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Etf Analysis

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yennanliu
etf-analysis

ETF / index-fund due diligence — expense ratio vs. category, tracking difference, liquidity, holdings concentration and tilt, overlap with your other positions, distribution and capital-gains history, structure warnings, and an ETF-vs-top-holdings comparison, scored as an ETF Fitness Score 0–10

Overview

Publisheryennanliu
RepositoryInvestSkill
Skill nameetf-analysis
Stars
254
Forks
61
Bundled files
Instructions only
LicenseMIT
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by yennanliu on GitHub. Read the source before you install it.

Installation

Install the Etf Analysis 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/yennanliu/InvestSkill/tree/main/plugins/us-stock-analysis/skills/etf-analysis
  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/yennanliu/InvestSkill.git /tmp/InvestSkill
mkdir -p .claude/skills
cp -r /tmp/InvestSkill/plugins/us-stock-analysis/skills/etf-analysis .claude/skills/etf-analysis
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Etf Analysis 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 Etf Analysis 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 Etf Analysis 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.

ETF Analysis — What You Actually Own, and What It Costs You

⚠️ Data Verification — Do This Before Any Analysis

Before running any analysis, always retrieve the latest fund data for the ticker:

  1. Fetch current price and NAV — use web search or ask the user for the live price, the fund's net asset value, and the premium/discount to NAV. Never assume a price from training data.
  2. Confirm the fund facts from the issuer — expense ratio, index tracked, AUM, inception date, holdings count, top-10 weights, and distribution history come from the issuer's factsheet and prospectus, not from memory. Fund facts change: expense ratios get cut, indexes get swapped, funds get merged or closed.
  3. State your data source — fill in the Data & Sources header (next section) so the origin, as-of date, retrieval path, and confidence of every figure are explicit at the top of the output.
  4. Flag stale data explicitly — if live data is unavailable, display this warning before proceeding:

⚠️ Live data unavailable. The following analysis uses training-data estimates which may be significantly out of date. Verify all prices and metrics before making any decisions.

Never silently substitute training-data estimates for current fund facts. When in doubt, ask the user to paste the factsheet.


📋 Data & Sources Header — Open Every Output With It

The first thing in the output is this provenance block, filled in — never left as placeholders. It is the standard documented on the Data & Accuracy page and the first thing result-validator looks for:

Data & Sources
  As of:      <date the figures represent, e.g. 2026-06-30>
  Source:     <primary docs — SEC EDGAR 10-K/10-Q, company IR, FRED, exchange data …>
  Retrieval:  <pasted by user | web/tool retrieval | model memory>
  Confidence: <HIGH | MEDIUM | LOW>
  • Retrieval: model memory must be paired with Confidence: LOW — memory is a placeholder until confirmed against a primary source.
  • Mixed sources: list each with its own as-of date rather than blending them.
  • Sources disagree on the same figure (a closing price, a deal term): prefer the primary source (SEC filing, company release, exchange data) over an aggregator, then the most recent dated one. Show both values with their sources and dates, say which you used and why, and never average them.
  • Data the user pasted is reported as pasted by user; do not upgrade its confidence beyond what the user's own source supports.

Overview

Most portfolios are built on an ETF core, yet every other framework in this catalog evaluates a single company. Nobody asks the questions that decide whether an ETF is a good vehicle: what does it cost to hold, does it deliver the index it promises, can you get out of it, what is actually inside it, and how much of it do you already own through other positions?

This skill is due diligence on the wrapper, not a market call. It does not predict whether US large caps will outperform; sector-analysis and economics-analysis do that. It answers: given that you want this exposure, is this fund a fit way to get it — and is it a fit for the portfolio you already have? The output is an ETF Fitness Score (0–10) built from cost, tracking, liquidity, what-you-own, overlap, distributions, and structure, plus a direct comparison against two or three peers and against simply buying the top five holdings.

Where it sits. stock-screener ranks stocks; portfolio-review judges the whole portfolio; sector-analysis picks where to be. This skill sits between them: it vets the fund that would carry the exposure, then hands the result to portfolio-review (does it fit the allocation?), risk-stress-test (what does it do in a drawdown?), and tax-lens (what does the wrapper cost after tax?).


1. Inputs

InputRequiredDefault if unstated
ETF ticker(s) — one fund, or several to compare✅—
The exposure the user thinks they are buying ("US large cap", "global ex-US", "dividend growth")recommendedinfer from the fund name and index, and say so
Current holdings with weights (tickers, other funds)optional — required for the overlap sectionoverlap reported as "not assessed" and its 15% weight redistributed (Phase 9)
Intended purchase size (as % of the post-purchase portfolio)optional — required for post-purchase exposure10% of the portfolio, stated
Account type (taxable / IRA / non-US)optionaltaxable US assumed; state it
Holding horizonoptional5+ years

Overlap and tilt need holdings data. Top-10 weights are on every factsheet; the full holdings file (issuer CSV) is needed for an exact overlap or factor tilt. If the user has not pasted it, compute from the top 10 only, label the result as a lower bound, and say what file would sharpen it.


2. Framework

Phase 1 — Fund card

Establish the facts before any judgment:

FieldWhy it matters
Issuer, ticker, inception dateFunds under 3 years old have no full-cycle record; funds under $100M AUM have closure risk
Index tracked (or "active")The index is the strategy — read its methodology: cap-weighted? screened? rebalanced when?
Expense ratio (net, after waivers) and waiver expiryA fee waiver that expires is a fee increase with a date
Structure: physical replication / sampling / synthetic (swap) / ETN / grantor trust / commodity poolDetermines counterparty risk, tax form, and whether "holdings" means anything
Legal domicile (US 1940-Act ETF, Irish UCITS, …)Drives withholding tax and estate-tax exposure for non-US holders
Distribution policy (accumulating / distributing, frequency)Matters for taxable accounts and for income users

Phase 2 — Cost and tracking

Expense ratio vs. category. Compare to the median for funds tracking the same kind of index, not to "all ETFs":

CategoryCheapExpensiveNeeds a reason
Broad US large-cap index≤ 0.05%> 0.20%> 0.75%
Broad international developed / EM index≤ 0.10%> 0.35%> 0.75%
Sector, factor, dividend, thematic≤ 0.15%> 0.50%> 0.95%
Active, leveraged, inverse, alternatives≤ 0.50%> 1.00%— (the whole product needs a reason)

Tracking difference vs. tracking error — keep them apart.

  • Tracking difference = fund total return − index total return over a period. This is the cost you actually paid. For a well-run physical fund it should be close to −(expense ratio); a fund that trails its index by more than the expense ratio every year is leaking money (sampling, cash drag, poor securities lending, withholding tax at the fund level).
  • Tracking error = the standard deviation of that difference. It measures consistency, not cost. Low tracking error with a large negative tracking difference is a fund that reliably underperforms.

Report the 1-, 3-, and 5-year tracking difference from the issuer's own performance table (NAV return vs. index return). Flag a tracking gap whenever |tracking difference| exceeds the expense ratio by more than 0.10% per year — and say which direction (a positive gap usually means securities-lending revenue, which is fine but not guaranteed).

Phase 3 — Liquidity and closure risk

MetricComfortableCautionWarning
AUM> $1B$100M – $1B< $100M (closure risk; issuers cull small funds)
Average daily $ volume> $10M$1M – $10M< $1M (use limit orders, avoid the open/close)
Bid-ask spread (median, %)≤ 0.05%0.05% – 0.25%> 0.25% (a round trip costs more than a year of fees)
Premium/discount to NAV (typical range)±0.10%±0.50%wider, or persistently one-sided

Note the caveat that matters: ETF liquidity comes from the underlying basket, not only from the ETF's own volume — a thinly traded ETF on liquid large caps trades fine with limit orders; a heavily traded ETF on illiquid bonds does not. Say which case applies.

Phase 4 — What you own

  • Holdings count and top-10 weight. Flag concentration when top-10 > 40% for a "broad" fund, or when a single name > 10%. Cap-weighted indexes concentrate in bull markets — a fund that was diversified five years ago may not be now.
  • Sector tilt — the fund's sector weights vs. the broad benchmark the user thinks they are buying (e.g. the total US market). Report the three largest over/under-weights in percentage points.
  • Factor tilt — size, value/growth, quality, momentum, low-vol, yield: which way does the index methodology lean, and does the user know it? A "dividend" fund is usually a value + low-growth tilt; an "equal-weight" fund is a small-cap tilt.
  • Country / currency exposure for international funds — and whether the fund is currency-hedged (a bet on the dollar, held whether the user wants it or not).
  • Look-through: if the fund holds other funds, look through to the final securities.

Phase 5 — Overlap with the user's other holdings

With the holdings the user supplied, compute overlap by weight:

  1. Look through every existing fund position to its underlying securities (top holdings from the issuer's factsheet; label the coverage — "top 10 = 34% of the fund" — so the reader knows the rest is unassessed). Existing single stocks are their own exposure.
  2. Express both sides as weights of the same denominator: the candidate ETF's holdings as % of the ETF; the existing portfolio's look-through exposures as % of the existing portfolio.
  3. Overlap % = Σ over shared securities of min(weight in ETF, weight in existing portfolio). This is the share of the ETF that duplicates what the user already owns, on a like-for-like basis.
  4. With the intended purchase size p (as % of the post-purchase portfolio; default 10%, stated), compute the post-purchase exposure to each shared name: (1 − p) × existing weight + p × ETF weight.

Report:

  • Overlap % (by weight) and the duplicated names, largest first; the look-through coverage used.
  • The effective post-purchase exposure to the top duplicated names (e.g. "at a 10% purchase you would own the largest holding through three vehicles at a combined 11% of the portfolio").
  • A plain verdict: redundant (> 60% overlap — this is the same bet in a second wrapper), partially overlapping (25–60% — fine if intentional), complementary (< 25%).

Without a holdings file, report the top-10-only overlap as a lower bound and say so.

Phase 6 — Distributions and taxes

  • Trailing 12-month distribution yield and its composition: qualified dividends, non-qualified income, return of capital, capital gains. REIT-, bond-, and commodity-heavy funds pay mostly non-qualified income taxed at ordinary rates.
  • Capital-gains distributions, last 5 years. Most equity ETFs distribute none, because in-kind creation/redemption lets them hand appreciated shares to authorized participants instead of selling them; a mutual fund cannot. An equity ETF that has distributed gains (heavy rebalancing, index reconstitution, small AUM with big redemptions) deserves a note. Bond and commodity funds are a different matter.
  • Tax form: 1099 for ordinary ETFs; K-1 and 60/40 treatment for many commodity and volatility funds structured as partnerships; grantor-trust rules for physically backed metals. A K-1 is a paperwork and timing cost, not just a tax rate.
  • Hand the detail to tax-lens — this skill flags, that one computes.

Phase 7 — Structure warnings

State any that apply, in a box the reader cannot miss:

StructureWarning to print
Leveraged (2×, 3×) or inverseResets daily. Over more than a day, path dependence ("volatility decay") means the return is not 2× or −1× the index. A holding tool for days, not years — say so even if the user asked for a long-term view
Synthetic / swap-basedThe fund holds a swap, not the securities. Counterparty exposure to the swap provider; "holdings" listed are collateral
ETNAn unsecured note issued by a bank. If the issuer fails, the ETN fails, regardless of the index. Check the issuer's credit and whether the ETN is still creating shares (closed ETNs trade at wild premiums)
Single-stock ETFA leveraged or option-overlaid position in one company. Concentration + decay + fees — nearly always dominated by owning the stock
Commodity pool / futures-basedRoll yield (contango) can make the fund diverge sharply from spot; K-1 tax form; not an investment in the physical commodity
Currency-hedgedEmbeds a short position in the foreign currency; hedging cost ≈ the interest-rate differential
Covered-call / buffer / defined-outcomeCaps upside by design; the "yield" is option premium, partly return of your own capital. Compare total return, never headline yield
Actively managedCompare to the cheapest index fund in the same category over the manager's tenure, not to the category average

Phase 8 — Alternatives and the direct-ownership check

  • Peers: two or three funds tracking the same or a very similar index. Compare expense ratio, 3-yr tracking difference, AUM, spread, and structure in one table. Prefer the cheapest fund with the smallest tracking gap and adequate liquidity — everything else is marketing.
  • ETF vs. buying the top 5 holdings directly: what share of the fund's weight the top 5 represent, the fund's fee on that share, what the investor would give up (the other N−5 holdings, rebalancing, dividend reinvestment convenience) and what they would gain (zero ongoing fee, tax-lot control, no tracking gap). For a broad fund with top-5 weight under 25%, direct ownership is a different, more concentrated bet — say so. For a "sector" fund where the top 5 are 60%+ of the weight, direct ownership is a serious alternative.

Phase 9 — ETF Fitness Score (0–10)

The headline number. It measures fitness of the vehicle for the stated exposure and portfolio, not the attractiveness of the asset class.

ComponentWeight10 looks like0 looks like
Cost & tracking25%Cheapest quartile for its category; tracking difference ≈ −ER with no gapExpensive for the category and a tracking gap > 0.25%/yr
Liquidity & viability15%AUM > $1B, spread ≤ 0.05%, tight to NAVAUM < $100M or spread > 0.25% or persistent discount
What you own vs. what you wanted20%Index methodology matches the stated exposure; tilts disclosed and acceptableThe fund is a different bet than the user thinks (hidden factor/country/currency tilt, top-10 > 50%)
Overlap with existing holdings15%< 25% overlap, or overlap is intentional> 60% overlap — a duplicate position
Distribution & tax efficiency10%No capital-gains distributions; income mostly qualified; 1099Regular gains distributions; K-1; non-qualified income in a taxable account
Structure15%Plain physical replicationLeveraged/inverse, ETN, or single-stock held as a long-term position

Hard caps: a leveraged/inverse/single-stock fund evaluated for a horizon longer than a few weeks caps at 3.0; an ETN caps at 5.0; > 60% overlap caps at 5.0. State any cap that fired.

When overlap is not assessed (no holdings supplied): drop the component and renormalize the remaining five weights to 100% (Cost & tracking 29.4% · Liquidity 17.6% · What you own 23.5% · Distribution 11.8% · Structure 17.6%), print Overlap: not assessed — supply holdings for the full score under the score, and cap Confidence at MEDIUM. Never score an unassessed component as 10 or as 0.


3. Output Format

Data & Sources · as of · issuer factsheet · fund prospectus · retrieval · confidence

1. Fund card          ticker · issuer · index tracked · inception · AUM · expense ratio · structure · domicile
2. Cost & tracking    expense ratio vs. category median · 1/3/5-yr tracking difference · tracking gap flag · spread · ADV
3. What you own       #holdings · top-10 weight · sector / factor / country / currency tilt · concentration flag
4. Overlap            % overlap by weight with the user's holdings · duplicated names · verdict (redundant / partial / complementary)
5. Distributions      trailing yield · composition (qualified / ordinary / ROC / gains) · capital-gains distributions (last 5 yrs) · tax form
6. Structure risks    leveraged / inverse / synthetic / ETN / single-stock / commodity / hedged / covered-call → warning box
7. Alternatives       2–3 peers in one table · "vs. top-5 holdings directly" table
8. ETF Fitness Score  0–10 with the six sub-scores and any cap that fired → Thesis Invalidation → signal block

When several tickers are given, produce sections 1–6 per fund compactly, then one comparison table, then one score per fund.


Example

User: Is VOO or SPYM the better core holding? I already hold VTI at 40% of my portfolio.

The assistant builds the fund card for both (same index family — S&P 500 — so the
comparison collapses to cost, tracking, liquidity and share price), pulls the issuer
performance tables to compare 1/3/5-yr tracking difference against each fund's expense
ratio, notes that both are physical, distributing, 1099 funds with no capital-gains
distributions in the record supplied, and that either is liquid enough for any retail
order with a limit. Then it runs the overlap check against VTI: an S&P 500 fund is
roughly four-fifths of a total-US-market fund by weight, so the overlap is high and the
verdict is "redundant — you already own this exposure through VTI; adding either fund
raises large-cap concentration without adding a new bet." Both funds score well on
fitness in isolation; the report's headline is the overlap finding, and the suggested
next step is `portfolio-review` to decide whether the user actually wants *more* US
large cap, or `etf-analysis` on an international or small-cap fund instead.

Notes

  • This is vehicle due diligence, not a market view. Do not score the asset class. A perfectly built fund on an exposure the user should not own still gets a high Fitness Score — pair with sector-analysis or economics-analysis for whether to own it, and portfolio-review for how much.
  • Never invent fund statistics. Expense ratios, AUM, tracking figures, and holdings change; if the issuer page or factsheet was not retrieved or pasted, say "not verified" rather than recalling a number.
  • Read the index methodology. Two funds with similar names can track very different indexes (screened vs. unscreened, capped vs. uncapped, quarterly vs. annual rebalancing). The methodology document, not the fund name, is the strategy.
  • Non-US investors (including readers using the Traditional Chinese site): a US-domiciled ETF exposes a non-resident holder to 30% (or treaty-rate) withholding on distributions and to US estate-tax exposure above a low exemption; an Irish-domiciled UCITS equivalent typically suffers 15% withholding at the fund level and no US estate-tax exposure, at a somewhat higher expense ratio and, for accumulating share classes, no distributions at all. Name the UCITS equivalent when one exists and hand the arithmetic to tax-lens --non-us.
  • Overlap arithmetic depends on the data supplied. Top-10-only overlap is a lower bound; label it. Full holdings files are downloadable from every major issuer.
  • Bond, commodity, and alternative ETFs need the extra questions this skill only flags (duration, credit quality, roll yield, collateral). Say when the fund is outside the equity-index case this framework is built for.
  • Pairs with: portfolio-review (allocation fit), risk-stress-test (drawdown behaviour), tax-lens (after-tax cost), sector-analysis / stock-screener (whether the exposure is worth owning at all).

Thesis Invalidation

After delivering the analysis signal, specify what would reverse it:

If signal is BULLISH (the fund is a fit vehicle) — the verdict breaks if:

  • The tracking difference widens beyond −(expense ratio + 0.10%) in the next annual performance table — the fund has started leaking
  • The issuer changes the index, raises the fee, lets a waiver expire, or announces a merger or liquidation (AUM falling below ~$50M is the early warning)
  • The fund's concentration crosses the thresholds above (top-10 > 40%, single name > 10%) so that "broad" is no longer true
  • The user's other holdings change such that the overlap verdict moves from complementary to redundant

If signal is BEARISH (the fund is not a fit vehicle) — the verdict breaks if:

  • A fee cut or a fixed tracking gap removes the cost objection (re-run against the peers)
  • The user's stated exposure changes so that the tilt this fund carries is now the intended bet
  • The overlap objection disappears because the duplicated position is sold
  • For a structure warning: the user's horizon and purpose genuinely match the product (a multi-day hedge with an inverse fund, for example) — the warning still prints, the cap may be lifted

Re-run this analysis when:

  • The issuer publishes its annual performance / tracking table
  • Any change to expense ratio, index, or fund structure is announced
  • The user adds or removes a position large enough to change the overlap verdict
  • AUM falls below $100M or the spread widens persistently
  • 12 months have elapsed

Standard Signal Output

This skill measures vehicle fitness, not market direction, so state the mapping: Signal: BULLISH means the fund is a fit way to hold the stated exposure for this portfolio; NEUTRAL means it is acceptable but a cheaper, better-tracking, or less overlapping alternative exists (name it); BEARISH means do not use this wrapper (structure cap fired, redundant overlap, or a tracking gap that peers do not have). Action: BUY here means "this fund is fit for the purpose" — it is not a view on the asset class. Read whether to own the exposure from sector-analysis or portfolio-review, not from this block.

All analysis concludes with this standardized block:

╔══════════════════════════════════════════════╗
║              INVESTMENT SIGNAL               ║
╠══════════════════════════════════════════════╣
║ Signal:      BULLISH / NEUTRAL / BEARISH     ║
║ Confidence:  HIGH / MEDIUM / LOW             ║
║ Horizon:     SHORT / MEDIUM / LONG-TERM      ║
║ Score:       X.X / 10                        ║
╠══════════════════════════════════════════════╣
║ Action:      BUY / HOLD / SELL               ║
║ Conviction:  STRONG / MODERATE / WEAK        ║
╚══════════════════════════════════════════════╝

Score Guide: 8.0–10.0 Strongly Bullish | 6.0–7.9 Moderately Bullish | 4.0–5.9 Neutral | 2.0–3.9 Moderately Bearish | 0.0–1.9 Strongly Bearish Confidence: HIGH (strong data, clear signals) | MEDIUM (mixed signals) | LOW (limited data, conflicting signals) Horizon: SHORT-TERM (1 week–3 months) | MEDIUM-TERM (3 months–1 year) | LONG-TERM (1+ years)

Note: The Score above is the ETF Fitness Score, mapped onto the standard scale for cross-skill comparability. It rates how fit this fund is as a vehicle for the stated exposure and portfolio — not how attractive the exposure is. Confidence is LOW whenever the issuer factsheet was not retrieved or the overlap was computed from the top 10 only.

Disclaimer: Educational analysis only. Not financial advice. Fund facts change; verify every figure against the issuer's current factsheet and prospectus before acting.

Frequently asked questions

What does the Etf Analysis AI skill do?

ETF / index-fund due diligence — expense ratio vs. category, tracking difference, liquidity, holdings concentration and tilt, overlap with your other positions, distribution and capital-gains history, structure warnings, and an ETF-vs-top-holdings comparison, scored as an ETF Fitness Score 0–10

Why use Etf Analysis on TypingMind?

Because you install it once and use it with any model. Etf Analysis 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 Etf Analysis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/yennanliu/InvestSkill/tree/main/plugins/us-stock-analysis/skills/etf-analysis. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Etf Analysis?

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 Etf Analysis?

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

Is the Etf Analysis AI skill free?

Yes. It is published on GitHub by yennanliu under the MIT license. 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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