codex-issue-digest

codex-issue-digest is a skill for Claude Code, Codex from MikeManifold/codex-unified. It costs 63 tokens per session (2,113 once invoked), scanned A, a copy of codex-issue-digest, Apache-2.0.

A tool and workflow for summarizing recent GitHub issues in the `openai/codex` repository. GitHub issues are reports of bugs or requests for improvements, grouped here by feature-area labels and time windows.

In plain words
What is it for?
Creating issue digests for selected areas such as the terminal interface or execution system, all areas, or a custom time period.
Why use it?
It reduces the effort of finding and condensing relevant recent bug reports and enhancement requests.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/mikemanifold/codex-unified/codex-issue-digest
Any agent
npx skills add MikeManifold/codex-unified --skill codex-issue-digest
Clone the repo
git clone --depth 1 https://github.com/MikeManifold/codex-unified

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for codex-issue-digest

README.md
[![agentmods](https://agentmods.dev/badge/skills/mikemanifold/codex-unified/codex-issue-digest.svg)](https://agentmods.dev/skills/mikemanifold/codex-unified/codex-issue-digest)
Your own site
<a href="https://agentmods.dev/skills/mikemanifold/codex-unified/codex-issue-digest"><img src="https://agentmods.dev/badge/skills/mikemanifold/codex-unified/codex-issue-digest.svg" alt="Measured on agentmods" height="20"></a>
Per session 63 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,113 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00063 $0.02113
Opus 5 $0.00032 $0.01056
Sonnet 5 $0.00013 $0.00423
Haiku 4.5 $0.00006 $0.00211

Measured 5d ago against content hash b010b5e498c2, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

codex-issue-digest scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/collect_issue_digest.py, scripts/test_collect_issue_digest.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

100% identical to codex-issue-digest — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

.codex/skills/codex-issue-digest/SKILL.md · 128 lines

How it starts

The opening of the file, as written. The whole thing — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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.

  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:

Read the full file on GitHub · 128 lines

Files

What ships with it

3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 5d ago First seen · 128 lines · 63 tokens per session scan A b010b5e498c2

Subscribe to this mod's changes

codex-issue-digest is a skill published in the GitHub repository MikeManifold/codex-unified (2 stars, last pushed 15d ago), licensed Apache-2.0. It adds 63 tokens to every session and 2,113 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to codex-issue-digest, differing in 0 lines, and is treated as a copy.

Related

Other skills, from other repositories

skill-creator

Create, install, or update skills in the workspace. Use when (1) installing a skill from a URL or remote source, (2) creating a new skill from scratch, (3) updating or restructuring existing skills. Always use this skill for any skill installation or creation task.

zhayujie/CowAgent · 61 tokens

image-generation

Generate or edit images from text prompts. Use when the user asks to create, draw, design, or edit an image, illustration, photo, icon, poster, or any visual content.

zhayujie/CowAgent · 41 tokens

implementation-final-review

Perform the repository's risk-tiered independent final review before implementation completion. Use only when explicitly invoked or when repository instructions require it after behavior-impacting implementation work; audit the complete task diff, supported contracts, lifecycle and security boundaries, complexity, and…

openai/openai-agents-python · 58 tokens

deploy-docker-compose

Run the Omnigent server as a Docker compose stack (server + Postgres) on any Docker host — your laptop, a VPS, EC2 by hand, or as the base layer of any container-platform deploy. Invoke when the user wants to build the image, bring up the compose stack, debug the stack on a host they already have, or extend the stack…

omnigent-ai/omnigent · 84 tokens

prompt-optimization

Improve a prompt on the evaluations workbench through a measured loop. Score the baseline first, then duplicate the target column, form a hypothesis from failing rows, edit the copy's prompt draft, run, compare pass rate and cost, and repeat until the numbers hold. Use when the user asks to optimize or improve a…

langwatch/langwatch · 105 tokens

debug-with-langwatch

Root-cause production errors and misbehaving agent runs with LangWatch. Finds errored traces, inspects spans, checks monitor and evaluator scores, then narrows to a root cause. Use when something is failing or misbehaving in production (errors, bad answers, latency spikes).

langwatch/langwatch · 63 tokens