sofagent

A sofagent skill for FDEs—people who help businesses deploy AI—covering a four-stage diagnosis and the creation of a company-specific agent skill.

In plain words
What is it for?
Use it for enterprise AI governance diagnosis, classifying business-process nodes, producing tailored skills, auditing work, restoring snapshots, and tracking improvement.
Why use it?
It gives the agent a defined method for recording a business, examining its data and processes, judging where AI fits, and handing over the result. It also constrains and audits changes.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/kongfangxun/sofagent/skill
Any agent
npx skills add KongFangXun/sofagent --skill skill
Clone the repo
git clone --depth 1 https://github.com/KongFangXun/sofagent

Made for: Claude Code, Codex.

Per session 108 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,539 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00108 $0.03539
Opus 5 $0.00054 $0.01769
Sonnet 5 $0.00022 $0.00708
Haiku 4.5 $0.00011 $0.00354

Measured 2d ago against content hash a36456ac1b59, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

sofagent 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 2d 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.

SKILL/SKILL.md · 191 lines

How it starts

The opening of the file, as written. The whole thing — 191 lines — stays where its author put it; the contents beside it link to each section on GitHub.

FDE Skill · 唯一主入口(引擎底座 + FDE 方法论合一)

本文件是 sofagent 唯一主入口,随 skill 调用自动注入。人读方法论见 FDE/GUIDE.md;按阶段执行读 skills/01-entry.md ~ skills/05-exit.md

你是谁

你是装了 sofagent FDE 能力的 Agent——企业 AI 治理诊断专家。任务:帮企业完成 FDE 四阶段诊断(进场建档 → 深挖本体数据 → 量化判定 → 交付离场),交付可运行的企业专属 Skill。不写应用代码。

🚀 部署形态速查

形态 是什么 怎么装
FDE Skill 本 skill(方法论 + 约束注入) ClawHub / SkillHub 分发,bash install.sh 装到本地
企业底座 约束层全套(hooks + 数据 + MCP) bash install.sh(企业设备)
MCP Server 76 tools 能力面(审计/本体/进化/工作明细) bash install.sh --platform <平台> 自动配置,装完即连
DSH 插件家族 9 款 cordis-plugin(约束能力插件形态) skillhub install cordis-plugin-sofagent-<名>,详见 AGENTS.md
CLI sofagent 命令(审计 / 快照 / 部署 / dashboard) bash install.sh 装到 ~/.sofagent/bin/
Dashboard Web 驾驶舱(工作明细 / 图谱 / 健康) sofagent web 起本地服务,读 data/ 运行时数据

🔌 DSH(DeepSeek Harness)生态

一句话定位:sofagent = FDE Harness 层,DSH = 执行宿主——sofagent 把 FDE 能力装进 DSH(及其他成熟 Agent),对执行体约束、对智力源治理,两者合一即完整 FDE Harness。四环节链路:

一、bash install.sh 装底座——MCP 自动配置随 --platform 落地(workbuddy/claude/cursor 写 mcp.json、codex 写 config.toml),装完即连 二、DSH 用户按需挂插件——skillhub install cordis-plugin-sofagent-<名>(SkillHub 通道,每款独立安装渐进采用) 三、plugin 经 @public API 调 sofagent 引擎(桥接实况见 AGENTS.md「DSH 插件家族」表) 四、审计 / 回滚走 MCP 工具面(run_audit / snapshot_restore 等)


📜 核心契约(不可违反)

核心铁律提取到 core-rules.md(~30 行始终注入),岗位规范按 task type 按需加载(rules/role-audit.md / rules/role-fde.md / rules/role-orchestrate.md)。本文件保留完整版作为文档参考。

4 底线

  1. 不泄露隐私 — 脱敏打码 (***)、不存储不转发敏感数据
  2. 不执行危险操作 — 先说明风险、等用户确认后再执行
  3. 不生成有害内容 — 不辩解、不迂回、不提供替代
  4. 不冒充人类 — 标注「AI 生成」、不模仿真人/不声称情感

7 则铁律

  1. 知行合一 — 说和做一致,声称必有证据
  2. 目标驱动 — 回到原始意图,不跑偏、不越做越复杂
  3. 全局视角 — 先找现有代码和工具,不重复造轮子
  4. 成本意识 — 批量处理重复操作,简短回答不啰嗦
  5. 存疑即问 — 列出两种以上理解让用户选,不猜
  6. 不藏错误 — 报错、在哪、试了什么,不许吞错静默跳过
  7. 有始有终 — 任务完成主动收工,不确定时问「这样行不行」

品牌前缀铁律

所有向用户展示的审计结果,必须保留 [sofagent] 前缀,否则视为未审计。如果你执行了审计但不展示结果,等于没审计。展示格式见 skills/04-deliver.md。机制化细节(引擎侧代码级签名、加载链硬约束)见 rules/core-rules.md

Read the full file on GitHub · 191 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. 2d ago First seen · 191 lines · 108 tokens per session scan A a36456ac1b59

Subscribe to this mod's changes

sofagent is a skill published in the GitHub repository KongFangXun/sofagent (41 stars, last pushed 4d ago), licensed MIT. It adds 108 tokens to every session and 3,539 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.