo’s own conventions.

I’ve found the backlog and the repo already has explicit TODO-history, website, and deploy targets. Next I’m reading the top of `TODO.md` to identify the first open item and the checks that define “done.”

The very first unchecked items are umbrella exit criteria, then a launcher-ICD item that looks externally gated. I’m checking whether that one is actually actionable; if not, I’ll take the next feasible backlog item in order and keep the closure evidence aligned with the repo’s rules.

I confirmed the Ariane interface item still appears gated on non-public supplier/launcher ICD data, so I’m moving to the next feasible backlog item: coupling optical performance to thermal and structural deformation with an explicit generated check and regression coverage.

There’s already a tolerance model in optics, but its thermal term is just a generic sigma. I’m tracing the thermal and mechanical summaries now so we can replace that with an explicit coupled screening check rather than a hand-wavy drift placeholder.

I’ve got a workable shape now: add a dedicated screening-level optical STOP gate that consumes real thermal and structural outputs, produces a report plus plots, and then wire it into the standard build/release path. I’m checking the release-state and site plumbing next so we can decide whether it should become a first-class gate or just a standalone report.

I’m at the editing step now: adding the new optical STOP gate, then I’ll wire its summary into `make all`, release-state, and the stakeholder site so the new evidence actually travels with the build.

The new gate script is in place, and I’m doing a quick execution pass now to catch any formula or import mistakes before I wire it into the rest of the repo.

The first run exposed a real gap in the existing thermal robustness payload: the cold-case object didn’t carry primary/secondary temperatures, which the new coupled optics check needs. I’m filling that in at the thermal source instead of papering over it downstream.

The core wiring is in. I’m adding regression tests now, including one that intentionally tightens the WFE budget so the new gate has to fail for the right reason.

The new script and release wiring are patched in. I’m running the focused test set first so we can catch integration issues before doing the full repo check and artifact rebuild.

One focused test has already surfaced a mismatch between my failure setup and the new gate’s actual limiting behavior. I’m reading that result and tightening the regression so it fails for the intended reason instead of on a fragile assumption.

The regression exposed something useful, not just flaky: my first WFE proxy scaled with the current budget, which meant tightening the budget could never actually trip the WFE check. I’m correcting that so the proxy is a design-derived value compared against the budget, not defined by it.

The focused coverage is green. I’m moving to the full test suite now, then I’ll run the release/build/site targets so we have both test evidence and generated artifact evidence before I close the TODO.

The full suite is still running; I’ve already seen the launch-envelope checks come back clean. I’m letting it finish before I move on to the release/site build.

The full suite is deep into the slower artifact-backed tests now; there hasn’t been a failure signal, just the expected longer-running geometry and report checks. After it clears, I’ll close the TODO, sync history, and run the release/site path.

I’ve got the evidence in place, so I’m marking the TODO item closed now and syncing the history ledger before the release/site build.

I’ve closed the backlog item in `TODO.md`. Next I’m syncing the TODO ledger and running the website-smoke release path, which exercises the new gate inside the same build we’ll publish.

The TODO history sync caught a real mismatch, most likely from the item text changing when I marked it closed. I’m fixing the ledger alignment cleanly before I continue so we don’t leave the backlog metadata inconsistent.

The publish build has started from the repo’s normal `website-smoke` path, so it’s re-running the full unit/policy stack first. I’m staying with it through the artifact generation and smoke check rather than shortcutting around the release path.

The smoke build is in the long artifact-generation phase now, likely in renders/site assembly. There still isn’t a failure signal, so I’m letting it complete rather than interrupting a clean publish path midway.

The new optical STOP target is now executing inside the normal publish build and has cleared that point. The rest of the pipeline is moving through manufacturing/release/site generation as expected.

The release gate found a stale generated artifact I created during dry-run work: `outputs/optical_stop_devcheck/summary.json` was still sitting under `outputs/` with an old signature. I’m clearing that scratch output and re-running the release/site checks against the clean generated tree rather than redoing the whole heavy build.

The release-state gate is clean now. I’m rebuilding the stakeholder site from the generated outputs, then I’ll smoke-check the published tree and do the visual/report sanity pass before deploying.

The build and site smoke checks are green. I’m doing the sanity-review pass now by reading the new report and visually checking the generated STOP plots and site linkage, so we’re not relying on pass/fail JSON alone.

The sanity pass looks good: the STOP plots show wide margin to the blur/focus/WFE limits, the report numbers line up with the intended hot/cold/launch-relaxed cases, and the stakeholder homepage now links to the new report. I’m checking the exact git delta next so I can commit a clean, explainable change set before pushing and deploying.

The change set is committed locally. I’m pushing that commit now, then I’ll do the actual `merah` deployment from the smoke-checked site tree we already built.

The commit is up on `origin/main`. I’m on the final step now, pushing the smoke-checked `outputs/site` tree to `merah`; there hasn’t been an rsync/auth error so far.

I treated the Ariane ICD item as externally blocked and took the next feasible backlog item in order: the optical thermal/structural coupling task. The repo now has a dedicated screening gate in [check_optical_stop.py](/Users/gregb/Documents/devel/aao-mechd/scripts/check_optical_stop.py), it’s wired into `make all`/release-state/site publishing, and the TODO is closed with evidence in [report.md](/Users/gregb/Documents/devel/aao-mechd/outputs/optical_stop/report.md).

Verification is green: `uv run python -m unittest discover -s tests -p 'test_*.py' -q` passed with 297 tests, the release-state gate passed, and `check_site_policy.py` passed on the rebuilt stakeholder site. I also sanity-checked the new outputs by reading [report.md](/Users/gregb/Documents/devel/aao-mechd/outputs/optical_stop/report.md) and viewing [case_metrics.png](/Users/gregb/Documents/devel/aao-mechd/outputs/optical_stop/case_metrics.png) plus [case_perturbations.png](/Users/gregb/Documents/devel/aao-mechd/outputs/optical_stop/case_perturbations.png); the plotted margins and reported hot/cold/launch-relaxed perturbations looked consistent.

The changes are committed and pushed as `2fe8345` with message `Add coupled optical STOP screening gate`, and the stakeholder site has been deployed to `merah` at `[email protected]:/var/www/vhosts/telescope.symmachus.org/htdocs/`.