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.
npx agentmods add skills/mikemanifold/codex-unified/codex-issue-digestnpx skills add MikeManifold/codex-unified --skill codex-issue-digestgit clone --depth 1 https://github.com/MikeManifold/codex-unifiedWrote 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.
[](https://agentmods.dev/skills/mikemanifold/codex-unified/codex-issue-digest)<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>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.
| Model | Per session | Once 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 |
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.
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.
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.
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 labelsto 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
- 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.
- 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 detaileddigest_rows. - Choose the output mode from the user's request:
- Default mode: start the report with
## Summaryand 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
## Detailsfrom the existing collector JSON when it is still available; otherwise rerun the collector.
- Default mode: start the report with
- In
## Summary, write a headline-first executive summary:- The first nonblank line under
## Summarymust 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_markerwhen 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'sattention_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
## Summaryunless they change the headline. Put metadata and optional counts in## Detailsor 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:
- The first nonblank line under
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.
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.
- 5d ago First seen · 128 lines · 63 tokens per session scan A b010b5e498c2
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.
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.
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.
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…
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…
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…
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).