Initial commit: AirCoding V1.0.0 Alpha architecture baseline

Complete architecture document set with multi-model review remediation:
- Frozen interface contracts, runtime semantics, DB schemas
- Event/tool/error/provider registries
- Scheduler and main agent state machines
- C4 module/code views, solution architecture, baseline V1
- Multi-model review reports and joint assessment
- Phase-gate remediation complete (P0/P1/P2/UX resolved)
- Implementation plan with T-000A through T-045
- Reference folders kept as placeholders only
This commit is contained in:
AirCoding
2026-05-28 18:45:01 +08:00
commit 82f3140847
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---
name: codex-issue-digest
description: Run a GitHub issue digest for openai/codex by feature-area labels, all areas, and configurable time windows. Use when asked to summarize recent Codex bug reports or enhancement requests, especially for owner-specific labels such as tui, exec, app, or similar areas.
---
# 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:
```bash
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:
```markdown
## Summary
No major issues reported by users.
Source: collector v5, git `abc123def456`, window `2026-04-27T00:00:00Z` to `2026-04-28T00:00:00Z`.
Want details? I can expand this into the issue table.
```
- Example active summary:
```markdown
## Summary
Two issues are being surfaced by users:
🔥🔥 Terminal launch hangs on startup [1](https://github.com/openai/codex/issues/123)
🔥 Resume switches model providers unexpectedly [2](https://github.com/openai/codex/issues/456)
Source: collector v5, git `abc123def456`, window `2026-04-27T00:00:00Z` to `2026-04-28T00:00:00Z`.
Want details? I can expand this into the issue table.
```
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
```text
Use $codex-issue-digest to run the Codex issue digest for labels tui and exec over the previous 24 hours.
```
```text
Use $codex-issue-digest to run the Codex issue digest for all areas over the past week.
```
## Validation
Dry run the collector against recent issues:
```bash
python3 .codex/skills/codex-issue-digest/scripts/collect_issue_digest.py --labels tui exec --window-hours 24
```
```bash
python3 .codex/skills/codex-issue-digest/scripts/collect_issue_digest.py --all-labels --window "past week" --limit-issues 10
```
Run the focused script tests:
```bash
pytest .codex/skills/codex-issue-digest/scripts/test_collect_issue_digest.py
```

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interface:
display_name: "Codex Issue Digest"
short_description: "Summarize Codex issues by labels or all areas"
default_prompt: "Use $codex-issue-digest to run the Codex issue digest for labels tui and exec over the previous 24 hours."

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import importlib.util
from datetime import timezone
from pathlib import Path
MODULE_PATH = Path(__file__).with_name("collect_issue_digest.py")
MODULE_SPEC = importlib.util.spec_from_file_location(
"collect_issue_digest", MODULE_PATH
)
collect_issue_digest = importlib.util.module_from_spec(MODULE_SPEC)
assert MODULE_SPEC.loader is not None
MODULE_SPEC.loader.exec_module(collect_issue_digest)
def test_build_search_queries_uses_each_owner_and_kind_label():
since = collect_issue_digest.parse_timestamp("2026-04-25T12:34:56Z", "--since")
queries = collect_issue_digest.build_search_queries(
"openai/codex", ["tui", "exec"], since
)
assert queries == [
"repo:openai/codex is:issue updated:>=2026-04-25 label:tui label:bug",
"repo:openai/codex is:issue updated:>=2026-04-25 label:tui label:enhancement",
"repo:openai/codex is:issue updated:>=2026-04-25 label:exec label:bug",
"repo:openai/codex is:issue updated:>=2026-04-25 label:exec label:enhancement",
]
def test_build_search_queries_can_scan_all_labels():
since = collect_issue_digest.parse_timestamp("2026-04-25T12:34:56Z", "--since")
queries = collect_issue_digest.build_search_queries(
"openai/codex", [], since, all_labels=True
)
assert queries == [
"repo:openai/codex is:issue updated:>=2026-04-25 label:bug",
"repo:openai/codex is:issue updated:>=2026-04-25 label:enhancement",
]
def test_normalize_requested_labels_accepts_all_area_phrases():
assert collect_issue_digest.normalize_requested_labels(["all", "areas"]) == (
[],
True,
)
assert collect_issue_digest.normalize_requested_labels(["all-labels"]) == (
[],
True,
)
def test_search_issue_numbers_requests_updated_sort(monkeypatch):
calls = []
def fake_gh_json(args):
calls.append(args)
return {
"items": [
{"number": 1, "updated_at": "2026-04-25T00:00:00Z"},
]
}
monkeypatch.setattr(collect_issue_digest, "gh_json", fake_gh_json)
assert collect_issue_digest.search_issue_numbers(["query"], limit=10) == [1]
assert "-f" in calls[0]
assert "sort=updated" in calls[0]
assert "order=desc" in calls[0]
def test_search_issue_numbers_applies_limit_per_query(monkeypatch):
calls = []
def fake_gh_json(args):
calls.append(args)
query = next(
value.removeprefix("q=") for value in args if value.startswith("q=")
)
page = int(
next(
value.removeprefix("page=")
for value in args
if value.startswith("page=")
)
)
base = 10_000 if query == "first" else 20_000
offset = (page - 1) * 100
return {
"items": [
{
"number": base + offset + idx,
"updated_at": f"2026-04-25T00:{idx:02d}:00Z",
}
for idx in range(100)
]
}
monkeypatch.setattr(collect_issue_digest, "gh_json", fake_gh_json)
collect_issue_digest.search_issue_numbers(["first", "second"], limit=150)
queried_pages = [
(
next(
value.removeprefix("q=") for value in args if value.startswith("q=")
),
next(
value.removeprefix("page=")
for value in args
if value.startswith("page=")
),
)
for args in calls
]
assert queried_pages == [
("first", "1"),
("first", "2"),
("second", "1"),
("second", "2"),
]
def test_summarize_issue_keeps_new_comments_and_reaction_signals():
since = collect_issue_digest.parse_timestamp("2026-04-25T00:00:00Z", "--since")
until = collect_issue_digest.parse_timestamp("2026-04-26T00:00:00Z", "--until")
issue = {
"number": 123,
"title": "TUI does not redraw",
"html_url": "https://github.com/openai/codex/issues/123",
"state": "open",
"created_at": "2026-04-24T20:00:00Z",
"updated_at": "2026-04-25T10:00:00Z",
"user": {"login": "alice"},
"author_association": "NONE",
"comments": 2,
"body": "The terminal freezes after resize.",
"labels": [{"name": "bug"}, {"name": "tui"}],
"reactions": {"total_count": 3, "+1": 2, "rocket": 1},
}
comments = [
{
"id": 1,
"created_at": "2026-04-25T11:00:00Z",
"updated_at": "2026-04-25T11:00:00Z",
"html_url": "https://github.com/openai/codex/issues/123#issuecomment-1",
"user": {"login": "bob"},
"author_association": "MEMBER",
"body": "I can reproduce this on main.",
"reactions": {"total_count": 4, "heart": 1, "+1": 3},
},
{
"id": 2,
"created_at": "2026-04-24T11:00:00Z",
"updated_at": "2026-04-24T11:00:00Z",
"html_url": "https://github.com/openai/codex/issues/123#issuecomment-2",
"user": {"login": "carol"},
"author_association": "NONE",
"body": "Older comment.",
"reactions": {"total_count": 1, "eyes": 1},
},
]
summary = collect_issue_digest.summarize_issue(
issue,
comments,
["tui", "exec"],
since,
until,
body_chars=200,
comment_chars=200,
)
assert summary == {
"number": 123,
"title": "TUI does not redraw",
"description": "TUI does not redraw",
"url": "https://github.com/openai/codex/issues/123",
"state": "open",
"author": "alice",
"author_association": "NONE",
"created_at": "2026-04-24T20:00:00Z",
"updated_at": "2026-04-25T10:00:00Z",
"labels": ["bug", "tui"],
"kind_labels": ["bug"],
"owner_labels": ["tui"],
"comments_total": 2,
"comments_hydration": {
"fetched": 2,
"since": None,
"truncated": False,
"max_pages": None,
},
"issue_reactions": {"+1": 2, "rocket": 1},
"issue_reaction_total": 3,
"comment_reaction_total": 5,
"new_comment_reaction_total": 4,
"new_issue_reactions": 0,
"new_issue_upvotes": 0,
"new_comment_reactions": 0,
"new_comment_upvotes": 0,
"new_reactions": 0,
"new_upvotes": 0,
"user_interactions": 1,
"attention": False,
"attention_level": 0,
"attention_marker": "",
"engagement_score": 12,
"activity": {
"new_issue": False,
"new_comments": 1,
"new_human_comments": 1,
"new_reactions": 0,
"new_upvotes": 0,
"updated_without_visible_new_post": False,
},
"body_excerpt": "The terminal freezes after resize.",
"new_comments": [
{
"id": 1,
"author": "bob",
"author_association": "MEMBER",
"created_at": "2026-04-25T11:00:00Z",
"updated_at": "2026-04-25T11:00:00Z",
"url": "https://github.com/openai/codex/issues/123#issuecomment-1",
"human_user_interaction": True,
"reactions": {"+1": 3, "heart": 1},
"reaction_total": 4,
"new_reactions": 0,
"new_upvotes": 0,
"new_reaction_counts": {},
"body_excerpt": "I can reproduce this on main.",
}
],
}
def test_summarize_issue_filters_non_owner_or_non_kind_labels():
since = collect_issue_digest.parse_timestamp("2026-04-25T00:00:00Z", "--since")
until = collect_issue_digest.parse_timestamp("2026-04-26T00:00:00Z", "--until")
base_issue = {
"number": 1,
"title": "Question",
"created_at": "2026-04-25T01:00:00Z",
"updated_at": "2026-04-25T01:00:00Z",
"labels": [{"name": "question"}, {"name": "tui"}],
}
assert (
collect_issue_digest.summarize_issue(
base_issue,
[],
["tui"],
since,
until,
body_chars=100,
comment_chars=100,
)
is None
)
issue_without_owner = dict(base_issue)
issue_without_owner["labels"] = [{"name": "bug"}, {"name": "app"}]
assert (
collect_issue_digest.summarize_issue(
issue_without_owner,
[],
["tui"],
since,
until,
body_chars=100,
comment_chars=100,
)
is None
)
def test_resolve_window_defaults_to_previous_hours():
class Args:
since = None
until = "2026-04-26T12:00:00Z"
window_hours = 24
since, until = collect_issue_digest.resolve_window(Args())
assert since.isoformat() == "2026-04-25T12:00:00+00:00"
assert until.tzinfo == timezone.utc
def test_parse_duration_hours_accepts_common_phrases():
assert collect_issue_digest.parse_duration_hours("past week") == 168
assert collect_issue_digest.parse_duration_hours("48h") == 48
assert collect_issue_digest.parse_duration_hours("2 days") == 48
assert collect_issue_digest.parse_duration_hours("1w") == 168
def test_attention_thresholds_scale_by_window_length():
one_day = collect_issue_digest.attention_thresholds_for_window(24)
assert one_day["elevated"] == 5
assert one_day["very_high"] == 10
half_day = collect_issue_digest.attention_thresholds_for_window(12)
assert half_day["elevated"] == 3
assert half_day["very_high"] == 5
week = collect_issue_digest.attention_thresholds_for_window(168)
assert week["elevated"] == 35
assert week["very_high"] == 70
assert collect_issue_digest.attention_marker_for(34, week) == ""
assert collect_issue_digest.attention_marker_for(35, week) == "🔥"
assert collect_issue_digest.attention_marker_for(70, week) == "🔥🔥"
def test_fetch_comments_uses_since_filter_and_page_cap(monkeypatch):
calls = []
def fake_gh_json(args):
calls.append(args)
return [{"id": idx} for idx in range(100)]
monkeypatch.setattr(collect_issue_digest, "gh_json", fake_gh_json)
since = collect_issue_digest.parse_timestamp("2026-04-25T00:00:00Z", "--since")
payload = collect_issue_digest.fetch_comments(
"openai/codex", 123, since=since, max_pages=1
)
assert len(payload["items"]) == 100
assert payload["truncated"] is True
assert payload["max_pages"] == 1
assert calls == [
[
"api",
"repos/openai/codex/issues/123/comments?since=2026-04-25T00%3A00%3A00Z&per_page=100&page=1",
]
]
def test_issue_description_prefers_title_over_body_noise():
issue = {
"title": "Codex.app GUI: MCP child processes not reaped after task completion",
"body": "A later crash mention should not override the title-level symptom.",
"labels": [{"name": "app"}, {"name": "bug"}],
}
description = collect_issue_digest.issue_description(issue)
assert "MCP child processes" in description
assert "crash" not in description.casefold()
def test_attention_markers_count_human_user_interactions():
since = collect_issue_digest.parse_timestamp("2026-04-25T00:00:00Z", "--since")
until = collect_issue_digest.parse_timestamp("2026-04-26T00:00:00Z", "--until")
issue = {
"number": 456,
"title": "Agent context is exploding",
"html_url": "https://github.com/openai/codex/issues/456",
"state": "open",
"created_at": "2026-04-25T01:00:00Z",
"updated_at": "2026-04-25T12:00:00Z",
"user": {"login": "alice"},
"labels": [{"name": "bug"}, {"name": "agent"}],
}
comments = [
{
"id": idx,
"created_at": "2026-04-25T02:00:00Z",
"updated_at": "2026-04-25T02:00:00Z",
"user": {"login": f"user-{idx}"},
"body": "same here",
}
for idx in range(4)
]
comments.append(
{
"id": 99,
"created_at": "2026-04-25T02:00:00Z",
"updated_at": "2026-04-25T02:00:00Z",
"user": {"login": "github-actions[bot]"},
"body": "duplicate bot note",
}
)
summary = collect_issue_digest.summarize_issue(
issue,
comments,
["agent"],
since,
until,
body_chars=100,
comment_chars=100,
)
assert summary["user_interactions"] == 5
assert summary["activity"]["new_human_comments"] == 4
assert summary["attention"] is True
assert summary["attention_level"] == 1
assert summary["attention_marker"] == "🔥"
issue["created_at"] = "2026-04-24T01:00:00Z"
comments.extend(
{
"id": idx,
"created_at": "2026-04-25T03:00:00Z",
"updated_at": "2026-04-25T03:00:00Z",
"user": {"login": f"extra-user-{idx}"},
"body": "also seeing this",
}
for idx in range(100, 106)
)
summary = collect_issue_digest.summarize_issue(
issue,
comments,
["agent"],
since,
until,
body_chars=100,
comment_chars=100,
)
assert summary["user_interactions"] == 10
assert summary["attention_level"] == 2
assert summary["attention_marker"] == "🔥🔥"
def test_reactions_count_toward_attention_markers():
since = collect_issue_digest.parse_timestamp("2026-04-25T00:00:00Z", "--since")
until = collect_issue_digest.parse_timestamp("2026-04-26T00:00:00Z", "--until")
issue = {
"number": 789,
"title": "Support 1M token context",
"html_url": "https://github.com/openai/codex/issues/789",
"state": "open",
"created_at": "2026-04-24T01:00:00Z",
"updated_at": "2026-04-25T12:00:00Z",
"user": {"login": "alice"},
"labels": [{"name": "enhancement"}, {"name": "context"}],
"reactions": {"total_count": 20, "+1": 20},
}
comments = [
{
"id": 1,
"created_at": "2026-04-25T02:00:00Z",
"updated_at": "2026-04-25T02:00:00Z",
"user": {"login": "commenter"},
"body": "please",
"reactions": {"total_count": 2, "+1": 2},
}
]
issue_reactions = [
{
"content": "+1",
"created_at": "2026-04-25T03:00:00Z",
"user": {"login": f"reactor-{idx}"},
}
for idx in range(18)
]
comment_reactions_by_id = {
1: [
{
"content": "heart",
"created_at": "2026-04-25T04:00:00Z",
"user": {"login": "human-reactor"},
},
{
"content": "+1",
"created_at": "2026-04-25T04:00:00Z",
"user": {"login": "github-actions[bot]"},
},
]
}
summary = collect_issue_digest.summarize_issue(
issue,
comments,
["context"],
since,
until,
body_chars=100,
comment_chars=100,
issue_reaction_events=issue_reactions,
comment_reactions_by_id=comment_reactions_by_id,
)
assert summary["new_reactions"] == 19
assert summary["new_upvotes"] == 18
assert summary["user_interactions"] == 20
assert summary["attention_level"] == 2
assert summary["attention_marker"] == "🔥🔥"
assert summary["new_comments"][0]["new_reactions"] == 1
assert summary["new_comments"][0]["new_upvotes"] == 0
def test_user_interactions_are_deduped_by_human_login():
since = collect_issue_digest.parse_timestamp("2026-04-25T00:00:00Z", "--since")
until = collect_issue_digest.parse_timestamp("2026-04-26T00:00:00Z", "--until")
def comment(comment_id, login):
return {
"id": comment_id,
"created_at": f"2026-04-25T0{comment_id + 1}:00:00Z",
"updated_at": f"2026-04-25T0{comment_id + 1}:00:00Z",
"user": {"login": login},
"body": "same issue",
}
def reaction(content, login, created_at="2026-04-25T10:00:00Z"):
return {
"content": content,
"created_at": created_at,
"user": {"login": login},
}
issue = {
"number": 790,
"title": "Repeated pings should not boost attention",
"html_url": "https://github.com/openai/codex/issues/790",
"state": "open",
"created_at": "2026-04-25T01:00:00Z",
"updated_at": "2026-04-25T12:00:00Z",
"user": {"login": "Alice"},
"labels": [{"name": "bug"}, {"name": "tui"}],
}
comments = [comment(1, "alice"), comment(2, "ALICE"), comment(3, "bob")]
comments.append(comment(4, "github-actions[bot]"))
issue_reactions = [
reaction("+1", "alice"),
reaction("rocket", "Alice"),
reaction("+1", "bob"),
reaction("+1", "github-actions[bot]"),
reaction("+1", "carol", created_at="2026-04-24T23:00:00Z"),
]
comment_reactions_by_id = {
1: [reaction("heart", "alice")],
2: [reaction("+1", "bob")],
3: [reaction("eyes", "carol")],
}
summary = collect_issue_digest.summarize_issue(
issue,
comments,
["tui"],
since,
until,
body_chars=100,
comment_chars=100,
issue_reaction_events=issue_reactions,
comment_reactions_by_id=comment_reactions_by_id,
)
assert summary["activity"]["new_human_comments"] == 3
assert summary["new_reactions"] == 6
assert summary["user_interactions"] == 3
assert summary["attention"] is False
assert summary["attention_marker"] == ""
def test_digest_rows_are_table_ready_with_concise_descriptions():
rows = collect_issue_digest.digest_rows(
[
{
"number": 1,
"title": "Quiet bug",
"description": "Quiet bug",
"url": "https://github.com/openai/codex/issues/1",
"owner_labels": ["context"],
"kind_labels": ["bug"],
"state": "open",
"attention": False,
"attention_level": 0,
"attention_marker": "",
"user_interactions": 1,
"new_reactions": 0,
"new_upvotes": 0,
"engagement_score": 3,
"issue_reaction_total": 0,
"comment_reaction_total": 0,
"updated_at": "2026-04-25T01:00:00Z",
"activity": {
"new_issue": True,
"new_comments": 0,
"new_reactions": 0,
"updated_without_visible_new_post": False,
},
},
{
"number": 2,
"title": "Busy bug",
"description": "High-volume bug report",
"url": "https://github.com/openai/codex/issues/2",
"owner_labels": ["agent"],
"kind_labels": ["bug"],
"state": "open",
"attention": True,
"attention_level": 1,
"attention_marker": "🔥",
"user_interactions": 17,
"new_reactions": 3,
"new_upvotes": 2,
"engagement_score": 20,
"issue_reaction_total": 5,
"comment_reaction_total": 2,
"updated_at": "2026-04-25T02:00:00Z",
"activity": {
"new_issue": False,
"new_comments": 16,
"new_reactions": 3,
"updated_without_visible_new_post": False,
},
},
]
)
assert rows[0] == {
"ref": 1,
"ref_markdown": "[1](https://github.com/openai/codex/issues/2)",
"marker": "🔥",
"attention_marker": "🔥",
"number": 2,
"description": "High-volume bug report",
"title": "Busy bug",
"url": "https://github.com/openai/codex/issues/2",
"area": "agent",
"kind": "bug",
"state": "open",
"interactions": 17,
"user_interactions": 17,
"new_reactions": 3,
"new_upvotes": 2,
"current_reactions": 7,
}
def test_summary_inputs_are_model_ready_without_preclustering():
issues = [
{
"number": 20,
"title": "Windows app Browser Use external navigation fails",
"description": "Browser Use navigation or app-server failure",
"url": "https://github.com/openai/codex/issues/20",
"labels": ["app", "bug"],
"owner_labels": ["app"],
"kind_labels": ["bug"],
"attention": False,
"attention_level": 0,
"attention_marker": "",
"user_interactions": 3,
"new_reactions": 1,
"engagement_score": 8,
"updated_at": "2026-04-25T04:00:00Z",
"activity": {"new_comments": 2},
},
{
"number": 21,
"title": "On Windows, cmake output waits until timeout",
"description": "Windows command timeout/capture problem",
"url": "https://github.com/openai/codex/issues/21",
"labels": ["app", "bug"],
"owner_labels": ["app"],
"kind_labels": ["bug"],
"attention": False,
"attention_level": 0,
"attention_marker": "",
"user_interactions": 3,
"new_reactions": 0,
"engagement_score": 7,
"updated_at": "2026-04-25T03:00:00Z",
"activity": {"new_comments": 3},
},
{
"number": 22,
"title": "Windows computer use tool fails to click buttons",
"description": "Computer-use workflow failure",
"url": "https://github.com/openai/codex/issues/22",
"labels": ["app", "bug"],
"owner_labels": ["app"],
"kind_labels": ["bug"],
"attention": False,
"attention_level": 0,
"attention_marker": "",
"user_interactions": 3,
"new_reactions": 0,
"engagement_score": 6,
"updated_at": "2026-04-25T02:00:00Z",
"activity": {"new_comments": 3},
},
]
rows = collect_issue_digest.summary_inputs(issues, ref_map={20: 1, 21: 2, 22: 3})
assert rows == [
{
"ref": 1,
"ref_markdown": "[1](https://github.com/openai/codex/issues/20)",
"number": 20,
"title": "Windows app Browser Use external navigation fails",
"description": "Browser Use navigation or app-server failure",
"url": "https://github.com/openai/codex/issues/20",
"labels": ["app", "bug"],
"owner_labels": ["app"],
"kind_labels": ["bug"],
"state": "",
"attention_marker": "",
"interactions": 3,
"new_comments": 2,
"new_reactions": 1,
"new_upvotes": 0,
"current_reactions": 0,
},
{
"ref": 2,
"ref_markdown": "[2](https://github.com/openai/codex/issues/21)",
"number": 21,
"title": "On Windows, cmake output waits until timeout",
"description": "Windows command timeout/capture problem",
"url": "https://github.com/openai/codex/issues/21",
"labels": ["app", "bug"],
"owner_labels": ["app"],
"kind_labels": ["bug"],
"state": "",
"attention_marker": "",
"interactions": 3,
"new_comments": 3,
"new_reactions": 0,
"new_upvotes": 0,
"current_reactions": 0,
},
{
"ref": 3,
"ref_markdown": "[3](https://github.com/openai/codex/issues/22)",
"number": 22,
"title": "Windows computer use tool fails to click buttons",
"description": "Computer-use workflow failure",
"url": "https://github.com/openai/codex/issues/22",
"labels": ["app", "bug"],
"owner_labels": ["app"],
"kind_labels": ["bug"],
"state": "",
"attention_marker": "",
"interactions": 3,
"new_comments": 3,
"new_reactions": 0,
"new_upvotes": 0,
"current_reactions": 0,
},
]