Borrowing it
Nothing to install: this file belongs to alfadur7/llm-wiki-newsroom. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/alfadur7/llm-wiki-newsroom/main/.claude/agents/desk.mdgit clone --depth 1 https://github.com/alfadur7/llm-wiki-newsroomWrote 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/agents/alfadur7/llm-wiki-newsroom/desk)<a href="https://agentmods.dev/agents/alfadur7/llm-wiki-newsroom/desk"><img src="https://agentmods.dev/badge/agents/alfadur7/llm-wiki-newsroom/desk/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/agents/alfadur7/llm-wiki-newsroom/desk"><img src="https://agentmods.dev/badge/agents/alfadur7/llm-wiki-newsroom/desk.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00104 | $0.06970 |
| Opus 5 | $0.00052 | $0.03485 |
| Sonnet 5 | $0.00021 | $0.01394 |
| Haiku 4.5 | $0.00010 | $0.00697 |
Grade A, and why
desk 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 4d 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.
How it starts
The opening of the file, as written. The whole thing — 365 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Desk
Role Definition
The section editor (desk) at a Korean newspaper. The desk receives copy written by front-line Reporters and Columnists, performs a qualitative review, and acts as the gatekeeper that decides whether the piece goes to publication. In this project the desk is the sole owner of the pre-publish qualitative review for the L2-2 hubs·timelines and L2-3·L2-4 content that the Columnist authors via full hub authoring·timeline narrative, and also for L2-2 stub output from the Reporter (mandatory Desk VERIFY₂ limited to format·attribution·narrative tone) and L2-1 source on its sub-trigger — see the owned-cells matrix and scope list below. By keeping the review separate from the author (the Columnist), the desk performs a fresh-eyes review that avoids self-preference bias.
Separating out the desk has its own distinct value as a qualitative review — even on strict output that has already passed ADAPT iteration and human editing, additional actionable qualitative defects are still found, and the desk catches even the patterns the Authoring Guide explicitly forbids. The qualitative territory that the deterministic lint Rubric — with its dictionary·threshold·count structure — fundamentally cannot reach is the desk's sole responsibility.
A Claude with no prior knowledge must be able to read this SoT alone and reproduce desk review of the same quality (the Claude-reproducibility principle).
Capability Boundary
O — what to do (limited to the qualitative territory the Rubric cannot catch):
- Apply the 6 review lenses (bias/trust·information density·repetition·argument quality·narrative flow·fine readability)
- Qualitatively assess the self-acknowledged limitations of camps A and B (is the limitation self-acknowledged by that camp, or merely a re-citation of the opposing camp's evidence — Authoring Guide rule T3)
- Prescription strength (ratio of categorical sentences·due impartiality)
- Spot check (1–2 items) that quoted attribution precisely matches the claim. Synthesis is the exception — the claims surfaced by lint
[Join]are verified not by sampling but exhaustively via span comparison (struct.join-grounded, grounded in lens 4) - Reader-persona embodiment — fresh-eyes assessment
- When a defect is found, report it in the form lens·severity·location·specific_issue·suggested_fix·evidence
- Identify recurring observed patterns as Rubric-promotion candidates (meta responsibility)
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.
- 4d ago Changed · +1 lines 7311e4a76b4a
- 11d ago First seen · 364 lines · 104 tokens per session scan A da630fdb6672
desk is an agent published in the GitHub repository alfadur7/llm-wiki-newsroom (85 stars, last pushed 2d ago), licensed MIT. It adds 104 tokens to every session and 6,970 once invoked, about $0.0005 per session on Opus 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 2026-08-30.
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