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Serenity Aleabitoreddit

Community
yan-labs
serenity-aleabitoreddit

Apply trader Serenity's (@aleabitoreddit) AI/semiconductor supply-chain analytical lens to US-stock ideas and market judgment. Use this skill whenever evaluating a stock decision (buy / sell / hold / size); forming an outlook on any AI, semiconductor, optical/CPO, memory, power/grid, or neocloud name; mentioning any ticker in Serenity's universe (NBIS, AXTI, LITE, SIVE, COHR, AAOI, IREN, CRWV, MU, SNDK, NVDA, TSM, MRVL, AVGO, INTC, SOI, IQE, TSEM, CIFR, XLU, VST, CEG, EWY, etc.); asking "what would Serenity think", "is this a real bottleneck", or wanting a supply-chain / bottleneck read on a thesis. Decision-support only — never auto-trades and never places or cancels orders.

Overview

Publisheryan-labs
Repositoryserenity-aleabitoreddit
Skill nameserenity-aleabitoreddit
Stars
485
Forks
85
Bundled files
12
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 12 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by yan-labs on GitHub. Read the source before you install it.

Installation

Install the Serenity Aleabitoreddit AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/yan-labs/serenity-aleabitoreddit/tree/main/serenity-aleabitoreddit
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/yan-labs/serenity-aleabitoreddit.git /tmp/serenity-aleabitoreddit
mkdir -p .claude/skills
cp -r /tmp/serenity-aleabitoreddit/serenity-aleabitoreddit .claude/skills/serenity-aleabitoreddit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Serenity Aleabitoreddit in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Serenity Aleabitoreddit on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Serenity Aleabitoreddit is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Serenity Supply-Chain Lens (@aleabitoreddit)

⚠️ STEP 0 — ALWAYS REFRESH BEFORE USE. This skill goes stale within ~30 minutes. Its theses, track record, and analysis are regenerated from @aleabitoreddit's live feed roughly every half hour, so the copy on disk is very likely out of date by the time you read it. Before reading any references/*.md / analysis/*.md file or giving any view, pull the latest version first:

bash
skills update serenity-aleabitoreddit -y    # auto-detects project vs global scope

Then read the refreshed reference files and proceed. Only if the update genuinely fails (offline / network error / command missing) should you fall back to the cached copy — and if you do, say explicitly that the data may be stale.

A reusable analytical lens distilled from 6,720 tweets (2025-07 to 2026-10) plus 4 long-form X Articles (2026-01 to 2026-05) by Serenity / @aleabitoreddit — an AI-and-semiconductor supply-chain analyst and trader (~500k followers, ~40k subscribers, ex-Reddit/WSB). Use it to pressure-test US-stock ideas and to reason about the AI/semi supply chain the way he does.

Decision-support lens, NOT financial advice and NOT an auto-trader. See "Risk & disclaimer framing" below. Always confirm current prices and fundamentals yourself — theses decay, and his returns are self-reported and unverified.

The raw tweet archive this lens was built from lives at the repo root in data/ (aleabitoreddit_tweets.json / .csv); the period-by-period distillation is in analysis/.


Who Serenity is and what his edge is

He hunts mispriced upstream supply-chain bottlenecks before institutions price them in. The mental model: don't buy the obvious "shovel seller" (NVDA) — trace the supply chain as far upstream as possible and find the single point of failure that a hyperscaler will pay anything to keep flowing.

His representative chain:

hyperscaler capex (GOOGL/MSFT/META/AMZN) → ASICs/TPUs → optical transceivers (LITE/AAOI/COHR) → InP epiwafer (IQE) → InP substrate (AXTI/Sumitomo) → InP feedstock (indium, Vital Materials).

The further upstream and the smaller the market cap, the more underpriced the chokepoint tends to be relative to the trillions flowing downstream. His biggest distilled calls — AXTI, SIVE, SOI, LITE, SNDK, the XLU power trade — all came from this multi-hop "OSINT BOM mapping" process.

He layers several other lenses on top: a Mag7-customer-concentration filter, signed-contract ARR vs. market-cap mismatch, a GAAP-margin war (real margins vs. cherry-picked non-GAAP), dilution/ATM as a disqualifier, a financing-quality spectrum for neoclouds, and macro overlays (rate cuts, tariff shocks, war). Full detail in references/methodology.md.

Independent calibration (2026-05-27 recheck): his true trading win rate is not independently knowable without broker statements, option-contract history, position sizes, and full loser disclosure. A local re-score of dated public calls using Yahoo Finance adjusted-close data found about 61% 30-day directional accuracy (30/49), 41% strict 30-day +10%/-10% hits (20/49), and 54% with a 20%+ favorable close within 60 days (29/54). Mature, externally checkable theses score better: roughly 65-75% of mature theses were at least partly validated by later price/fundamental evidence, and his strongest AI photonics / CPO / InP / memory bottleneck subset looks closer to 75-85%. Treat those as rough calibration bands, not a replicable trading return.

Signal timing calibration (2026-06-11 recheck): his best supply-chain posts are usually not reliable 1-day copy-trade signals. The repeatable pattern is often 5-60 trading days from public thesis to market validation, as company orders, media coverage, institutional ownership, or local-market attention catch up. Same-day or same-week reactions are more likely only when the post combines (1) a fresh, underpriced bottleneck, (2) a concrete external catalyst such as an order, filing, policy item, or named customer path, and (3) a small/illiquid equity where local media or retail attention can move the float. Treat late mainstream validation, victory laps, and broad supplier lists as research inputs, not fresh entry signals.

Important caveat: he trades volatile micro/small-caps that move 20%+ a day, runs ~1.25–1.5x margin, and self-reports very high YTD returns (237% in Feb 2026, later 4502.45% YTD on May 26). Those numbers are unverified and carry obvious survivorship / selection bias. Treat his lens as a source of questions to ask, not signals to copy.


How the reference files are organized

Read progressively — pull in only what the task needs.

FileWhat it isRead it when
references/methodology.mdHis framework as ~12 named, transferable principles + a checklist you can run on any new nameEvaluating how he thinks, or vetting any ticker (even one he never covered)
references/theses.mdPer-ticker knowledge base, merged across all periods, grouped by sub-sector, with conviction tier + how it evolved + latest stanceLooking up his actual view on a specific name
references/articles.mdCompact summaries and durable portfolio-use rules from his long-form X Articles, without redistributing full article textChecking whether a thesis has article-level backing, especially SIVE, AXTI/materials, robotics/rare earths, or crypto-policy risk
references/track-record.mdChronological timeline of his dated calls + an honest calibration note on what worked, what reversed, and the selection-bias caveatDeciding how much to weight his opinion
references/maintenance.mdRules for incrementally distilling new posts into the smallest useful skill/reference updateMaintaining this skill from fresh X posts
analysis/*.mdThe six period analyses the lens was synthesized from (provenance)Going deeper than the merged knowledge base, or auditing a claim

Workflows

(a) Evaluate one ticker through his lens

  1. Look the ticker up in references/theses.md. If present, note his stance, conviction tier, how it evolved, and his latest known view. Flag if his view reversed (e.g. IREN, CRWV, POET).
  2. If the ticker or theme appears in references/articles.md, treat that as higher-context long-form backing, but still distinguish public evidence from inferred customer paths.
  3. If he never covered it, run the checklist at the bottom of references/methodology.md — apply his principles to a fresh name.
  4. Sanity-check timeliness: his theses are dated. Anything older than a couple of months may have decayed — say so, and confirm current price/fundamentals.
  5. Weight his opinion using references/track-record.md and the calibration bands above: his bottleneck theses deserve more weight than event trades, old flipped stances, or self-reported options images.
  6. Present: his view, the supply-chain read, the bull/bear case, and the risks — framed as analysis, never as an order to place.

(b) Review a portfolio or watchlist against his views

  1. Take the list of tickers the reader provides (their holdings, a watchlist, a sector basket).
  2. For each name, pull his view from references/theses.md and bucket into:
    • Agreements — he is bullish on it.
    • Conflicts — he is bearish/cautious on it (surface his dated reasoning).
    • Gaps — his high-conviction names absent from the list (e.g. the photonics/CPO chain: SIVE/LITE/COHR/AAOI/SOI/AXTI/TSEM/IQE; NBIS among neoclouds; SNDK for memory).
  3. Check references/articles.md for long-form article support. Article-backed signals should raise discussion priority only when they also fit the user's risk budget, liquidity, and execution constraints.
  4. Produce a prioritized discussion list. Keep it advisory; never generate, place, or cancel a trade order.

(c) Form a forward sector view

  1. Identify which of his thematic threads the question touches: photonics/CPO, memory/HBM supercycle, neocloud financing quality, power/grid, defense, AI-agent hardware, "not-disrupted-by-AI" software.
  2. Pull the relevant theses and thread summaries from references/theses.md.
  3. Note his leading indicators (hyperscaler capex guidance, TSM projections, SMM 7N indium price, GPU availability, DRAM/NAND spot pricing).
  4. State the view with his confidence level and the dated evidence behind it, plus what would invalidate it.

(d) Decide whether fresh posts imply an investment window

Use this when the user asks whether his recent posts, a cluster of X threads, or a market pullback create a current buying window.

  1. Pull the latest skill data first, then inspect the fresh post text, replies, quotes/search echoes, and the matching entries in references/theses.md and references/track-record.md.
  2. Classify the post type before talking about execution:
    • New bottleneck thesis: a fresh, underpriced supply-chain dependency with named customers, capacity constraints, or policy/filing evidence. Highest weight, but still requires price and risk checks.
    • Reaffirmation / buy-the-dip: he already owns or has high conviction, and the pullback appears tied to a false report, mechanical selloff, ATM overhang, or misunderstood earnings. Medium-high weight if external checks support his mechanism.
    • Supplier map / watchlist / no-position idea: useful for research and future watchlists, but not a standalone buy signal.
    • Victory lap / mainstream validation: confirms an old thesis worked; it often means the easy mispricing has already compressed. Do not treat it as a new entry unless valuation and positioning reset.
  3. Compare the fresh signal against historical analogs from track-record.md. Focus on whether the setup matches the mechanism of prior winners, not just whether the ticker/theme is adjacent. Many past winners initially chopped or fell before 20-60 day validation.
  4. Check the market window separately from the stock thesis: broad breadth, sector uptrend participation, theme crowding, macro/event risk, and whether leadership is broadening or narrowing. A strong Serenity thesis inside a weak or narrow market is a selective/left-side window, not a green light.
  5. Convert the result into an action discipline, not an order: maintain existing high-priority limit ladders, lower trigger prices, or add a watch rule when the setup is early; avoid near-price chasing when the post is only a supplier list, late validation, or social-media heat.

Risk & disclaimer framing (state this when giving any view)

  • Self-reported, unverified returns. His YTD figures, from 237% in Feb 2026 to 4502.45% on May 26, are his own images. No independent verification exists.
  • Estimated public-call calibration, not trading proof. A 2026-05-27 recheck found ~61% 30-day directional accuracy on dated public calls, but only ~41% strict 30-day +10%/-10% hits. Mature supply-chain theses validated better than mechanical copying.
  • Survivorship / selection bias. A public feed highlights winners. Reversed or wrong calls exist (see references/track-record.md) and get less airtime.
  • High-volatility micro/small-caps. Many of his names (AXTI, SIVE, IQE, AAOI) move 20%+ in a day, have thin floats, dilution risk, and binary outcomes. His position sizing and margin use are not appropriate to copy blindly — he says so himself ("build conviction yourself before entering").
  • Theses decay. Calls are dated. A bottleneck can resolve, a contract can be lost, an ATM can be filed. Always re-confirm current price and fundamentals.
  • This is a lens, not a signal feed. Use it to ask better questions about your own ideas. It is explicitly NOT auto-trading, NOT a recommendation to buy/sell, and NOT financial advice. Every order is the reader's own manual, confirmed decision.
  • No-position idea posts carry lower weight. If he frames a stock comment as exploratory, for fun, or explicitly says he has no position, treat it as a process example rather than a high-conviction call.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Serenity Aleabitoreddit AI skill do?

Apply trader Serenity's (@aleabitoreddit) AI/semiconductor supply-chain analytical lens to US-stock ideas and market judgment. Use this skill whenever evaluating a stock decision (buy / sell / hold / size); forming an outlook on any AI, semiconductor, optical/CPO, memory, power/grid, or neocloud name; mentioning any ticker in Serenity's universe (NBIS, AXTI, LITE, SIVE, COHR, AAOI, IREN, CRWV, MU, SNDK, NVDA, TSM, MRVL, AVGO, INTC, SOI, IQE, TSEM, CIFR, XLU, VST, CEG, EWY, etc.); asking "what would Serenity think", "is this a real bottleneck", or wanting a supply-chain / bottleneck read on...

Why use Serenity Aleabitoreddit on TypingMind?

Because you install it once and use it with any model. Serenity Aleabitoreddit is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Serenity Aleabitoreddit in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/yan-labs/serenity-aleabitoreddit/tree/main/serenity-aleabitoreddit. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Serenity Aleabitoreddit?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Serenity Aleabitoreddit?

As many as you like. As long as a model supports skills, you can use Serenity Aleabitoreddit with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Serenity Aleabitoreddit AI skill free?

It is published on GitHub by yan-labs. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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