init: AI日报 pipeline 完整代码 + 技能文档 + 运行记录

2026-06-04 10:38:44 +08:00
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+---
+name: ai-daily-report-pipeline
+description: |
+  Maintain and operate the AI daily report cron pipeline (ai_daily_blog_pipeline.py).
+  Covers: script configuration, LLM prompt tuning, data sources, timeout settings,
+  output format, and publishing workflow.
+trigger:
+  - AI日报 / AI daily report / daily briefing
+  - ai_daily_blog_pipeline.py
+  - cron job 76297415d88d
+  - ai_morning_out / 橘鸦 / AI HOT
+---
+
+# AI Daily Report Pipeline
+
+Automated daily AI news digest that publishes to `blog.ephron.ren`.
+
+## Architecture
+
+- **Script**: `~/.hermes/scripts/ai_daily_blog_pipeline.py` (~1100 lines)
+- **Output dir**: `~/.hermes/scripts/ai_morning_out/`
+- **Cron job**: `76297415d88d`, schedule `0 10 * * *` (10:00 CST daily)
+- **Delivery**: `origin` (back to the chat that created it)
+- **Mode**: `no_agent: true` (script-only, no LLM wrapper)
+
+## Data Sources
+
+| Source | Type | Notes |
+|--------|------|-------|
+| AI HOT | API (aihot skill) | Primary, category-specific |
+| 橘鸦 AI 早报 | RSS (content:encoded) | Publishes ~09:34. Parsed from RSS `content:encoded` field (no article page fetch). 45s timeout. |
+| InfoQ AI | RSS | `feed.infoq.com/ai-ml-data-eng/` |
+| MIT 科技评论 AI | RSS | `technologyreview.com/topic/.../feed` |
+| 量子位 | RSS | `qbitai.com/feed` |
+
+## Processing Pipeline (4-Stage Architecture)
+
+Stages are designed to maximize script work, minimize LLM calls.
+
+### Stage 0: Script Dedup (pure Python)
+- Normalize titles: strip punctuation, lowercase, remove stopwords
+- Remove exact duplicates (title match or Jaccard > 0.85)
+- No LLM involved — deterministic
+
+### Stage 1: LLM Semantic Dedup
+- Single LLM call to find semantically equivalent items (e.g. same news from different sources)
+- Input: `{index, title, summary}` for each item
+- Output: `{duplicates: [{keep: 0, remove: [1,2]}, ...]}`
+- Removes less-detailed version of each duplicate pair
+
+### Stage 2: Parallel Summary Rewrite + Classify (2 concurrent LLM calls)
+- **Stage 2a**: Rewrite summaries + translate titles to Chinese
+  - Brand/model names preserved in English (GPT-5, Codex, etc.)
+  - Other title text translated to Chinese
+  - Summary: max 120 chars, concise Chinese
+  - Output: `{summaries: [{index, title, summary}, ...]}`
+- **Stage 2b**: Classify items into sections
+  - Sections: 模型与技术, 产品与工具, 开发与工程, 行业与公司, 研究与发现, 观点与评论
+  - Output: `{classifications: [{index, section}, ...]}`
+- Both run in parallel via `concurrent.futures.ThreadPoolExecutor`
+
+### Stage 3: LLM Guide Generation
+- Single LLM call for "今日观察" (observation/analysis)
+- Input: all item titles + summaries
+- Output: `{guide: [{type, text}, ...]}` JSON array
+- Types: `theme` (1), `strong` (2-3), `medium` (1-2), `risk` (1-2)
+- NO `advice` type
+
+### Stage 4: Script Assemble + Publish (pure Python)
+- Merge Stage 2a output (titles+summaries) with Stage 2b output (sections)
+- Assemble markdown: 导览 → 分类新闻 → 总结
+- Publish via Service API (create → publish → PATCH)
+
+## User Preferences (CRITICAL)
+
+- **NO CURATION / NO SELECTION**: Only filter exact duplicates. ALL non-duplicate items must be preserved. Do NOT use words like "精选" (curated/selected) in output. The user explicitly rejected any editorial filtering beyond deduplication.
+- **No emoji** in the output
+- **No reference numbers** like [1][3] — readers can't see what they point to. Strip all `[N]` from guide text via `clean_guide_text()`.
+- **No "主线判断：" prefix** in 导览 section — strip via regex `r'^主线判断[：:]\s*'` in `clean_guide_text()`.
+- **No advice/suggestions** section — no "开发者应该..." type content. Guide types are: theme, strong, medium, risk ONLY.
+- **Concrete not generic** — avoid vague statements like "行业焦点转向XX". Point to specific events.
+- **Plain language** — no academic/formal tone, use 大白话
+- **Concise** — each guide item 2-3 sentences max
+- **Readable formatting** — summary section uses type labels as headers, then bullet-list format:
+  ```
+  **强信号**
+  - **标题**
+    内容...
+  ```
+- Guide format: `[{type, text}]` JSON array. Types: `theme` (1), `strong` (2-3), `medium` (1-2), `risk` (1-2). NO `advice` type.
+- Structure: 导览 (blockquote, no prefix) → 新闻 → 总结 (type labels + bullet list, grouped by type)
+- Links must be verified accessible before inclusion
+
+## Key Configuration
+
+- **Cron timeout**: `cron.script_timeout_seconds: 600` (in `~/.hermes/config.yaml`)
+- **LLM urllib timeout**: 600s (in script, `urllib.request.urlopen(req, timeout=600)`)
+- **RSS fetch timeout**: 25s per regular feed. 橘鸦: 45s (GitHub Pages, 262KB RSS).
+- **LLM API**: follows the active Hermes model config by default. Current production path is Sub2API (`~/.hermes/config.yaml` → `model.provider: sub2api`, `model.default: findmini/gpt-5.5`, `model.base_url: http://sub2api.ephron.ren/v1`) with `SUB2API_API_KEY` from `~/.hermes/.env`. Keep API key, base_url, and model from the same provider family; do not mix `SUB2API_API_KEY` with Xiaomi/MiMo `base_url`, or the LLM stages will fail with 401 Unauthorized.
+- **max_items**: 30 in `_prefilter_items` — controls LLM prompt size; 38 items worked fine, 30 is conservative
+
+## Pitfalls
+
+1. **Config file is protected**: `~/.hermes/config.yaml` cannot be edited with `patch` tool. Use `sed -i 's/old/new/' ~/.hermes/config.yaml` via terminal.
+2. **橘鸦 timing**: Publishes ~09:34 CST. Script sleeps 120s if empty. Don't run before 10:00.
+3. **橘鸦 regex bug (fixed 2026-06-04)**: The `block_pattern` regex had `\\s*` (two backslashes in source = literal backslash in regex) before `<code>` instead of `\s*` (one backslash = whitespace class). This caused the regex to never match any 橘鸦 items, silently returning empty results. The `first_real_block` qwen-ID regex was also dead (site migrated away from Qwen IDs). **Fix**: (a) split into `fetch_juya_rss` + `parse_juya`; (b) parse from RSS `content:encoded` eliminating the second HTTP fetch; (c) changed escaped backslash to whitespace class; (d) changed `.*?` to `[^<]*?` to prevent overview section from leaking into matches (the overview `<h2>概览</h2>` has no `<code>#N</code>`, but the lazy `.*?` would cross the h2 boundary to find it).
+4. **橘鸦 timeout**: Now uses 45s timeout (up from 25s) because GitHub Pages can be slow and the RSS feed is ~262KB. Content is parsed from RSS `content:encoded` to avoid a second HTTP request for the article page. Falls back to fetching the article page if `content:encoded` is unavailable.
+5. **MiMo token limit**: With the 4-stage architecture, each LLM call handles a smaller prompt (dedup ~3K, summary ~6K, classify ~3K, guide ~5K). max_items=30 is safe. Old single-call approach needed max_items=18.
+6. **Gateway restart needed**: After config changes, `systemctl --user restart hermes-gateway` is required.
+7. **Timeout tuning (USER IS VERY SENSITIVE)**: User explicitly demands timeouts set to 1.5-2x of theoretical time. Being conservative causes repeated failures and user frustration. If theoretical time is ~80s, set timeout to 600s. Never start low and increment — go generous from the start. User said: "一直超时太影响体验了".
+8. **LLM prompt anti-patterns**: Never instruct LLM to "精选" (curate/select). Never ask for [N] reference numbers. Never include "建议" (advice) section. Never include "主线判断：" prefix in theme text. These all produce unwanted output.
+9. **Title translation**: Stage 2a MUST translate English titles to Chinese. Brand/model names (GPT-5, Codex, Gemini, etc.) are preserved in English. All other title text translated. If titles come back in English, check that the Stage 2a prompt includes explicit title translation instruction and the output format includes `"title"` field.
+10. **patch tool and regex**: The `patch` tool's Escape-drift detection can interfere with multi-backslash regex patterns. For complex regex changes in the pipeline script, use `terminal` with `sed -i` or a Python script that reads/writes the file directly.
+
+## Files
+
+- `run_meta.json` — last run metadata (date, slug, url, errors, source counts)
+- `raw_items.json` — raw fetched items
+- `llm_digest.json` — LLM output
+- `blog_markdown.md` — rendered blog post
+
+## References
+
+- `references/timeout-config.md` — timeout values and tuning rules for all script stages
+- `references/llm-config-auto-follow.md` — how the script auto-follows Hermes model config
+- `references/mimo-api-performance.md` — MiMo API performance characteristics
+- `references/rendering-guide.md` — blog post rendering rules
--- a/skill/references/llm-config-auto-follow.md
+++ b/skill/references/llm-config-auto-follow.md
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+# AI Daily Pipeline — LLM Config Auto-Follow (2026-05-30)
+
+## Problem
+The daily report script had hardcoded `XIAOMI_API_KEY` / `XIAOMI_BASE_URL` env vars. When the user switches Hermes' main model provider, the script would still use the old provider unless manually updated.
+
+## Solution: `resolve_llm_config(env)`
+Added to `ai_daily_blog_pipeline.py` (replaces hardcoded reads in `llm_call()`):
+
+```python
+def resolve_llm_config(env: dict):
+    """Read Hermes config to get the active provider's API key, base_url, and model."""
+    # 1. Read ~/.hermes/config.yaml → model.provider, model.base_url, model.default
+    # 2. Read ~/.hermes/auth.json → credential_pool[provider].source (e.g. "env:XIAOMI_API_KEY")
+    # 3. Resolve env var name → actual key from .env
+    # 4. Fallback to LLM_API_KEY / XIAOMI_API_KEY if auth.json lookup fails
+    return api_key, base_url, model_name
+```
+
+## Config Sources (priority order)
+1. `~/.hermes/config.yaml` → `model.provider`, `model.base_url`, `model.default`
+2. `~/.hermes/auth.json` → `credential_pool[provider][0].source` (format: `env:VAR_NAME`)
+3. `~/.hermes/.env` → actual key value
+4. Legacy fallback: `LLM_API_KEY` / `XIAOMI_API_KEY` / `LLM_BASE_URL` / `LLM_MODEL`
+
+## Usage
+When user runs `hermes config set model.provider=minimax`, the daily report script automatically uses MiniMax's API key and endpoint on the next run. No script changes needed.
+
+## Pitfall
+The script needs `import yaml` — ensure `PyYAML` is installed. It's available in the Hermes venv but may not be in system Python.
--- a/skill/references/mimo-api-performance.md
+++ b/skill/references/mimo-api-performance.md
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+# MiMo-v2.5-pro API Performance Profile
+
+Empirically tested on `https://token-plan-sgp.xiaomimimo.com/v1` (2026-05-29).
+
+## Latency by Prompt Size
+
+| Prompt Size | Items | Response Time | Status |
+|-------------|-------|---------------|--------|
+| ~500 chars | 1-2 | 2-4s | ✅ Reliable |
+| ~4,500 chars | 15 | ~73s | ✅ OK |
+| ~7,400 chars | 25 | >120s | ❌ Timeout |
+| ~10,900 chars | 35 | >120s | ❌ Timeout |
+| ~19,000 chars | 65-70 | >150s | ❌ Timeout |
+
+## Key Constraints
+
+- **Max reliable prompt size: ~5K chars / ~18 items** for structured output tasks
+- Output token generation is slow (~50-80 tokens/s for large JSON outputs)
+- Simple prompts (<1K) are fast and reliable (2-4s)
+- Latency is **highly variable** — same prompt can take 73s or timeout at 150s
+- Temperature 0.2 used for structured output consistency
+
+## Implications for Cron Jobs
+
+- **Pre-filter aggressively** before sending to LLM: dedupe + source priority + cap at 18 items
+- **Cron timeout 300s** budget: ~35s data fetch + ~80s LLM = ~115s typical, but retries can push to 250s+
+- Set LLM urllib timeout to **150s** (not 300s — it won't help, just wastes cron budget)
+- **Retry 2x max** (not 3x) to stay within 300s cron budget
+- If LLM consistently times out, check if API is rate-limited (test with simple prompt first)
+
+## Workaround: Pre-filter Pattern
+
+```python
+def _prefilter_items(raw_items, max_items=18):
+    """Dedupe + prioritize before LLM call."""
+    seen = set()
+    filtered = []
+    priority_sources = {'AI HOT': 1, '橘鸦AI早报': 1, 'InfoQ AI': 2, '量子位': 2}
+    sorted_items = sorted(raw_items, key=lambda r: priority_sources.get(r.get('source_group', ''), 3))
+    for item in sorted_items:
+        norm = re.sub(r'[^\w\u4e00-\u9fff]+', '', item['title_raw'].lower())
+        if not norm or len(norm) < 3 or norm in seen:
+            continue
+        seen.add(norm)
+        filtered.append(item)
+        if len(filtered) >= max_items:
+            break
+    return filtered
+```
+
+## Alternative Providers (tested same day)
+
+- **Findmini (gpt-5.4)**: `https://api.findmini.top/gpt/v1` — returned 503
+- **OpenRouter (free models)**: returned 429 rate limit
+- **MiMo small prompts**: consistently 2-4s, reliable for simple tasks
--- a/skill/references/rendering-guide.md
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+# Rendering & Guide Formatting Reference
+
+## `clean_guide_text(text)` function (in `blog_markdown()`)
+
+Strips unwanted artifacts from LLM-generated guide text:
+
+```python
+def clean_guide_text(text):
+    # Strip all [N] reference numbers
+    text = re.sub(r'\[\d+\]', '', text)
+    text = re.sub(r'\[N\]', '', text).strip()
+    # Strip "主线判断：" prefix
+    text = re.sub(r'^主线判断[：:]\s*', '', text)
+    # Clean extra whitespace
+    text = re.sub(r'\s+', ' ', text).strip()
+    return text
+```
+
+## Summary section rendering
+
+Type labels map: `{'strong': '强信号', 'medium': '中信号', 'risk': '待验证'}`
+
+Output format per type group:
+```
+## 总结
+
+**强信号**
+
+- **标题（从text第一句提取）**
+  解释内容...
+
+- **标题**
+  解释内容...
+
+**中信号**
+
+- **标题**
+  解释内容...
+
+**待验证**
+
+- **标题**
+  解释内容...
+```
+
+Title extraction logic:
+1. Try splitting on `：` or `:` — if prefix < 60 chars, use as title
+2. Otherwise, split on `。！？` and use first sentence as title
+
+## Title translation (Stage 2a)
+
+Titles are translated from English to Chinese in Stage 2a. Rules:
+- Brand names preserved: GPT-5, Codex, Gemini, OpenAI, Meta, etc.
+- Technical terms with no good Chinese equivalent: keep English
+- Everything else: translate to natural Chinese
+- LLM prompt explicitly states: "英文品牌名/模型名保留原样，其余翻译为中文"
+
+## LLM prompt for guide (as of 2026-05-30)
+
+Key instructions to LLM:
+- 不要空泛总结（如"行业焦点转向XX"），要指向具体事件
+- 不要引用编号如[1][3]，读者看不到对应关系
+- 不要建议（"开发者应该..."之类删掉）
+- 每条控制在2-3句话以内
+- 用大白话，不要学术腔
--- a/skill/references/timeout-config.md
+++ b/skill/references/timeout-config.md
@@ -0,0 +1,34 @@
+# Timeout Configuration Reference
+
+## Timeout Locations
+
+| Setting | Location | Current Value | Notes |
+|---------|----------|---------------|-------|
+| Script total timeout | `~/.hermes/config.yaml` → `cron.script_timeout_seconds` | 600s | Max time for entire script execution |
+| LLM urllib timeout | `ai_daily_blog_pipeline.py` → `llm_call()` → `urlopen(timeout=...)` | 600s | Single LLM API call timeout |
+| RSS fetch timeout | `ai_daily_blog_pipeline.py` → `fetch_text()` → `urlopen(timeout=...)` | 25s | Per-RSS-feed fetch |
+| 橘鸦 RSS timeout | `ai_daily_blog_pipeline.py` → `fetch_juya_rss()` → `urlopen(timeout=...)` | 45s | GitHub Pages can be slow; 262KB RSS |
+| 橘鸦 fallback page timeout | `ai_daily_blog_pipeline.py` → `parse_juya()` → `urlopen(timeout=...)` | 45s | Only used if content:encoded unavailable |
+| Service API timeout | `ai_daily_blog_pipeline.py` → `blog_api_request()` → `urlopen(timeout=...)` | 25s | Blog publish API call |
+| 橘鸦 wait timeout | `ai_daily_blog_pipeline.py` → sleep(120) | 120s | Wait if 橘鸦 RSS is empty |
+
+## Timeout Tuning Rules
+
+1. **Always set generously** — user explicitly wants 1.5-2x theoretical time minimum
+2. **MiMo API is slow** for long prompts — 18 items with 600s timeout works; 30+ items times out even at 600s
+3. **Config file is protected** — use `sed -i` via terminal, not `patch` tool
+4. **Gateway restart required** after config changes: `systemctl --user restart hermes-gateway`
+
+## Theoretical Timing
+
+- Script without LLM: ~10-15s (fetch + parse + publish)
+- LLM call (18 items): ~60-120s typically, can spike to 300s+
+- Total theoretical: ~80-150s
+- Recommended timeout: 600s (generous, accounts for API variability)
+
+## If Timeout Still Occurs
+
+1. Check `run_meta.json` → `llm_error` field
+2. If `TimeoutError: The read operation timed out` → LLM API is slow
+3. Check if `max_items` was increased — more items = longer LLM time
+4. Consider reducing `max_items` in `_prefilter_items()` back to 18