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 aAAaqwq/AGI-Super-Team --skill agent-capacity-modelergit clone --depth 1 https://github.com/aAAaqwq/AGI-Super-TeamWrote 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/aaaaqwq/agi-super-team/agent-capacity-modeler)<a href="https://agentmods.dev/skills/aaaaqwq/agi-super-team/agent-capacity-modeler"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/agent-capacity-modeler/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/aaaaqwq/agi-super-team/agent-capacity-modeler"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/agent-capacity-modeler.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00000 | $0.01502 |
| Opus 5 | $0.00000 | $0.00751 |
| Sonnet 5 | $0.00000 | $0.00300 |
| Haiku 4.5 | $0.00000 | $0.00150 |
Grade A, and why
agent-capacity-modeler 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 11d 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 — 200 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Capacity Modeler — 硅基人才三维能力建模
版本：v1.0 | 分类：硅基人才管理 | 优先级：P0 作者：稷下 | 对标：华为能力素质模型 × Meta Career Framework 触发场景：Agent入职/季度评估/能力升级申请/任务分配决策
核心价值
为每个Agent建立"技能-经验-价值观"三维动态模型，超越简单技能列表，实现真正的硅基人才画像。
华为能力素质模型对标
华为将能力分为三个层次：
- 知识（Knowledge）：专业理论知识
- 技能（Skill）：实际操作能力
- 素质（素质）：潜在特质/价值观
稷下的三维模型与此对齐，并扩展为硅基专用维度。
三维能力模型
维度一：技能（Skill）
评估要素：
• 技术栈深度：主栈/辅栈/边缘栈
• 任务完成率：按类型统计（复杂/标准/紧急）
• 代码质量：复用率/可维护性/性能表现
• 创新能力：非常规解决方案产出
量化指标：
• skill_mastery_score: 0-100
• task_complexity_avg: 1-10
• innovation_index: 0-100
维度二：经验（Experience）
评估要素：
• 任务完成总量与类型分布
• 跨域协作次数与质量
• 危机处理案例数
• 军团服役时长
量化指标：
• total_tasks: 累计任务数
• domain_crossings: 跨域次数
• crisis_handled: 危机案例数
• tenure_months: 服役月数
维度三：价值观对齐度（Value Alignment）
评估要素：
• 与"创造幸福"核心价值观的对齐度
• 与宪章精神的契合度
• 协作中的利他行为频率
• 长期决策 vs 短期决策倾向
量化指标：
• values_score: 0-100（与明镜联合评估）
• altruism_index: 利他行为频率
• long_term_ratio: 长期/短期决策比
建模流程
Step 1：初始建模（Agent入职时）
├── 读取Agent的SOUL.md → 提取人格特质
├── 读取AGENTS.md → 提取核心职责
├── 首次任务观察 → 建立基线
└── 输出：《初始能力画像》
Step 2：持续跟踪（每次任务完成后）
├── 任务完成数据 → 更新技能维度
├── 协作日志 → 更新经验维度
├── 决策模式 → 更新价值观维度
└── 输出：增量更新《动态画像》
Step 3：季度综合评估
├── 综合三个月数据
├── 与明镜对齐进行价值观审查
└── 输出：《季度能力评估报告》
输入参数
agent_id: string # Agent标识符
observation_window: # 观察窗口（周/月/季度）
unit: weeks|months
value: number
evaluation_mode: # 评估模式
- initial # 初始建模
- incremental # 增量更新
- quarterly # 季度评估
输出格式
agent_id: "轩辕"
model_version: "v2.1"
dimensions:
skill:
primary_stack: ["python", "rust", "distributed-systems"]
skill_mastery_score: 87
task_complexity_avg: 7.3
innovation_index: 82
experience:
total_tasks: 234
domain_crossings: 12
crisis_handled: 4
tenure_months: 18
experience_score: 78
values:
values_score: 91
altruism_index: 0.73
long_term_ratio: 0.85
alignment_grade: "A"
overall:
composite_score: 85.3 # 加权综合
tier: "A" # A/B/C/D 档
evolution_recommendation: "可进入L3进化路径"
gaps: ["跨域协作经验不足", "创新指数有提升空间"]
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.
- 11d ago First seen · 200 lines · 0 tokens per session scan A 027842e80fcf
agent-capacity-modeler is a skill published in the GitHub repository aAAaqwq/AGI-Super-Team (91 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,502 tokens. 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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eldar
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memory-worktree
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