Initial AM516 Feishu bot package

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---
name: joint-module-model-recommendation
description: Parse one eRob robot joint module model and generate internal similar-model candidates from the packaged v3.1 recommendation rule source. This skill does not query inventory, ERP, production, purchasing, lead-time, or customer commitment data.
---
# Joint Module Model Recommendation
## Purpose
Generate internal similar-model candidates for one eRob joint module model.
## Rule Source
Use the packaged local rule file:
```text
capabilities/am516-delivery-prediction/rules/eRob关节模组相似型号推荐规则_v3.1.md
```
If the rule file is missing, stop.
## Boundary
- Output is internal candidate only.
- Do not query API, ERP, inventory, purchasing, production, or lead-time data.
- Do not write "can directly replace", "committed", or "can deliver".
- The original model is the baseline.
- Candidates require customer confirmation and AR516 or authorized human review.
## Input
Accept one model only.
Example:
```text
eRob90H100I-FHM-18CT[V6]
```
## Core Constraints
Strong constraints must stay unchanged:
- outer diameter;
- gear ratio;
- installation;
- communication;
- output shaft.
Allowed upgrades:
- brake `F -> B`;
- encoder upgrade only, never downgrade;
- sensor `N -> T`;
- standard grease to low-temperature grease;
- DZ and LF brand may be candidates.
## Full Candidate Generation
- Keep outer diameter, structure, gear ratio, installation, communication, output shaft, and version unchanged.
- Generate the full Cartesian set across every allowed value for brake, encoder, sensor, grease, and DZ/LF brand.
- Encoder options are directional: `S -> M/HS/HM`, `M -> HS/HM`, `HS -> HM`, and `HM` has no further automatic upgrade.
- Include the original model separately as the baseline and deduplicate all generated models.
- Never generate a brake, encoder, sensor, or grease downgrade.
- Query every generated candidate through the controlled delivery-prediction script.
- Show candidates with usable lead-time summaries in full; summarize missing or unsupported models under `不可用候选`.
## Output
Use a vertical list with:
- model;
- candidate type;
- difference;
- recommendation reason;
- customer confirmation item;
- human review item.
Format the difference field as `字段 原值→新值`. Join multiple changes with the Chinese semicolon ``, for example:
```text
差异项:编码器 HS→HM品牌 DZ→LF
```