ModelSieve（模型筛）：供应商中立的模型情报与写回层

ModelSieve · 模型筛 / Model Intelligence & Writeback Layer

中文：供应商中立的「模型情报 / 策略顾问层」——情报聚合 + 分档推荐 + 写回适配器。监测全网模型质量/价格漂移，按任务域给三档推荐，可解释、可回退、零配置。

EN: A vendor-neutral model-intelligence / strategy-advisor layer — intelligence aggregation + tiered recommendation + writeback adapters. Tracks quality/price drift across the model landscape and recommends three tiers per task; explainable, reversible, zero-config.

为什么存在 · Why it exists

中文：过去两年有百余款前沿和可用模型发布，同一任务的有效成本差可达 30×。但没有任何一个供应商中立的工具告诉你：今天哪个模型在你的任务上最优质？哪个性价比最高？哪个最便宜能兜底？更没有人把答案写回你已在用的工作流（Dify / LangGraph / n8n / otari）。

EN: Over the past two years there have been over a hundred frontier and usable model releases; effective cost for the same task can differ by 30×. Yet no vendor-neutral tool tells you: which model is best quality for your task today? which has the best value? which is cheapest but still works? And nobody writes the answer back into the workflow you already use (Dify / LangGraph / n8n / otari).

ModelSieve 只做两件事 · ModelSieve does only two things: ① 情报层（雷达）· intelligence layer (radar)；② 写回适配器 · writeback adapters。网关/路由的 plumbing 直接骑在 otari（Apache-2.0）等开源底座上，不重复造网关 · gateway plumbing rides on open-source bases like otari; the gateway is not rebuilt.

三档推荐 · Three tiers

档位 Tier	含义 Meaning	典型用途 Typical use
`best_quality`	当前质量上限最高的模型 · highest quality ceiling	关键任务、对质量要求高的生成 · critical / high-quality generation
`best_value`	质量/价格性价比最优 · best quality-per-dollar	日常主力、规模化调用 · daily workhorse, scale calls
`cheapest`	价格最低、能兜底 · lowest cost, still works	高容错、批量、内部 · tolerant / batch / internal

架构 · Architecture

OpenRouter /api/v1/models  ──▶  radar/snapshot.py (统一每日入口)  ──▶  site/daily-radar-live.{html,md,json}
                                        │  ├─ 解析 + 三档分层                                      │
                                        │  ├─ quality_aa.py (AA 真实质量分, 启发式兜底)             ▼
                                        │  └─ 缓存快照 data/snapshots/日期.json        adapters/<target>_writeback_adapter.py
                                        │                                                    │
                              radar/diff.py (跨日漂移对比) ◀── 快照历史                        ▼
                                                                          Dify / LangGraph / n8n / otari  model-config

情报层（开源）· Intelligence (OSS)：radar/snapshot.py 拉取实时价格与（可选）真实质量分，输出三档并缓存快照；radar/diff.py 做跨日漂移对比。
写回层（开源）· Writeback (OSS)：adapters/{dify,langgraph,n8n,otari} 把三档映射成各平台 model-config，默认 dry-run，可 --apply 实跑。
托管报告（免费）· Hosted report (free)：GitHub Actions 每日生成 → GitHub Pages 自动更新（CI 已写好，发布步骤见 launch/LAUNCH.md）。
推荐技能（开源）· Recommendation skill (OSS)：skill/modellens/ 聚合多源情报（OpenRouter/AA/LMArena/社区）分档推荐，详见下方「按需推荐技能」。

按需推荐技能 · On-demand recommendation skill

中文：skill/modellens/ 是一个推荐技能。用户提出「推荐个模型 / 哪个性价比最高 / 不考虑钱用哪个」时，它自动聚合多平台、论坛与权威测试源（价格、Artificial Analysis 智能指数、LMArena 人类偏好、LiveBench、r/LocalLLaMA 等）的情报，按档位推荐，并以易读形式（表格 + 一句话理由 + 来源链接）呈现。数据源明细见 skill/modellens/references/sources.md，输出模板见 references/output-template.md。

EN: skill/modellens/ is a recommendation skill. When asked "recommend a model / best value / best regardless of cost", it aggregates intelligence from platforms, forums, and authoritative benchmarks (prices, Artificial Analysis Intelligence Index, LMArena human preference, LiveBench, r/LocalLLaMA…), recommends by tier, and presents it in a readable form (table + one-line rationale + source links). Source details: skill/modellens/references/sources.md; output template: references/output-template.md.

安装 · Install: 把 skill/modellens/ 复制到所用平台的技能目录（用户级或项目级）即可。· Copy skill/modellens/ to the skill directory of the platform in use (user-level or project-level).

本地运行 · Local run

# 1. 生成报告 + 快照（联网；或用 --offline 走缓存，完全离线）
#    Generate report + snapshot (online; use --offline to run fully offline via cache)
python radar/snapshot.py --json site/daily-radar-live.json --html site/daily-radar-live.html
#    （离线示例 · Offline example）python radar/snapshot.py --offline --html site/daily-radar-live.html

# 2. 跨日漂移对比（默认取 data/snapshots 最近两份）
#    Cross-day drift diff (defaults to the two most recent snapshots in data/snapshots)
python radar/diff.py --md data/drift.md

# 3. 写回适配器（dry-run 默认，不调用任何远程 API）
#    Writeback adapters (dry-run by default; makes no remote API calls)
python adapters/dify/dify_writeback_adapter.py --radar data/sample-radar-report.json --tier best_value --app-id DEMO --dry-run
python adapters/langgraph/langgraph_writeback_adapter.py --radar data/sample-radar-report.json --tier best_quality
python adapters/n8n/n8n_writeback_adapter.py --radar data/sample-radar-report.json --tier best_value
python adapters/otari/otari_writeback_adapter.py --radar data/sample-radar-report.json --tier cheapest

# 4. 测试 & 本地预览
#    Tests & local preview
python -m pytest tests/ -q
python serve.py   # 打开 http://localhost:8000/  · open http://localhost:8000/

# 5. 任务域细分档位（P2）：每个任务域独立三档 → site/domains.json
#    Per-task-domain tiers (P2): independent three tiers per domain → site/domains.json
python radar/snapshot.py --offline --domains site/domains.json

# 6. 历史漂移可视化（P2）：生成自包含 SVG 趋势图（需 ≥2 份历史快照）
#    Historical drift viz (P2): self-contained SVG trend chart (needs ≥2 historical snapshots)
python radar/visualize.py --out site/drift-chart.svg

四个写回适配器（dify / langgraph / n8n / otari）均已交付并验证 · all four writeback adapters are delivered and verified.

质量分说明 · On quality scores

中文：开源版默认使用启发式质量分层（基于模型家族/规模的经验估计，非权威 benchmark，页面已标注）。接 Artificial Analysis 真实质量分后，作为 Premium 能力（详见 docs/quality.md）。每个分数都可解释、可回退——选择某个模型的理由随时可查，且能一键改回。
EN: The OSS build defaults to a heuristic quality tier (experience-based on family/scale, not an authoritative benchmark, labeled on the page). Real scores from Artificial Analysis become a Premium capability (see docs/quality.md). Every score is explainable and reversible — the rationale for choosing a model can be inspected at any time and reverted in one step.

贡献 · Contributing

Apache-2.0，欢迎 PR。重点白空间：新适配器、新数据源、质量分校准。· Apache-2.0, PRs welcome. White space: new adapters, new data sources, score calibration.

许可证 · License

Apache-2.0（权威文本为英文；中文说明见 LICENSE.zh.md）。内核开源、可审计、可 self-host。· Apache-2.0 (authoritative text in English; Chinese summary in LICENSE.zh.md). Core stays open, auditable, self-hostable.