xdev-research

xdev-research is a skill for Claude Code, Codex from Minokun/xdev. It costs 8 tokens per session (9,013 once invoked), scanned A, original, MIT.

A research workflow for algorithm studies. It sets hypotheses, decision thresholds, evaluation data, and experiment records before analysis, then checks results against those records.

In plain words
What is it for?
Use it to plan and run algorithm experiments, review existing research, record positive and negative results, and prepare an auditable research report.
Why use it?
It reduces after-the-fact changes to the research plan and makes reported numbers traceable to saved experiment files. It also provides structured review of methods and results.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions subagents.

Good fit Use it to plan and run algorithm experiments, review existing research, record positive and negative results, and prepare an auditable research report.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/minokun/xdev/xdev-research
View source ↗ Minokun/xdev
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.

Any agent
npx skills add Minokun/xdev --skill xdev-research
Clone the repo
git clone --depth 1 https://github.com/Minokun/xdev

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 xdev-research

README.md
[![agentmods](https://agentmods.dev/badge/skills/minokun/xdev/xdev-research.svg)](https://agentmods.dev/skills/minokun/xdev/xdev-research)
Your own site
<a href="https://agentmods.dev/skills/minokun/xdev/xdev-research"><img src="https://agentmods.dev/badge/skills/minokun/xdev/xdev-research.svg" alt="Measured on agentmods" height="20"></a>
Per session 8 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 9,013 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. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
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 Rogue Agent · line 171
    Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.
    Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
How audits are shown
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 $0.00008 $0.09013
Opus 5 $0.00004 $0.04507
Sonnet 5 $0.00002 $0.01803
Haiku 4.5 $0.00001 $0.00901

Measured today against content hash e9d23db7d369, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

xdev-research 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 today.

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.

skills/xdev-research/SKILL.md · 367 lines

How it starts

The opening of the file, as written. The whole thing — 367 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/xdev-research — 算法研究流程

研究输入: 用户消息(手势之外的原文)

本流程不是独立流程,而是 xdev 体系的研究特化——复用 full-dev 的整套机制,只替换审查维度: 三审查员面板 + 门下门(A1–A4 → R1a–R1c + R1 门下门)、worker receipt 派发、drift check (对照基准换成预注册 proposal.md)、pre-landing 对抗审查(附录 D → 报告对抗审查)。 三大机制的映射:对照基准 = 预注册的假设与判定阈值(≈ Intent Contract), 真实执行 = 实验真跑且指标落盘fresh 审核 = 研究视角的审查员面板 + 结果溯源审计。 硬规则 1–5 全部生效(定义见 /xdev-full-dev,速览见本文附录 H),另有研究专属铁律 T1–T3(见下)。 设计依据:AI-Scientist / Agent Laboratory / RE-Bench / SWE-bench 的自动实验经验—— 预注册防事后调标、指标必须由落盘文件解析、评测集固定禁子采样、负结果同样落盘; 以及 DeepResearch 类系统(Anthropic multi-agent research、STORM、plan-execute-replan)经验—— 三视角开局、orchestrator-worker + 文件系统产物指针(防传话失真)、replan 循环显式退出条件。 专业实践溯源(均已在线验证,对应机制见正文):OSF 严格预注册与 Registered Reports (预注册门禁 + if-then 决策树 + 结局分级);晶体学 R-free(留出集调优/终判分离 + 访问记账); cryo-EM 金标准半图(关键数字双路独立复算);CASP(对应物 = judge 机器盲判——不看叙述只看数字, fresh 审查员提供独立性而非盲性,如实声明);wwPDB OneDep(judge 输出作为机器验证产物随报告归档); FLT blueprint / Prove2Me(matrix 先冻结后执行、陈述与执行分离、文件系统充当多智能体共享记忆); NCI 实验记录规范(append-only + SHA-256 哈希链信任锚)。

研究专属铁律(在通用 5 条之上,违反即流程失败)

  • T1 预注册不可逆:假设(H0/H1)、判定阈值、评测集、seed 数一经用户确认 (阶段 2 门禁单次确认,或授权包内的预授权生效),分析阶段只许判定不许改; 要改 = 显式回到阶段 2 重新过门禁并留痕。
  • T2 数字必须溯源:报告/结论中的每个数字必须指向 runs/ 下真实存在且通过哈希链校验的 metrics 文件路径;由模型凭记忆写出的数字一律无效。评测集固定,禁止子采样挑好结果。 溯源目标必须在持久位置(研究目录/仓库入库);指向 /tmp、会话缓存等易失位置的证据视为无证据。
  • T3 负结果同样落盘:假设被证伪、实验失败、方向走不通,都必须写入报告与实验记录, 不得只报喜。用户喊停只阻止开新轮,不豁免已产生数据的报告义务

目录约定(默认值,按项目惯例可调)

docs/research/<date>-<slug>/
  literature.md    # 文献与现状扫描(阶段 1)
  directions.md    # 方向看板:候选方向清单 + 每轮后的状态更新(阶段 1 建,轮级循环维护)
                   #   只记方向级结论(证实/证伪/搁置+证据),不记 run 状态
  proposal.md      # 假设 + 实验方案 + 预注册验收(阶段 2 产出并冻结;补跑追加为 append-only)
  matrix.yaml      # 机器可读实验矩阵(阶段 2 随方案冻结 = "陈述",阶段 3 执行)
  judge/           # judge 判定脚本 + 解析脚本 + fixtures + 单测 + 冻结内容哈希
  runs/<run-id>/   # 每 run 独立目录:config 副本 + append-only 日志 + metrics.json
                   #   + runtime.json(pid/job 句柄)+ receipt.md + manifest.sha256
  report.md        # 报告,按轮追加;终稿含结局核对表与 judge 原始输出(阶段 5)
  state.md         # 跨会话状态:固定头部(当前阶段/已过门禁/待办/活跃 job)+ run 状态 +
                   #   哈希链清单。只记 run 级状态,不记方向结论(与 directions.md 互斥)

Read the full file on GitHub · 367 lines

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. today Changed · +34 lines e9d23db7d369
  2. 2d ago First seen · 333 lines · 8 tokens per session scan A e8246bc80fac

Subscribe to this mod's changes

xdev-research is a skill published in the GitHub repository Minokun/xdev (24 stars, last pushed yesterday), licensed MIT. It adds 8 tokens to every session and 9,013 once invoked, about $0.0000 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 2026-09-06.

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