codex-issue-digest

Codex Issue Digest

Objective

Produce a headline-first, insight-oriented digest of openai/codex issues for the requested feature-area labels over the previous 24 hours by default. Honor a different duration when the user asks for one, for example "past week" or "48 hours". Default to a summary-only response; include details only when requested.

Include only issues that currently have bug or enhancement plus at least one requested owner label. If the user asks for all areas or all labels, collect bug/enhancement issues across all labels.

Inputs

• Feature-area labels, for example tui exec
• all areas / all labels to scan all current feature labels
• Optional repo override, default openai/codex
• Optional time window, default previous 24 hours; examples: 48h, 7d, 1w, past week

Workflow

1. Run the collector from a current Codex repo checkout:

python3 .codex/skills/codex-issue-digest/scripts/collect_issue_digest.py --labels tui exec --window-hours 24

Use --window "past week" or --window-hours 168 when the user asks for a non-default duration. Use --all-labels when the user says all areas or all labels.

2. Use the JSON as the source of truth. It includes new issues, new issue comments, new reactions/upvotes, current labels, current reaction counts, model-ready summary_inputs, and detailed digest_rows.
3. Choose the output mode from the user's request:
   • Default mode: start the report with ## Summary and do not emit ## Details.
   • Details-upfront mode: if the user asks for details, a table, a full digest, "include details", or similar, start with ## Summary, then include ## Details.
   • Follow-up details mode: if the user asks for more detail after a summary-only digest, produce ## Details from the existing collector JSON when it is still available; otherwise rerun the collector.
4. In ## Summary, write a headline-first executive summary:
   • The first nonblank line under ## Summary must be a single-line headline or judgment, not a bullet. It should be useful even if the reader stops there.
   • On quiet days, prefer exactly: No major issues reported by users. Use this when there are no elevated rows, no newly repeated theme, and nothing that needs owner action.
   • When users are surfacing notable issues, make the headline name the count or theme, for example Two issues are being surfaced by users:.
   • Immediately under an active headline, list only the issues or themes driving attention, ordered by importance. Start each line with the row's attention_marker when present, then a concise owner-readable description and inline issue refs.
   • Treat 🔥🔥 as headline-worthy and 🔥 as elevated. Do not add fire emoji yourself; only copy the row's attention_marker.
   • Keep any extra summary detail after the headline to 1-3 terse lines, only when it adds a decision-relevant caveat, repeated theme, or owner action.
   • Do not include routine counts, broad stats, or low-signal table summaries in ## Summary unless they change the headline. Put metadata and optional counts in ## Details or the footer.
   • In default mode, end the report with a concise prompt such as Want details? I can expand this into the issue table. Keep this separate from the summary headline so the headline stays clean.
   • Cluster and name themes yourself from summary_inputs; the collector intentionally does not hard-code issue categories.
   • Use a cluster only when the issues genuinely share the same product problem. If several issues merely share a broad platform or label, describe them individually.
   • Do not omit a repeated theme just because its individual issues fall below the details table cutoff. Several similar reports should be called out as a repeated customer concern.
   • For single-issue rows, summarize the concern directly instead of calling it a cluster.
   • Use inline numbered issue links from each relevant row's ref_markdown.
   • Example quiet summary:

## Summary
No major issues reported by users.
 
Source: collector v5, git `abc123def456`, window `2026-04-27T00:00:00Z` to `2026-04-28T00:00:00Z`.

• Example active summary:

5. In ## Details, when details are requested, include a compact table only when useful:
   • Prefer rows from digest_rows; include a Refs column using each row's ref_markdown.
   • Keep the table short; omit low-signal rows when the summary already covers them.
   • Use compact columns such as marker, area, type, description, interactions, and refs.
   • The Description cell should be a short owner-readable phrase. Use row description, title, body excerpts, and recent comments, but do not mechanically copy the raw GitHub issue title when it contains incidental details.
   • A clear quiet/no-concern sentence when there is no meaningful signal.
6. Use the JSON attention_marker exactly. It is empty for normal rows, 🔥 for elevated rows, and 🔥🔥 for very high-attention rows. The actual cutoffs are in attention_thresholds.
7. Use inline numbered references where a row or bullet points to issues, for example Compaction bugs [1](https://github.com/openai/codex/issues/123), [2](https://github.com/openai/codex/issues/456). Do not add a separate footnotes section.
8. Label interactions as Interactions; it counts unique human GitHub users who created a new issue, added a new comment, or reacted during the requested window. Multiple posts/reactions from the same user on the same issue count once.
9. Mention the collector script_version, repo checkout git_head, and time window in one compact source line. In default mode, put this before the details prompt so the final line still asks whether the user wants details. In details-upfront mode, it can be the footer.

Reaction Handling

The collector uses GitHub reactions endpoints, which include created_at, to count reactions created during the digest window for hydrated issues. It reports both in-window reaction counts and current reaction totals. Treat current reaction totals as standing engagement, and treat new_reactions / new_upvotes as windowed activity.

By default, the collector fetches issue comments with since=<window start> and caps the number of comment pages per issue. This keeps very long historical threads from dominating a digest run and focuses the report on recent posts. Use --fetch-all-comments only when exhaustive comment history is more important than runtime.

GitHub issue search is still seeded by issue updated_at, so a purely reaction-only issue may be missed if reactions do not bump updated_at. Covering every reaction-only case would require either a persisted snapshot store or a broader scan of labeled issues.

Attention Markers

The collector scales attention markers by the requested time window. The baseline is 5 unique human users for 🔥 and 10 unique human users for 🔥🔥 over 24 hours; longer or shorter windows scale those cutoffs linearly and round up. For example, a one-week report uses 35 and 70 interactions. Unique human users are users who authored a new issue, authored a new comment, or reacted during the window, including upvotes. Multiple actions from the same user on the same issue count once. Bot posts and bot reactions are excluded. In prose, explain this as high user interaction rather than naming the emoji.

Freshness

The automation should run from a repo checkout that contains this skill. For shared daily use, prefer one of these patterns:

• Run the automation in a checkout that is refreshed before the automation starts, for example with git pull --ff-only.
• If the automation cannot safely mutate the checkout, have it report the current git_head from the collector output so readers know which skill/script version produced the digest.

Sample Owner Prompt

Validation

Dry run the collector against recent issues:

python3 .codex/skills/codex-issue-digest/scripts/collect_issue_digest.py --labels tui exec --window-hours 24

Run the focused script tests: