source-intel-recommender

name	source-intel-recommender
description	Recommend high-depth sources for broad and edge-case learning (including adversarial SEO/security perspectives), using Karpathy's 92 feeds and curated deep GitHub source maps. Use when user asks for 信源推荐, 边缘玩法研究入口, 深水论坛/论文/情报入口, or wants predicted knowledge value.

Source Intel Recommender

When To Use

Use this skill when the user asks:

• 针对某个问题推荐高价值信源
• 想要更广视野，尤其是边缘/对抗思路
• 先看什么、后看什么最有效
• 这些来源可能带来什么知识价值

Inputs

• User question and constraints (time, language, risk appetite, build stage)
• references/karpathy-92-hn-2025.tsv
• references/deep-sources-github.tsv
• references/deep-sources-telegram.tsv (optional dynamic pool)

Workflow

1. Detect query focus tags (SEO/PSEO/GEO, adversarial, OSINT, security, research, automation, commerce).
2. Build candidate pool:

• Karpathy 92 long-form feed list (depth baseline)
• Deep GitHub source maps (entry points for high-signal communities and docs)
• Telegram channel pool (fast-moving adversarial/community signals)

3. Score each source on 1-5:

• Relevance: fit to query intent
• Depth: insight density / first-principles value
• Actionability: can turn into testable workflow quickly
• Freshness: likely still useful now
• Consequence: potential downside if copied blindly (higher score = better controllability)

4. Compute KnowledgeValue:

• KnowledgeValue = 0.30*Relevance + 0.25*Depth + 0.25*Actionability + 0.10*Freshness + 0.10*Consequence

5. Output top recommendations:

• Why source fits
• What likely value it unlocks
• Reading order (fast execution -> deep method -> adversarial counterexample)
• Potential consequences (account risk, ranking volatility, reputation, legal exposure) when relevant
• Missing list types needed to further expand deep-zone coverage

Output Format

1. 问题标签:
2. 推荐信源 Top N:

• <source> | KnowledgeValue: x.x/5 | 预计知识价值:
• ...

3. 优先阅读顺序: <3-5 items>
4. 可能后果: <only when query has edge/adversarial intent>
5. 建议补充列表类型: <1-5 precise categories>
6. 实验建议: <1 action in 24h>

Heuristics

• Prefer high-signal entry maps over random tactic posts.
• For adversarial topics, explain likely consequences instead of only binary prohibition wording.
• Mix at least one deep methodology source and one executable source map.