Codeg is a collaborative workspace that brings multiple AI coding agents and their sessions into one searchable application. Developers use it to coordinate agents, delegate work, and review unattended coding tasks across desktop, server, Docker, and mobile clients. The catalogue entries are skills and instructions for workflows within Codeg.
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 skills add xintaofei/codeg --skill paper-lookupgit clone --depth 1 https://github.com/xintaofei/codegWrote 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/xintaofei/codeg/paper-lookup)<a href="https://agentmods.dev/skills/xintaofei/codeg/paper-lookup"><img src="https://agentmods.dev/badge/skills/xintaofei/codeg/paper-lookup/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/xintaofei/codeg/paper-lookup"><img src="https://agentmods.dev/badge/skills/xintaofei/codeg/paper-lookup.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Data Exfiltration · line 124 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 5.1 | $0.00146 | $0.03639 |
| Opus 5.5 | $0.00058 | $0.01456 |
| Sonnet 5.5 | $0.00029 | $0.00728 |
| Haiku 4.5 | $0.00015 | $0.00364 |
Grade A, and why
paper-lookup scanned grade A with 1 finding 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 1mo 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
| Claude Code | `WebFetch` | `curl` via Bash | How it starts
The opening of the file, as written. The whole thing — 215 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Paper Lookup
This skill gives you 10 academic literature APIs with documented endpoints. Your job is to turn the user's intent into a reproducible retrieval: pick the authoritative database(s), make bounded and rate-limited calls, and return an answer with enough provenance (endpoints, parameters, identifiers, access date) that a human or another agent can repeat it.
A literature lookup is only as trustworthy as it is repeatable. Prefer explicit identifiers and documented endpoints over broad guessing, report what you queried, and say plainly when a result is partial or a database came back empty — a silent gap reads as "nothing exists" when it may just mean "not indexed here."
Core Workflow
-
Define the retrieval contract — What is the user after? A specific paper by DOI/PMID/arXiv ID? Papers on a topic? An author's publications? A citation graph? An open-access PDF? Full text? Note any constraints that change the answer: date range, field of study, open-access-only, exhaustive list vs. a few top hits. If a constraint that affects correctness is missing (e.g., "recent" with no year, or an author name with many namesakes), ask rather than guess.
-
Select database(s) — Use the selection guide below. Route to the primary database for the intent, then add others only when they earn their place: identifier resolution, open-access lookup, or a known coverage gap. Don't fan out across all ten just because they're available.
-
Read the reference file — Each database has a file in
references/with endpoints, parameters, example calls, and response shapes. Read the relevant file(s) before calling — the parameter and identifier details matter and are easy to get wrong from memory. -
Make bounded API calls — See Making API Calls. For a targeted lookup, the first page is usually enough. For an exhaustive search ("all papers by X", "every citation of Y"), count first when the API exposes a total, paginate deterministically, and reconcile what you retrieved against that total. Ask before a retrieval would exceed ~1,000 records or ~50 calls.
What ships with it
10 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.
- 1mo ago First seen · 215 lines · 146 tokens per session scan A 03b623807cd7
paper-lookup is a skill published in the GitHub repository xintaofei/codeg (4,087 stars, last pushed 3d ago), licensed Apache-2.0. It adds 146 tokens to every session and 3,639 once invoked, about $0.0006 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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