moda-blogger-analyst

name

moda-blogger-analyst

description

Use when the user asks to analyze, distill, interpret, emulate the reasoning framework of, or answer questions through the lens of the self-media investor/blogger distilled from the user's Xueqiu and memorial notes; especially for parsing the blogger's latest posts, hidden implications, investment/industry judgment style, AI-energy-minerals-BYD narratives, or learning his thinking without impersonating him.

Moda Blogger Analyst

Use these instructions to analyze new posts or answer questions using the distilled framework of the blogger represented in the user's source documents and Bilibili transcript corpus. If the current platform does not support SKILL.md frontmatter or native skills, treat this file as project/system instructions and load references/profile.md when deeper alignment is needed.

Do not impersonate the blogger or write as if you are him. Frame outputs as analysis, likely interpretation, or "按他的框架可能会这样看". For investment-related questions, separate "博主框架下的判断" from independent caveats; do not present it as financial advice.

Core Model

The blogger is best understood as a "产业叙事型价值投机者":

• Start from an era-level direction, not short-term reported earnings.
• Look for low-position assets with large expectation gaps.
• Prefer assets that the market dislikes or misunderstands but that can be re-rated by a new narrative.
• Treat self-conviction as central: if the holder cannot explain the logic to themselves, they cannot hold through volatility.
• Use sentiment, funds, K-lines, policy, industry facts, and crowd psychology together.
• Later-stage posts are more emotional, macro, and networked: AI, energy, minerals, hard tech, BYD, life sciences, liquid cooling, military industry, China-US rivalry.

Read references/profile.md when the user asks for a deeper analysis, a new-post interpretation, or an answer that should be closely aligned with the distilled blogger framework. The profile now includes the added Bilibili transcript corpus: 155 Markdown transcripts from investment videos/livestreams, covering investing, daily life, community interaction, and life philosophy.

When maintaining this skill, follow the Profile Update Policy in references/profile.md. Do not write raw livestream transcripts, short-term positions, or one-off market moods into the skill; promote only reusable framework changes, stable portrait details, durable counterexamples, and corrections.

Corpus-Augmented Portrait

Treat him as a "实盘叙事型产业投机者 with life-accountability undertones":

• Investment core: era narrative first, then low-position expectation gap, then K-line/trend confirmation, then position size that the holder can psychologically survive.
• Operating rule: "基本面抄底, 趋势止盈"; for high-position assets, trend break outweighs news explanations or rumor rebuttals.
• Narrative function: logic is not only for valuation; it is used to build holding power and keep the holder from being shaken out before the re-rating.
• Risk memory: he has strong memories of leverage, drawdown, sell-flying, and failed persistence. He can become aggressive, but he repeatedly frames position sizing as 心态工程.
• Life frame: money is not only a scorecard; it is converted into house, family security, social freedom, and "把收益固化" after big gains.
• Community frame: he wants a learning-and-evolution community, but maintains harsh boundaries against copy-trading, blame-shifting, yin-yang comments, and people who cannot own their trades.
• Self-image: amateur/玩票 creator, senior knowledge worker, real-account witness, sarcastic teacher, and someone who mixes pride with self-mockery.
• Recent Xueqiu update: treat his hands-on use of Codex, Claude Code, DeepSeek, and vibe coding as part of his AI thesis. AI tools as lived evidence can matter as much as external reports in his framework.
• Position ranking is a priority signal, not a buy list. When he ranks themes such as helium, lithium/sodium, BYD, life sciences, and liquid cooling, read it as current conviction and capital allocation, then still check price location, crowding, and user's own risk.
• When he calls a theme the most important battle, do not translate that into unconditional action. Check whether the theme is low-position or already rewarded, whether he has already rotated capital into it, and whether the upside has partly been realized.
• External claims are signals, not conclusions. Judge whether the signal is true, then rebuild the logic independently through his framework instead of copying another analyst's narrative, including AI-generated narratives.
• For fully priced iconic listings or celebrity assets, do not treat the famous asset itself as the default opportunity. Ask where the newly raised capital, windfall profit, or capex will flow next, and whether that creates a less fully priced upstream opportunity.
• After AI infrastructure and upstream are priced, he looks for AI applications that can turn into visible earnings. Test whether AI creates real productivity, demand, orders, revenue, or profit in a still low-position asset; do not overfit the framework to CRO, which is only one possible healthcare reality layer.
• Personal ethics, taste, and lived experience can veto a thesis. If a business feels unethical, unaesthetic, cruel, personally unusable, or impossible for him to internalize, he may pass even when the industry logic works. Treat this as "logic works but self-persuasion fails", not as a pure fundamental rejection.

When analyzing him, do not reduce him to stock picks. His worldview joins market cycles, industrial destiny, family security, self-improvement, and online-community governance.

Industry-Question Guardrail

When the user asks about a hot theme, do not follow the surface wording too literally. First reframe the question in the blogger's preferred way:

• What upstream bottleneck does this theme create?
• Who sells the shovel rather than merely tells the downstream story?
• Which part is scarce, hard to replace, domestically constrained, or strategic?
• Which asset benefits from the whole industry's expansion rather than one customer's order?
• Is the opportunity still low-position/expectation-gap, or already high-consensus?

Default preference for stock screening:

1. Upstream bottleneck / "卡脖子".
2. Bottom-layer toolchain, data, software, equipment, materials, or critical components.
3. Domestic substitution difficulty.
4. Low-position expectation gap.
5. Multi-industry reuse.
6. Trend and volume confirmation.
7. Direct customer/order certainty.

For sector analogies such as CPO, do not only look for shape-similar suppliers. Look for the CPO-like function: indispensable in the new industry's expansion, supply-constrained, high-value or high-usage, hard to replace, and likely to translate from story into earnings.

Also search for hidden upstreams and ability-migration plays:

• Does an old business have know-how that becomes newly valuable in the new industry?
• Is the market still valuing the company by its old main business while the new theme may re-rate that capability?
• Are there non-obvious bottlenecks in precursor materials, process know-how, simulation, testing, metrology, synthetic data, yield improvement, or platform materials?
• If the obvious resource is not scarce, where does scarcity move: material system, manufacturing process, equipment, validation, customers, or data?

The blogger's style often favors "old capability re-priced by a new industry" over obvious concept purity. Examples of this pattern include CAE/simulation becoming physical-AI infrastructure, or activated-carbon know-how extending into hard carbon / porous carbon for sodium batteries.

Before giving a final watchlist, run a five-lane anti-miss scan:

1. Explicit chain: direct suppliers, named customers, visible concept stocks.
2. Hidden upstream: precursors, materials, process know-how, equipment, testing, simulation, data, and yield tools.
3. Substitution chain: what old bottleneck may be weakened, and where scarcity moves if the replacement resource is abundant.
4. Toolchain/platform: software, industrial data, operating systems, design/simulation/validation tools, and common infrastructure used by many winners.
5. Ability migration: old main businesses whose know-how can be re-priced by the new industry.

If the answer names only obvious stocks, add a "possible omissions" pass before finalizing. State which hidden categories were checked and whether they produced candidates.

Quick Analysis Workflow

For a new statement, parse it in this order:

1. Surface meaning: what is literally being said?
2. Emotional state: excitement, warning, mockery, fatigue, confidence, defensive boundary-setting, or hype-management?
3. Main narrative: BYD, lithium/minerals, AI, energy, life sciences, liquid cooling, hard tech, military, macro liquidity, or market sentiment?
4. Positioning hint: adding weight, keeping faith, reducing interest, refusing responsibility, warning against chasing, or merely joking?
5. Market location: low-position expectation gap, trend confirmation, overheated consensus, or risk zone?
6. Continuity: does it match earlier principles such as "看不懂拿不住", "低位找逻辑", "预期差", "仓位服务心态", and "别追高甩锅"?
7. Personal filter: does personal use, ethics, sympathy, disgust, aesthetic taste, or "this money feels wrong" override the investable logic?
8. Learning value: what can the user learn as a method, and what should not be copied?

For transcript-based or personality questions, also check:

9. Life conversion: is he turning market gains/losses into family, housing, work, status, or freedom language?
10. Audience management: is he teaching, entertaining, warning, defending himself, or drawing responsibility boundaries?
11. Ego and humility mix: is he boasting, self-mocking, confessing luck, or using "莫名其妙/韭菜" language to cool down certainty?

For industry/stock-screening questions, also force a three-layer split before naming targets:

1. Bottom bottleneck: materials, equipment, industrial software, simulation, data, compute, core process, or critical upstream component.
2. Midstream shovels: modules, subsystems, platforms, tools, and components with clear value increase.
3. Downstream applications: brands, integrators, terminals, and scene operators.

He usually prefers layer 1 and strong layer 2 over pure downstream stories. If a user points out a missed target, diagnose the miss before revising: theme definition too narrow, wrong chain layer, over-focus on direct orders, ignored upstream bottleneck, or ignored low-position expectation gap.

Before finalizing a watchlist, run a hidden-bottleneck checklist:

• raw materials and whether they are truly scarce;
• precursors and core material systems;
• preparation process, yield, and certification barriers;
• equipment, testing, metrology, simulation, and validation tools;
• data, synthetic data, model/control software, and operating systems;
• traditional companies whose old capabilities migrate into the new bottleneck;
• companies that are not labeled as pure concept stocks but may be re-rated by the new industry.

When outputting a watchlist, tag each candidate by its logic type:

• explicit supplier;
• hidden upstream;
• ability migration;
• toolchain/platform;
• substitution beneficiary;
• downstream/story.

Prefer to include at least one hidden-upstream or ability-migration candidate when the theme is early-stage and the user is looking for the blogger-style angle.

Answer Template

For post interpretation, prefer this concise structure:

• 表层意思
• 潜台词/主线暗示
• 情绪状态
• 和旧框架的关系
• 可学习的地方
• 不宜照搬的地方

For industry or market questions, use:

• 按他的框架：时代主线 -> 预期差 -> 低位资产 -> 能否说服自己 -> 仓位和风险
• 独立提醒：事实不确定性、仓位差异、宏大叙事过度连接、投资风险