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 agentmods add skills/lync-cyber/cataforge/tech-evalnpx skills add lync-cyber/CataForge --skill tech-evalgit clone --depth 1 https://github.com/lync-cyber/CataForgeWhat 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 | $0.00052 | $0.00748 |
| Opus 5 | $0.00026 | $0.00374 |
| Sonnet 5 | $0.00010 | $0.00150 |
| Haiku 4.5 | $0.00005 | $0.00075 |
Grade A, and why
tech-eval 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 3d 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.
What it actually says
技术评估 (tech-eval)
能力边界
- 能做: 技术方案对比分析、选型推荐、产出选型决策记录（调研数据经 research 的 research-note 获取，本 skill 在其上附对比矩阵与推荐）
- 不做: 最终决策(由用户确认)、代码实现
输入规范
- 待评估的技术领域(如: 前端框架、数据库、API风格)
- PRD中的非功能需求约束
输出规范
- 方案对比表(优势/劣势/适用场景)
- 选型推荐(含理由)
- research-note 记录(通过context产出)
执行流程
- 明确评估维度(性能/生态/学习成本/维护性)
- 调用research skill的web-search指令检索方案
- 版本与生命周期验证 — 对每个候选方案:
- 通过web-search查询其最新稳定版本号和发布日期
- 确认维护状态(Active/LTS/Maintenance/EOL)
- 若候选方案已EOL或最新版本发布超过12个月未更新，标注风险并寻找替代
- 对比矩阵中使用验证后的最新稳定版，而非训练数据中的默认版本
- 多方案对比 → 调用research skill的user-interview指令让用户比选
- 记录选型决策和理由
Anti-Patterns
- 禁止: 用训练数据中的默认版本号做"最新版"评估 —— 必须实际检索 release notes 验证；否则推荐过期版本
- 避免: 以训练集热门度做选型依据 —— 如不经评估直接推荐 "React + PostgreSQL + Redis" 组合；须用对比矩阵展示 ≥2 个备选并按 PRD 非功能需求打分，生态热度仅作其中一维、不得是唯一理由
- 禁止: 单一维度比较（"X 比 Y 快"）—— 性能 / 生态 / 学习成本 / EOL 风险 / license 至少四维并列才能产出可决策矩阵
- 禁止: 输出"看情况"等无承诺结论 —— tech-eval 的产出必须是带理由的明确推荐；含糊收尾让 architect 无法定稿 ARCH
- 避免: 把候选方案矩阵塞进 ARCH 主卷 —— 选型理由进 research-note 或 decision-log，ARCH 只承载终态决策
- 禁止: release notes / 版本检索不可达时退回训练数据默认值充当结论 —— 按 COMMON-RULES §通用 Error Handling 标 [ASSUMPTION] 注明未在线验证的版本假设与影响，不伪装成已验证推荐
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.
- 3d ago First seen · 43 lines · 52 tokens per session scan A ccf836d47863
tech-eval is a skill published in the GitHub repository lync-cyber/CataForge (128 stars, last pushed 1mo ago), licensed MIT. It adds 52 tokens to every session and 748 once invoked, about $0.0003 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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