ai-research-arm: Skill for Codex

.agents/skills/gen-research-ko-backfill/SKILL.md

gen-research-ko-backfill is a skill for Codex from guzus/ai-research-arm. It costs 23 tokens per session (694 once invoked), scanned A, original, MIT.

A workflow for publishing a Korean translation of an existing ARA generative-research article. It uses the repository's translation process and sends the result for review.

In plain words
What is it for?
Use it to check that an article exists, see whether a Korean version is already present, and dispatch a translation with a review pull request or requested automatic merge.
Why use it?
It avoids translating the wrong article, creating duplicate Korean content, or bypassing the repository's required publication path.

Skill for Codex

Written for Codex: agents/openai.yaml present. Also seen: installed under .agents/ (shared by several agents).

This is guzus/ai-research-arm's own configuration. It tells Codex how to work on ai-research-arm itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything ai-research-arm configures →

Reuse

Borrowing it

Nothing to install: this file belongs to guzus/ai-research-arm. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/guzus/ai-research-arm/main/.agents/skills/gen-research-ko-backfill/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/guzus/ai-research-arm

Made for: Codex.

Wrote this? Show the measurements

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README.md
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Your own site
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Your own site · 80×15
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Per session 23 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 694 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found 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 5.1 $0.00023 $0.00694
Opus 5.5 $0.00009 $0.00278
Sonnet 5.5 $0.00005 $0.00139
Haiku 4.5 $0.00002 $0.00069

Measured 21d ago against content hash 40d3cd170d59, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

gen-research-ko-backfill 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 21d 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.

.agents/skills/gen-research-ko-backfill/SKILL.md · 63 lines

How it starts

The opening of the file, as written. The whole thing — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Gen Research Ko Backfill

Publish a Korean translation of an existing ARA generative-research article by dispatching the repo's translate-generative-research.yml ("Korean Generative Research Backfill"). That workflow is the publication path: it resolves the canonical source, translates in an isolated adapter, checks source/translation parity, writes through the canonical writer, verifies the exact commit, and lands it as a review PR (or auto-merges on request). main is PR-only, so do not translate and commit locally instead. The dashboard is static and manifest-driven: do not add runtime translation.

First-Principles Check

Before dispatching, answer:

  • Which article is the target, and does research/generative/index.json on the default branch contain its slug?
  • Is there already a Korean artifact under translations.ko? If yes, did the user ask to replace it?
  • Does the user want the review PR (the default) or an auto-merge?

If the target slug does not exist, stop and ask for the correct article.

Workflow

  1. Resolve the repo and the target article from an up-to-date default branch.
cd "$(git rev-parse --show-toplevel)"
git fetch origin
git show origin/main:research/generative/index.json \
  | jq -r '.[] | select(.slug=="<slug>") | [.slug, .file, (.translations.ko.file // "")] | @tsv'
  1. Dispatch the backfill.
gh workflow run translate-generative-research.yml -f slug="<slug>"

Add -f overwrite=true only when a Korean version exists and the user asked to replace it. Add -f auto_merge=true only when the user asked to skip the review PR.

  1. Follow the run to completion.
gh run list --workflow translate-generative-research.yml --limit 3
gh run watch <run-id> --exit-status

A failed run is a failed backfill: report the failing step and its log excerpt (gh run view <run-id> --log-failed), and do not retry by publishing another way. The run summary names the artifact path and, in review mode, the PR (branch prefix automation/ko-backfill).

Read the full file on GitHub · 63 lines

Files

What ships with it

1 file 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. 21d ago First seen · 63 lines · 23 tokens per session scan A 40d3cd170d59

Subscribe to this mod's changes

gen-research-ko-backfill is a skill published in the GitHub repository guzus/ai-research-arm (14 stars, last pushed today), licensed MIT. It adds 23 tokens to every session and 694 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-16.

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