Agent skill

Codex Issue Digest

by deonmenezes in deonmenezes/mantishack

Run a GitHub issue digest for openai/codex by feature-area labels, all areas, and configurable time windows.

Apache-2.0Auto-check passedTesting & QA

Install Codex Issue Digest

skills CLI
$ npx skills add deonmenezes/mantishack --skill codex-issue-digest -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install deonmenezes/mantishack codex-issue-digest --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/deonmenezes/mantishack.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.codex/skills/codex-issue-digest .claude/skills/codex-issue-digest && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
codex-issue-digest
GitHub stars
505
Token cost
~2.3k tokens
SKILL.md length
1,130 words
Files
4 (incl. scripts)
Skills in repo
11
Repo updated
First seen
Licence
Apache-2.0

At a glance

Run a GitHub issue digest for openai/codex by feature-area labels, all areas, and configurable time windows.

  • Works in 8 steps: Use the JSON as the source of truth. It… → Choose the output mode from the user's… → In ## Summary, write a headline-first… → …
  • Asked to summarize recent Codex bug reports
  • SKILL.md covers Objective, Inputs, Workflow and Reaction Handling, plus 4 more sections
  • Runs Python scripts from its folder; calls python3, pytest and git; reaches github.com

What it does

Codex Issue Digest is an agent skill from deonmenezes/mantishack. 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.

Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts (for example `agents/openai.yaml`, `scripts/collect_issue_digest.py` and `scripts/test_collect_issue_digest.py`).

It sits in Testing & QA, covering QA and bug reports. It works with GitHub. The licence is Apache-2.0.

When your agent uses it

  • Asked to summarize recent Codex bug reports
  • Enhancement requests
  • Especially for owner-specific labels such as tui

Example prompts

  • “/codex-issue-digest”

Requirements

  • Python 3

Workflow steps

8 steps, taken from the first numbered list in SKILL.md.

  1. Use the JSON as the source of truth. It includes new issues, new issue comments, new reactions/upvotes, current labels, current reaction…
  2. Choose the output mode from the user's request
  3. In ## Summary, write a headline-first executive summary
  4. In ## Details, when details are requested, include a compact table only when useful
  5. Use the JSON attention_marker exactly. It is empty for normal rows, 🔥 for elevated rows, and 🔥🔥 for very high-attention rows. The…
  6. Use inline numbered references where a row or bullet points to issues, for example Compaction bugs 1, 2. Do not add a separate footnotes…
  7. Label interactions as Interactions; it counts unique human GitHub users who created a new issue, added a new comment, or reacted during…
  8. Mention the collector script_version, repo checkout git_head, and time window in one compact source line. In default mode, put this before…

What it can do on your machine

Read from SKILL.md and the folder at commit c3a2e68. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 2 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python3
    • pytest
    • git

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • github.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Codex Issue Digest loads about 2.3k tokens when it runs. Until then it costs about 71 tokens; SKILL.md has 1,130 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~71
When it runs · the whole SKILL.md, loaded when a task matches
~2.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from deonmenezes/mantishack at commit c3a2e68, republished under its Apache-2.0 licence (© deonmenezes). 1,130 words, ~2,328 tokens.

Download SKILL.mdSave it as .claude/skills/codex-issue-digest/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
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.

  1. 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.
  2. 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.
  3. 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.
  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
Show full SKILL.md (323 more words)Show less

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

© deonmenezes, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (scripts) in .codex/skills/codex-issue-digest of deonmenezes/mantishack.

  • SKILL.md
  • agents/openai.yaml
  • scripts/collect_issue_digest.py
  • scripts/test_collect_issue_digest.py

Open the folder on GitHubat commit c3a2e68

Compare with similar skills

Codex Issue Digest next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Codex Issue Digest compared with similar skills
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Create GitHub IssueNVIDIA/OpenShell15k—~1.7kAutomated safety check: PassApache-2.0
Triage IssuesClickHouse/clickhouse-java1.6k—~904Automated safety check: PassApache-2.0
Gentle AI Issue CreationGentleman-Programming/gentle-shell1.2k—~2.5kAutomated safety check: PassApache-2.0

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Works with

Categories

Questions about Codex Issue Digest

What does Codex Issue Digest do?

Run a GitHub issue digest for openai/codex by feature-area labels, all areas, and configurable time windows. Codex Issue Digest is an agent skill from deonmenezes/mantishack. Run a GitHub issue digest for openai/codex by feature-area labels, all areas, and configurable time windows.

When should I use Codex Issue Digest?

Codex Issue Digest fits situations like: asked to summarize recent Codex bug reports; enhancement requests; especially for owner-specific labels such as tui.

How do I install Codex Issue Digest in Claude Code?

Run `npx skills add deonmenezes/mantishack --skill codex-issue-digest -a claude-code`. Or copy the skill folder (.codex/skills/codex-issue-digest in deonmenezes/mantishack) into .claude/skills/codex-issue-digest in your project. Claude Code loads it when a task matches its description.

How do I install Codex Issue Digest in Codex?

Run `npx skills add deonmenezes/mantishack --skill codex-issue-digest -a codex`. Or copy the skill folder (.codex/skills/codex-issue-digest in deonmenezes/mantishack) into .agents/skills/codex-issue-digest in your project. Codex loads it when a task matches its description.

Can I use Codex Issue Digest in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add deonmenezes/mantishack --skill codex-issue-digest -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/codex-issue-digest, .gemini/skills/codex-issue-digest, .github/skills/codex-issue-digest and .opencode/skills/codex-issue-digest in your project.

What does Codex Issue Digest need to run?

Going by SKILL.md and its folder, Codex Issue Digest needs Python for the scripts in its folder and the command-line tools its instructions call (python3, pytest and git). Our summary lists: Python 3.

Does Codex Issue Digest access the network?

SKILL.md names 1 domain. In commands or code: github.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Codex Issue Digest safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Codex Issue Digest use?

Codex Issue Digest is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Codex Issue Digest use?

About 2.3k tokens (SKILL.md is roughly 9.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Codex Issue Digest?

Skills that share tags, products or a category with Codex Issue Digest: Weavebench Cua Reproduce (AMAP-ML/LongHorizon-Harness, 1.7k stars), Evidence-Driven Testing (michaelshimeles/skills, 1.3k stars), Create GitHub Issue (NVIDIA/OpenShell, 15k stars) and Triage Issues (ClickHouse/clickhouse-java, 1.6k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Codex Issue Digest?

deonmenezes (a GitHub user) maintains it in deonmenezes/mantishack, which has 505 GitHub stars. The repository holds 11 skills in this directory. The repository was last updated on October 3, 2026.

Source: deonmenezes/mantishack on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.