Agent skill

Cost Basis Engine

by agiprolabs in agiprolabs/claude-trading-skills

Multi-method cost basis computation including specific identification, FIFO, LIFO, HIFO, and proportional average cost with partial sell handling

MITAuto-check passed

Install Cost Basis Engine

skills CLI
$ npx skills add agiprolabs/claude-trading-skills --skill cost-basis-engine -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install agiprolabs/claude-trading-skills cost-basis-engine --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/agiprolabs/claude-trading-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/cost-basis-engine .claude/skills/cost-basis-engine && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
cost-basis-engine
GitHub stars
410
Token cost
~3.1k tokens
SKILL.md length
915 words
Files
3 (incl. scripts, references)
Skills in repo
68
Repo updated
First seen
Licence
MIT

At a glance

Multi-method cost basis computation including specific identification, FIFO, LIFO, HIFO, and proportional average cost with partial sell handling

  • Works in 9 steps: FIFO (First-In, First-Out) → LIFO (Last-In, First-Out) → HIFO (Highest-In, First-Out) → …
  • SKILL.md covers Prerequisites, Methods Overview, 1. FIFO (First-In, First-Out) and 2. LIFO (Last-In, First-Out), plus 10 more sections
  • Runs Python scripts from its folder

What it does

Cost Basis Engine is an agent skill from agiprolabs/claude-trading-skills. Multi-method cost basis computation including specific identification, FIFO, LIFO, HIFO, and proportional average cost with partial sell handling

Its SKILL.md is about 3.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including scripts and reference files (for example `references/planned_features.md` and `scripts/cost_basis_calculator.py`).

The repository describes itself as: 68 trading, DeFi, and quantitative finance Agent Skills. Works with Claude Code, Cursor, Codex, Gemini CLI, and 30+ other tools. The licence is MIT.

Example prompts

  • “/cost-basis-engine”

Requirements

  • Python 3

Workflow steps

9 steps, taken from the step headings in SKILL.md.

  1. FIFO (First-In, First-Out)
  2. LIFO (Last-In, First-Out)
  3. HIFO (Highest-In, First-Out)
  4. Specific Identification
  5. Proportional / Average Cost Method
  6. Special Events
  7. LP Entry/Exit as Token Swaps
  8. Multi-Hop Swaps
  9. Comparison View

What it can do on your machine

Read from SKILL.md and the folder at commit 981e1d7. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Cost Basis Engine loads about 3.1k tokens when it runs, and up to ~5.5k if it reads all its reference files. Until then it costs about 41 tokens; SKILL.md has 915 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~41
When it runs · the whole SKILL.md, loaded when a task matches
~3.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~5.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from agiprolabs/claude-trading-skills at commit 981e1d7, republished under its MIT licence (© agiprolabs). 915 words, ~3,148 tokens.

Download SKILL.mdSave it as .claude/skills/cost-basis-engine/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
cost-basis-engine
description
Multi-method cost basis computation including specific identification, FIFO, LIFO, HIFO, and proportional average cost with partial sell handling
license
MIT
metadata.author
agipro
metadata.version
0.1.0
metadata.category
trading

Cost Basis Engine

Compute cost basis for crypto trades using multiple accounting methods and compare the resulting tax liability across methods. This skill handles the full complexity of on-chain activity: partial sells, token migrations, airdrops, staking rewards, LP entry/exit, and multi-hop swaps.

Disclaimer: This skill provides computational tools for informational purposes only. It does not constitute tax, legal, or financial advice. Consult a qualified tax professional for your specific situation. Tax law varies by jurisdiction and changes frequently.

Prerequisites

  • Python 3.10+
  • No external dependencies required (standard library only)
  • Trade history as a list of dicts or CSV with columns: date, action, token, quantity, price_usd, fee_usd

Methods Overview

MethodLogicBest For
FIFOFirst lots purchased are sold firstSimplicity, many jurisdictions' default
LIFOLast lots purchased are sold firstDeferring gains when prices rise over time
HIFOHighest-cost lots are sold firstMinimizing current tax liability
Specific IDTrader selects which lots to sellMaximum control, requires record-keeping
Average CostWeighted average of all held lotsSimplicity, required in some jurisdictions

1. FIFO (First-In, First-Out)

Sell the oldest lots first. This is the default method in the US if no other method is elected.

python
def fifo_sell(lots: list[dict], sell_qty: float, sell_price: float) -> list[dict]:
    """Sell using FIFO. lots sorted oldest-first."""
    remaining = sell_qty
    realized = []
    while remaining > 0 and lots:
        lot = lots[0]
        used = min(lot["qty"], remaining)
        gain = (sell_price - lot["cost_per_unit"]) * used
        realized.append({"qty": used, "basis": lot["cost_per_unit"], "gain": gain})
        lot["qty"] -= used
        remaining -= used
        if lot["qty"] <= 0:
            lots.pop(0)
    return realized
Partial sell example

You hold three lots of TOKEN:

  • Lot A: 100 units @ $1.00 (oldest)
  • Lot B: 50 units @ $2.00
  • Lot C: 75 units @ $1.50

You sell 120 units at $3.00:

  • 100 from Lot A: gain = (3.00 - 1.00) * 100 = $200
  • 20 from Lot B: gain = (3.00 - 2.00) * 20 = $20
  • Total realized gain: $220
  • Lot B remainder: 30 units @ $2.00

2. LIFO (Last-In, First-Out)

Sell the newest lots first. Reverses the order compared to FIFO.

python
def lifo_sell(lots: list[dict], sell_qty: float, sell_price: float) -> list[dict]:
    """Sell using LIFO. Pops from end (newest first)."""
    remaining = sell_qty
    realized = []
    while remaining > 0 and lots:
        lot = lots[-1]
        used = min(lot["qty"], remaining)
        gain = (sell_price - lot["cost_per_unit"]) * used
        realized.append({"qty": used, "basis": lot["cost_per_unit"], "gain": gain})
        lot["qty"] -= used
        remaining -= used
        if lot["qty"] <= 0:
            lots.pop()
    return realized

Using the same lots and selling 120 at $3.00 with LIFO:

  • 75 from Lot C: gain = (3.00 - 1.50) * 75 = $112.50
  • 45 from Lot B: gain = (3.00 - 2.00) * 45 = $45
  • Total realized gain: $157.50

3. HIFO (Highest-In, First-Out)

Sell the highest-cost lots first to minimize realized gains.

python
def hifo_sell(lots: list[dict], sell_qty: float, sell_price: float) -> list[dict]:
    """Sell using HIFO. Sort by cost descending, consume highest first."""
    lots.sort(key=lambda x: x["cost_per_unit"], reverse=True)
    remaining = sell_qty
    realized = []
    for lot in lots:
        if remaining <= 0:
            break
        used = min(lot["qty"], remaining)
        gain = (sell_price - lot["cost_per_unit"]) * used
        realized.append({"qty": used, "basis": lot["cost_per_unit"], "gain": gain})
        lot["qty"] -= used
        remaining -= used
    lots[:] = [l for l in lots if l["qty"] > 0]
    return realized

Same lots, selling 120 at $3.00 with HIFO:

  • 50 from Lot B ($2.00, highest): gain = (3.00 - 2.00) * 50 = $50
  • 70 from Lot C ($1.50, next highest): gain = (3.00 - 1.50) * 70 = $105
  • Total realized gain: $155
  • Remaining: Lot A 100 @ $1.00, Lot C 5 @ $1.50

4. Specific Identification

The trader explicitly selects which lots to sell. Provides maximum control but requires meticulous record-keeping. Each lot must be uniquely identifiable (e.g., by purchase date and time, or a lot ID).

python
def specific_id_sell(lots: dict[str, dict], lot_ids: list[tuple[str, float]],
                     sell_price: float) -> list[dict]:
    """Sell specific lots by ID. lot_ids = [(lot_id, qty_to_sell), ...]"""
    realized = []
    for lot_id, sell_qty in lot_ids:
        lot = lots[lot_id]
        used = min(lot["qty"], sell_qty)
        gain = (sell_price - lot["cost_per_unit"]) * used
        realized.append({"lot_id": lot_id, "qty": used, "basis": lot["cost_per_unit"], "gain": gain})
        lot["qty"] -= used
        if lot["qty"] <= 0:
            del lots[lot_id]
    return realized

5. Proportional / Average Cost Method

Compute a single weighted-average cost per unit across all held lots. Every sell uses that average cost. The average updates after each buy.

python
def average_cost_basis(lots: list[dict]) -> float:
    """Compute weighted average cost per unit across all lots."""
    total_cost = sum(l["qty"] * l["cost_per_unit"] for l in lots)
    total_qty = sum(l["qty"] for l in lots)
    if total_qty == 0:
        return 0.0
    return total_cost / total_qty

def average_cost_sell(lots: list[dict], sell_qty: float, sell_price: float) -> dict:
    """Sell using average cost. Reduces all lots proportionally."""
    avg = average_cost_basis(lots)
    total_qty = sum(l["qty"] for l in lots)
    sell_qty = min(sell_qty, total_qty)
    gain = (sell_price - avg) * sell_qty
    # Reduce each lot proportionally
    ratio = sell_qty / total_qty
    for lot in lots:
        lot["qty"] *= (1 - ratio)
    lots[:] = [l for l in lots if l["qty"] > 1e-12]
    return {"qty": sell_qty, "avg_basis": avg, "gain": gain}
Partial sell with average cost

Lots: 100 @ $1.00, 50 @ $2.00, 75 @ $1.50. Total: 225 units, total cost $312.50.

Average cost = $312.50 / 225 = $1.3889/unit

Sell 120 at $3.00: gain = (3.00 - 1.3889) * 120 = $193.33

After the sell, 105 units remain at the same $1.3889 average.


6. Special Events

Airdrops

Airdrops are treated as income at fair market value (FMV) on the date received. The FMV becomes the cost basis for future sales.

python
airdrop_lot = {
    "date": "2025-03-15",
    "qty": 1000,
    "cost_per_unit": 0.05,   # FMV at time of receipt
    "income_recognized": 50.0,  # 1000 * 0.05 reported as income
    "source": "airdrop"
}
Staking Rewards

Staking rewards are income at FMV when received (similar to airdrops). Each reward event creates a new lot.

python
staking_lot = {
    "date": "2025-04-01",
    "qty": 5.2,
    "cost_per_unit": 150.0,  # SOL price at receipt
    "income_recognized": 780.0,
    "source": "staking_reward"
}
Token Splits and Migrations

A token split or migration (old token to new token 1:1 or N:M) is generally not a taxable event. The total cost basis transfers to the new tokens.

python
def apply_split(lots: list[dict], split_ratio: float) -> None:
    """Apply a token split. split_ratio > 1 means more tokens."""
    for lot in lots:
        lot["qty"] *= split_ratio
        lot["cost_per_unit"] /= split_ratio

For a 1:10 split of 100 tokens @ $5.00: result is 1000 tokens @ $0.50. Total basis unchanged at $500.


Show full SKILL.md (355 more words)Show less

7. LP Entry/Exit as Token Swaps

Entering an LP position is treated as selling the deposited tokens and receiving LP tokens. Exiting is the reverse.

LP Entry (deposit 10 SOL + 1500 USDC into SOL/USDC pool):

  1. Dispose of 10 SOL at current FMV → capital gain/loss event
  2. Dispose of 1500 USDC at current FMV → usually negligible gain/loss
  3. Receive LP tokens with cost basis = FMV of deposited assets

LP Exit (redeem LP tokens for 12 SOL + 1400 USDC):

  1. Dispose of LP tokens at FMV of received assets → capital gain/loss
  2. Receive 12 SOL with cost basis = FMV at redemption
  3. Receive 1400 USDC with cost basis = FMV at redemption
python
def lp_entry(sol_qty: float, sol_price: float, usdc_qty: float,
             lp_tokens_received: float) -> dict:
    """Model LP entry as disposal of component tokens."""
    total_value = sol_qty * sol_price + usdc_qty * 1.0
    lp_cost_basis = total_value / lp_tokens_received
    return {
        "disposals": [
            {"token": "SOL", "qty": sol_qty, "price": sol_price},
            {"token": "USDC", "qty": usdc_qty, "price": 1.0},
        ],
        "lp_lot": {"qty": lp_tokens_received, "cost_per_unit": lp_cost_basis}
    }

8. Multi-Hop Swaps

A multi-hop swap (e.g., SOL -> USDC -> TOKEN) creates multiple taxable events, one for each intermediate step. Jupiter often routes through intermediate tokens.

python
def multi_hop_events(hops: list[dict]) -> list[dict]:
    """
    Each hop is: {"sell_token", "sell_qty", "sell_price",
                  "buy_token", "buy_qty", "buy_price"}
    Each hop is a separate taxable event.
    """
    events = []
    for i, hop in enumerate(hops):
        events.append({
            "event": i + 1,
            "dispose": hop["sell_token"],
            "dispose_qty": hop["sell_qty"],
            "dispose_value": hop["sell_qty"] * hop["sell_price"],
            "acquire": hop["buy_token"],
            "acquire_qty": hop["buy_qty"],
            "acquire_basis": hop["buy_qty"] * hop["buy_price"],
        })
    return events

Example: Swap 1 SOL ($150) -> 150 USDC -> 10,000 TOKEN ($0.015 each)

  • Event 1: Dispose 1 SOL (basis vs. $150 proceeds) → gain/loss on SOL
  • Event 2: Dispose 150 USDC (basis vs. $150 proceeds) → usually ~$0 gain
  • Result: 10,000 TOKEN with cost basis = $0.015/unit

9. Comparison View

The core value of this skill: run the same trade history through all five methods and compare total realized gain and estimated tax liability.

python
methods = ["FIFO", "LIFO", "HIFO", "Specific ID", "Average Cost"]
# After processing all trades through each method:
comparison = {
    "FIFO":        {"total_gain": 220.00, "tax_at_30pct": 66.00},
    "LIFO":        {"total_gain": 157.50, "tax_at_30pct": 47.25},
    "HIFO":        {"total_gain": 155.00, "tax_at_30pct": 46.50},
    "Specific ID": {"total_gain": 160.00, "tax_at_30pct": 48.00},
    "Average Cost":{"total_gain": 193.33, "tax_at_30pct": 58.00},
}
# HIFO minimizes liability in this example

See scripts/cost_basis_calculator.py for a full runnable comparison with realistic trade data including partial sells.


Quick Start

python
from scripts.cost_basis_calculator import CostBasisEngine

engine = CostBasisEngine()

# Add purchases
engine.add_buy("2025-01-10", "TOKEN", 100, 1.00)
engine.add_buy("2025-02-15", "TOKEN", 50, 2.00)
engine.add_buy("2025-03-01", "TOKEN", 75, 1.50)

# Sell and compare methods
results = engine.sell_compare("2025-04-01", "TOKEN", 120, 3.00)
engine.print_comparison(results)

Use Cases

  1. Tax season preparation: Run your full year of trades through all methods before choosing one to report.
  2. Accumulation strategy: Track partial sells during DCA accumulation, see how each method affects remaining basis.
  3. LP position tracking: Model LP entry/exit as swaps and capture the associated gain/loss events.
  4. Airdrop and staking income: Properly record income events and set cost basis for future disposals.
  5. Multi-hop swap decomposition: Break down Jupiter routes into individual taxable events.

Files

FileDescription
references/planned_features.mdMethod formulas, partial sell worked examples, special event handling, multi-hop treatment
scripts/cost_basis_calculator.pyFull engine with all 5 methods, comparison table, demo mode with realistic trades

Remember: The "best" method depends on your jurisdiction, your specific trade history, and your tax situation. This engine helps you compare — a tax professional helps you decide.

© agiprolabs, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (scripts, references) in skills/cost-basis-engine of agiprolabs/claude-trading-skills.

  • SKILL.md
  • references/planned_features.md
  • scripts/cost_basis_calculator.py

Open the folder on GitHubat commit 981e1d7

Compare with similar skills

Cost Basis Engine next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Cost Basis Engine compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Cost Basis Engine this skillagiprolabs/claude-trading-skills410—~3.1kAutomated safety check: PassMIT
Cost Trackingaffaan-m/ECC276k1 repos~1.3kAutomated safety check: PassMIT
Cost Conversationruvnet/ruflo74k—~407Automated safety check: NotesMIT
Cost Reportruvnet/ruflo74k—~830Automated safety check: NotesMIT
Cost Budget Checkruvnet/ruflo74k—~645Automated safety check: NotesMIT
Ito Computeaffaan-m/ECC276k1 repos~1.7kAutomated safety check: PassMIT

Similar skills

  • Cost Tracking

    affaan-m/ECC

    Track and report Claude Code token usage, spending, and budgets from the local ECC cost-tracker metrics log.

    276k GitHub starsUsed in 1 repo~1.3k tokens
    AI & LLM EngineeringAuto-check passed
  • Cost Conversation

    ruvnet/ruflo

    Per-conversation cost view — list every session in cost-tracking with started-at, message count, top model, and total cost

    74k GitHub stars~407 tokensUpdated today
    AI & LLM EngineeringAuto-check: notes
  • Cost Report

    ruvnet/ruflo

    Generate a cost report showing token usage and USD costs by agent and model

    74k GitHub stars~830 tokensUpdated today
    AI & LLM EngineeringAuto-check: notes
  • Cost Budget Check

    ruvnet/ruflo

    Read accumulated cost-tracking spend + budget config, compute utilization, emit 50/75/90/100% alert ladder

    74k GitHub stars~645 tokensUpdated today
    AI & LLM EngineeringAuto-check: notes
  • Ito Compute

    affaan-m/ECC

    Query live GPU inventory, submit an authenticated Itô fixed-rate RFQ, inspect RFQ or procurement status, revoke device credentials, and run explicitly gated node qualification through the separately…

    276k GitHub starsUsed in 1 repo~1.7k tokens
    Business, Finance & HRAuto-check passed
  • Cost Optimize

    ruvnet/ruflo

    Analyze token usage patterns and recommend cost optimizations with estimated savings

    74k GitHub stars~997 tokensUpdated today
    AI & LLM EngineeringAuto-check: notes

More from agiprolabs/claude-trading-skills

All 68 skills in this repo
  • Backtrader

    agiprolabs/claude-trading-skills

    Event-driven backtesting with bar-by-bar execution, complex order types, multiple analyzers, and custom indicators

    410 GitHub stars~2.4k tokensUpdated 1 mo ago
    Auto-check passed
  • Birdeye API

    agiprolabs/claude-trading-skills

    Solana token market data via Birdeye — prices, OHLCV, trades, token metadata, security checks, and trader activity

    410 GitHub stars~1.8k tokensUpdated 1 mo ago
    Auto-check passed
  • Coingecko API

    agiprolabs/claude-trading-skills

    Broad crypto market data from CoinGecko covering 13,000+ tokens.

    410 GitHub stars~1.6k tokensUpdated 1 mo ago
    Auto-check passed
  • Cointegration Analysis

    agiprolabs/claude-trading-skills

    Cointegration testing for pairs trading using Engle-Granger, Johansen, and rolling stability analysis

    410 GitHub stars~2.1k tokensUpdated 1 mo ago
    Auto-check passed
  • Copy Trading

    agiprolabs/claude-trading-skills

    Wallet evaluation, monitoring, and copy-trade strategy design for Solana DEX trading

    410 GitHub stars~2.4k tokensUpdated 1 mo ago
    Auto-check passed
  • Correlation Analysis

    agiprolabs/claude-trading-skills

    Cross-asset correlation analysis including rolling correlation, hierarchical clustering, tail dependence, and regime-dependent correlation

    410 GitHub stars~2.4k tokensUpdated 1 mo ago
    Auto-check passed

Questions about Cost Basis Engine

What does Cost Basis Engine do?

Multi-method cost basis computation including specific identification, FIFO, LIFO, HIFO, and proportional average cost with partial sell handling. Cost Basis Engine is an agent skill from agiprolabs/claude-trading-skills.

How do I install Cost Basis Engine in Claude Code?

Run `npx skills add agiprolabs/claude-trading-skills --skill cost-basis-engine -a claude-code`. Or copy the skill folder (skills/cost-basis-engine in agiprolabs/claude-trading-skills) into .claude/skills/cost-basis-engine in your project. Claude Code loads it when a task matches its description.

How do I install Cost Basis Engine in Codex?

Run `npx skills add agiprolabs/claude-trading-skills --skill cost-basis-engine -a codex`. Or copy the skill folder (skills/cost-basis-engine in agiprolabs/claude-trading-skills) into .agents/skills/cost-basis-engine in your project. Codex loads it when a task matches its description.

Can I use Cost Basis Engine in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add agiprolabs/claude-trading-skills --skill cost-basis-engine -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/cost-basis-engine, .gemini/skills/cost-basis-engine, .github/skills/cost-basis-engine and .opencode/skills/cost-basis-engine in your project.

What does Cost Basis Engine need to run?

Going by SKILL.md and its folder, Cost Basis Engine needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Cost Basis Engine access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Cost Basis Engine safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Cost Basis Engine use?

Cost Basis Engine is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Cost Basis Engine use?

About 3.1k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.3k tokens, read only when the agent opens those files.

What are the alternatives to Cost Basis Engine?

Skills that share tags, products or a category with Cost Basis Engine: Cost Tracking (affaan-m/ECC, 276k stars), Cost Conversation (ruvnet/ruflo, 74k stars), Cost Report (ruvnet/ruflo, 74k stars) and Cost Budget Check (ruvnet/ruflo, 74k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Cost Basis Engine?

agiprolabs (a GitHub user) maintains it in agiprolabs/claude-trading-skills, which has 410 GitHub stars. The repository holds 68 skills in this directory. The repository was last updated on September 3, 2026.

Source: agiprolabs/claude-trading-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.