Cost Tracking
affaan-m/ECC
Track and report Claude Code token usage, spending, and budgets from the local ECC cost-tracker metrics log.
Multi-method cost basis computation including specific identification, FIFO, LIFO, HIFO, and proportional average cost with partial sell handling
$ npx skills add agiprolabs/claude-trading-skills --skill cost-basis-engine -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install agiprolabs/claude-trading-skills cost-basis-engine --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-srcUse ~/.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/
Install the "cost-basis-engine" agent skill from https://github.com/agiprolabs/claude-trading-skills/tree/main/skills/cost-basis-engine into .claude/skills/cost-basis-engine/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cost-basis-engine", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/agiprolabs/claude-trading-skills/tree/main/skills/cost-basis-engineType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add agiprolabs/claude-trading-skills --skill cost-basis-engine -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install agiprolabs/claude-trading-skills cost-basis-engine --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agiprolabs/claude-trading-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/cost-basis-engine .agents/skills/cost-basis-engine && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "cost-basis-engine" agent skill from https://github.com/agiprolabs/claude-trading-skills/tree/main/skills/cost-basis-engine into .agents/skills/cost-basis-engine/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cost-basis-engine", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add agiprolabs/claude-trading-skills --skill cost-basis-engine -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install agiprolabs/claude-trading-skills cost-basis-engine --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agiprolabs/claude-trading-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/cost-basis-engine .cursor/skills/cost-basis-engine && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "cost-basis-engine" agent skill from https://github.com/agiprolabs/claude-trading-skills/tree/main/skills/cost-basis-engine into .cursor/skills/cost-basis-engine/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cost-basis-engine", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/agiprolabs/claude-trading-skills.git --path skills/cost-basis-engine--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add agiprolabs/claude-trading-skills --skill cost-basis-engine -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install agiprolabs/claude-trading-skills cost-basis-engine --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agiprolabs/claude-trading-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/cost-basis-engine .gemini/skills/cost-basis-engine && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "cost-basis-engine" agent skill from https://github.com/agiprolabs/claude-trading-skills/tree/main/skills/cost-basis-engine into .gemini/skills/cost-basis-engine/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cost-basis-engine", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install agiprolabs/claude-trading-skills cost-basis-engineInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add agiprolabs/claude-trading-skills --skill cost-basis-engine -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/agiprolabs/claude-trading-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/cost-basis-engine .github/skills/cost-basis-engine && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "cost-basis-engine" agent skill from https://github.com/agiprolabs/claude-trading-skills/tree/main/skills/cost-basis-engine into .github/skills/cost-basis-engine/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cost-basis-engine", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add agiprolabs/claude-trading-skills --skill cost-basis-engine -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install agiprolabs/claude-trading-skills cost-basis-engine --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agiprolabs/claude-trading-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/cost-basis-engine .opencode/skills/cost-basis-engine && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "cost-basis-engine" agent skill from https://github.com/agiprolabs/claude-trading-skills/tree/main/skills/cost-basis-engine into .opencode/skills/cost-basis-engine/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cost-basis-engine", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
cost-basis-engineMulti-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. 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.
9 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 981e1d7. It shows what the files ask for, not the result of running them.
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.
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.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
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.
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.
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.
The full file from agiprolabs/claude-trading-skills at commit 981e1d7, republished under its MIT licence (© agiprolabs). 915 words, ~3,148 tokens.
.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.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.
date, action, token, quantity, price_usd, fee_usd| Method | Logic | Best For |
|---|---|---|
| FIFO | First lots purchased are sold first | Simplicity, many jurisdictions' default |
| LIFO | Last lots purchased are sold first | Deferring gains when prices rise over time |
| HIFO | Highest-cost lots are sold first | Minimizing current tax liability |
| Specific ID | Trader selects which lots to sell | Maximum control, requires record-keeping |
| Average Cost | Weighted average of all held lots | Simplicity, required in some jurisdictions |
Sell the oldest lots first. This is the default method in the US if no other method is elected.
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 realizedYou hold three lots of TOKEN:
You sell 120 units at $3.00:
Sell the newest lots first. Reverses the order compared to FIFO.
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 realizedUsing the same lots and selling 120 at $3.00 with LIFO:
Sell the highest-cost lots first to minimize realized gains.
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 realizedSame lots, selling 120 at $3.00 with HIFO:
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).
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 realizedCompute a single weighted-average cost per unit across all held lots. Every sell uses that average cost. The average updates after each buy.
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}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.
Airdrops are treated as income at fair market value (FMV) on the date received. The FMV becomes the cost basis for future sales.
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 are income at FMV when received (similar to airdrops). Each reward event creates a new lot.
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"
}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.
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_ratioFor a 1:10 split of 100 tokens @ $5.00: result is 1000 tokens @ $0.50. Total basis unchanged at $500.
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):
LP Exit (redeem LP tokens for 12 SOL + 1400 USDC):
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}
}A multi-hop swap (e.g., SOL -> USDC -> TOKEN) creates multiple taxable events, one for each intermediate step. Jupiter often routes through intermediate tokens.
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 eventsExample: Swap 1 SOL ($150) -> 150 USDC -> 10,000 TOKEN ($0.015 each)
The core value of this skill: run the same trade history through all five methods and compare total realized gain and estimated tax liability.
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 exampleSee scripts/cost_basis_calculator.py for a full runnable comparison with realistic trade data including partial sells.
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)| File | Description |
|---|---|
references/planned_features.md | Method formulas, partial sell worked examples, special event handling, multi-hop treatment |
scripts/cost_basis_calculator.py | Full 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
SKILL.md and 2 other files (scripts, references) in skills/cost-basis-engine of agiprolabs/claude-trading-skills.
Open the folder on GitHubat commit 981e1d7
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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Cost Basis Engine this skillagiprolabs/claude-trading-skills | 410 | — | ~3.1k | Automated safety check: Pass | MIT | |
| Cost Trackingaffaan-m/ECC | 276k | 1 repos | ~1.3k | Automated safety check: Pass | MIT | |
| Cost Conversationruvnet/ruflo | 74k | — | ~407 | Automated safety check: Notes | MIT | |
| Cost Reportruvnet/ruflo | 74k | — | ~830 | Automated safety check: Notes | MIT | |
| Cost Budget Checkruvnet/ruflo | 74k | — | ~645 | Automated safety check: Notes | MIT | |
| Ito Computeaffaan-m/ECC | 276k | 1 repos | ~1.7k | Automated safety check: Pass | MIT |
affaan-m/ECC
Track and report Claude Code token usage, spending, and budgets from the local ECC cost-tracker metrics log.
ruvnet/ruflo
Per-conversation cost view — list every session in cost-tracking with started-at, message count, top model, and total cost
ruvnet/ruflo
Generate a cost report showing token usage and USD costs by agent and model
ruvnet/ruflo
Read accumulated cost-tracking spend + budget config, compute utilization, emit 50/75/90/100% alert ladder
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…
ruvnet/ruflo
Analyze token usage patterns and recommend cost optimizations with estimated savings
agiprolabs/claude-trading-skills
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agiprolabs/claude-trading-skills
Broad crypto market data from CoinGecko covering 13,000+ tokens.
agiprolabs/claude-trading-skills
Cointegration testing for pairs trading using Engle-Granger, Johansen, and rolling stability analysis
agiprolabs/claude-trading-skills
Wallet evaluation, monitoring, and copy-trade strategy design for Solana DEX trading
agiprolabs/claude-trading-skills
Cross-asset correlation analysis including rolling correlation, hierarchical clustering, tail dependence, and regime-dependent correlation
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.
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.
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.
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.
Going by SKILL.md and its folder, Cost Basis Engine needs Python for the scripts in its folder. Our summary lists: Python 3.
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.
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.
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.
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.
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.
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.