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

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.

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

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

skill/references/timeout-config.md

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+# 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