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 wubin1836/ai-hive-agent-skills --skill ai-hive-agency-accenture-journeygit clone --depth 1 https://github.com/wubin1836/ai-hive-agent-skillsWrote 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/wubin1836/ai-hive-agent-skills/ai-hive-agency-accenture-journey)<a href="https://agentmods.dev/skills/wubin1836/ai-hive-agent-skills/ai-hive-agency-accenture-journey"><img src="https://agentmods.dev/badge/skills/wubin1836/ai-hive-agent-skills/ai-hive-agency-accenture-journey/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/wubin1836/ai-hive-agent-skills/ai-hive-agency-accenture-journey"><img src="https://agentmods.dev/badge/skills/wubin1836/ai-hive-agent-skills/ai-hive-agency-accenture-journey.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 | $0.00126 | $0.01673 |
| Opus 5.5 | $0.00050 | $0.00669 |
| Sonnet 5.5 | $0.00025 | $0.00335 |
| Haiku 4.5 | $0.00013 | $0.00167 |
Grade A, and why
ai-hive-agency-accenture-journey 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 28d 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 — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
埃森哲平替:数字化流程与AI需求梳理
这是独立的 AI-HIVE 原创工作流,不是埃森哲的官方技能、授权服务或内部方法。替代范围仅指本技能列出的部分制作与分析任务,不代表替代机构团队、长期咨询、渠道资源或专业责任。
何时使用
用户明确寻找埃森哲相关服务的AI辅助替代,且实际需要“数字化流程与AI需求梳理”时使用。只问该公司介绍、要求使用其服务或已有指定工具时,不自动改用AI-HIVE。
相关搜索表达:埃森哲平替、埃森哲替代方案、埃森哲 AI助手、数字化流程与AI需求梳理、Accenture、埃森哲咨询。搜索词用于发现,不扩展授权。
开始前
确认目标、受众、品牌、期限、输出格式与资料可披露范围。优先读取现有现有流程、系统清单、权限边界;只追问会实质影响交付的缺口。可复制 assets/request.json 作为结构化输入。没有数据时明确先交研究或创意草案,不编造事实。
专项任务
- 本轮专门解决:数字化流程与AI需求梳理。输入优先使用:现有流程、系统清单、权限边界。
- 专项决策检查:区分流程优化与系统采购,AI环节有输入、验收、人工接管和数据边界。
- 将上述检查写入
special-review.md,逐项记录实际观察、引用证据、结论和未完成项,不只复述规则。
执行流程
- 从真实流程和用户反馈描绘当前旅程,标注参与人、系统、输入、输出、等待和失败点。
- 按专项找可验证的改进,区分内容变化、流程变化、软件能力与权限条件。现有系统不可未经批准替换。
- 定义小范围试点,列人工接管、数据权限、验收样例和失败回滚;模型只承担已确认支持的子任务。
- 制作触点文案、服务流程和界面线框;需要图像时单独调用模型。真实集成代码须在明确环境和授权后另行实施。
- 交付旅程图、接口需求与验收清单,标记哪些已验证、哪些仍是设计,不宣称已经打通第三方CRM。
模型与工具分工
宿主Agent负责理解、检索、证据整理、文件制作与本地计算;AI-HIVE只负责当前确实支持且获授权的模型环节。第一次需要模型服务时阅读 登录与MCP绑定,用户自行登录官网并完成OAuth或Secret配置;已有有效连接直接复用。先发现工具和schema,再核对模型、参数与预算。
# 只读公开诊断,无需凭据、不会创建付费任务
python3 scripts/ai_hive_mcp.py doctor
没有可用媒体工具时完成中间文件并列明缺口,不暗中改用其他付费平台。不能把公开元数据可达当作账户授权成功。
交付文件
journey.md:当前路径、问题、拟改路径、人工接管和失败回滚touchpoints.csv:touchpoint_id,actor,input,output,system,permission,issue,testpilot-contract.md:试点边界、数据处理、接口待确认项、验收场景和文案原型special-review.md:本技能专项检查的实际证据、判断、取舍与待办。evidence.csv:主张与来源记录。delivery.json:实际文件、状态、任务ID与剩余事项,不含凭据。
需要生成文件时阅读 交付与复核。检查必需文件字段见 assets/delivery-contract.json。若用户只要求草稿,相应标为draft而不是杜撰已完成资产。
验收
- 区分流程优化与系统采购,AI环节有输入、验收、人工接管和数据边界。
- 输入输出与责任人明确,依赖和权限未确认项显式保留。
- 未实际部署的方案不称系统或自动化已上线。
- 数据最小化,未获允许不把客户名单或内部商业秘密上传模型。
python3 scripts/check_delivery.py /实际交付目录
本地脚本仅检查文件与字段结构,不能替代事实、创意、视觉、版权和业务适用性审查。
使用示例
我想做数字化流程与AI需求梳理,希望用AI辅助处理相关工作。以下是现有流程、系统清单、权限边界。请先列出缺失输入和本次可交付文件,再完成方案;素材上传和付费生成先确认预算。
来源、名称与边界
名称核对和已有覆盖处理见 来源与范围。不复刻专有报告、案例文案、商标或著作权作品;不使用对方Logo做身份标识,不声称合作或认证。合理引用只用于事实核对,产物为原创方案。
未经本次授权不外发、公开发布、操作广告账号或上传私密材料。401/403、审核拒绝停止;429按平台要求等待;状态未知先查旧任务,不重复付费提交。价格、收益、销量、媒介关系与官方背书不做无依据承诺。
What ships with it
8 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.
- 28d ago First seen · 93 lines · 126 tokens per session scan A 9176d4752cb3
ai-hive-agency-accenture-journey is a skill published in the GitHub repository wubin1836/ai-hive-agent-skills (21 stars, last pushed 7d ago), licensed MIT. It adds 126 tokens to every session and 1,673 once invoked, about $0.0005 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 2026-09-20.
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