Agent skill

Algo Risk Var

by asgard-ai-platform in asgard-ai-platform/skills

Calculate Value at Risk to estimate maximum portfolio loss at a given confidence level.

MITAuto-check passed

Install Algo Risk Var

skills CLI

$ npx skills add asgard-ai-platform/skills --skill algo-risk-var -a claude-code

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

GitHub CLI

$ gh skill install asgard-ai-platform/skills algo-risk-var --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/asgard-ai-platform/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/algo-risk-var .claude/skills/algo-risk-var && 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/

Paste into Claude Code

Install the "algo-risk-var" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-risk-var into .claude/skills/algo-risk-var/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-risk-var", 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

$skill-installer install https://github.com/asgard-ai-platform/skills/tree/main/algo-risk-var

Type 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.

skills CLI

$ npx skills add asgard-ai-platform/skills --skill algo-risk-var -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install asgard-ai-platform/skills algo-risk-var --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/algo-risk-var .agents/skills/algo-risk-var && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "algo-risk-var" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-risk-var into .agents/skills/algo-risk-var/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-risk-var", 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.

skills CLI

$ npx skills add asgard-ai-platform/skills --skill algo-risk-var -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install asgard-ai-platform/skills algo-risk-var --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/algo-risk-var .cursor/skills/algo-risk-var && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "algo-risk-var" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-risk-var into .cursor/skills/algo-risk-var/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-risk-var", 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

$ gemini skills install https://github.com/asgard-ai-platform/skills.git --path algo-risk-var

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add asgard-ai-platform/skills --skill algo-risk-var -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install asgard-ai-platform/skills algo-risk-var --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/algo-risk-var .gemini/skills/algo-risk-var && rm -rf skills-src

Use ~/.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/

Paste into Gemini CLI

Install the "algo-risk-var" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-risk-var into .gemini/skills/algo-risk-var/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-risk-var", 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.

GitHub CLI

$ gh skill install asgard-ai-platform/skills algo-risk-var

Installs 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).

skills CLI

$ npx skills add asgard-ai-platform/skills --skill algo-risk-var -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/algo-risk-var .github/skills/algo-risk-var && rm -rf skills-src

Use ~/.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/

Paste into GitHub Copilot

Install the "algo-risk-var" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-risk-var into .github/skills/algo-risk-var/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-risk-var", 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.

skills CLI

$ npx skills add asgard-ai-platform/skills --skill algo-risk-var -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install asgard-ai-platform/skills algo-risk-var --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/algo-risk-var .opencode/skills/algo-risk-var && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "algo-risk-var" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-risk-var into .opencode/skills/algo-risk-var/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-risk-var", 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.

Facts

Skill name: algo-risk-var
GitHub stars: 242
Token cost: ~1.2k tokens
SKILL.md length: 480 words
Files: 4 (incl. references)
Skills in repo: 207
Repo updated: Jun 6, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Calculate Value at Risk to estimate maximum portfolio loss at a given confidence level.

Works in 4 steps: Input Validation → Core Algorithm → Verification → …
The user needs to quantify downside risk
SKILL.md covers Overview, When to Use, Algorithm and Output Format, plus 3 more sections
Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Algo Risk Var is an agent skill from asgard-ai-platform/skills. Calculate Value at Risk to estimate maximum portfolio loss at a given confidence level. Use this skill when the user needs to quantify downside risk, set risk limits, or report regulatory risk measures — even if they say 'worst case loss', 'portfolio risk', or 'how much could we lose'.

Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `examples/sample_scenario.md`, `references/backtesting.md` and `references/expected-shortfall.md`).

The repository describes itself as: 301 open-source coding agent skills across 22 domains — methodology, judgment & gotchas packaged as Claude Agent Skills for the Asgard AI Platform. The licence is MIT.

When your agent uses it

The user needs to quantify downside risk
Set risk limits
Report regulatory risk measures — even if they say worst case loss
How much could we lose

Example prompts

“worst case loss”
“portfolio risk”
“how much could we lose”
“/algo-risk-var”

Workflow steps

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

Input Validation
Core Algorithm
Verification
Output

What it can do on your machine

Read from SKILL.md and the folder at commit 4e7f4f8. 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
No scripts in the folder and no shell commands in SKILL.md (its code samples are json).
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

Algo Risk Var loads about 1.2k tokens when it runs, and up to ~5.5k if it reads all its reference files. Until then it costs about 75 tokens; SKILL.md has 480 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~75
When it runs · the whole SKILL.md, loaded when a task matches: ~1.2k
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); files beside SKILL.md are not scanned.

SKILL.md

The full file from asgard-ai-platform/skills at commit 4e7f4f8, republished under its MIT licence (© asgard-ai-platform). 480 words, ~1,187 tokens.

Download SKILL.mdSave it as .claude/skills/algo-risk-var/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.

name: algo-risk-var
description: Calculate Value at Risk to estimate maximum portfolio loss at a given confidence level. Use this skill when the user needs to quantify downside risk, set risk limits, or report regulatory risk measures — even if they say 'worst case loss', 'portfolio risk', or 'how much could we lose'.
metadata.category: WP-40 風險演算法
metadata.tags: risk, value-at-risk, portfolio, risk-management

Value at Risk (VaR)

Overview

VaR estimates the maximum loss a portfolio can suffer over a given time horizon at a specified confidence level. Example: "95% 1-day VaR of $1M" means there's a 5% chance of losing more than $1M in one day. Three methods: parametric (normal), historical simulation, Monte Carlo.

When to Use

Trigger conditions:

Quantifying portfolio downside risk for risk management
Setting trading limits and capital reserves
Regulatory reporting (Basel III requires VaR-based capital)

When NOT to use:

When you need to know how bad losses CAN get beyond VaR (use CVaR/Expected Shortfall)
For illiquid assets with no price history (VaR needs return data)

Algorithm

IRON LAW: VaR Does NOT Tell You How Bad It Gets BEYOND the Threshold
VaR says "95% of the time, losses won't exceed $X." It says NOTHING
about the 5% worst case. A portfolio can have low VaR but catastrophic
tail losses. Always supplement with Expected Shortfall (CVaR) which
measures the average loss in the tail.

Phase 1: Input Validation

Collect: portfolio positions, historical returns (min 250 days for 1Y), confidence level (typically 95% or 99%), time horizon (1 day or 10 days). Gate: Sufficient return history, positions valued at current market.

Phase 2: Core Algorithm

Parametric VaR: VaR = -μ + zα × σ (assumes normal returns). For portfolio: use covariance matrix for portfolio σ.

Historical Simulation: 1. Compute daily P&L from historical returns. 2. Sort P&L ascending. 3. VaR = the (1-α) percentile loss.

Monte Carlo: 1. Fit return distribution (or use historical). 2. Simulate 10,000+ portfolio paths. 3. VaR = (1-α) percentile of simulated losses.

Phase 3: Verification

Backtest: count how often actual losses exceed VaR over the past year. At 95% confidence, exceedances should be ~5%. Use Kupiec or Christoffersen test. Gate: Backtest exceedance rate within acceptable bounds.

Phase 4: Output

Return VaR estimate with backtest results.

Output Format

json

{
  "var": {"amount": 1250000, "confidence": 0.95, "horizon_days": 1, "currency": "TWD"},
  "cvar": {"amount": 1800000},
  "backtest": {"exceedances": 13, "expected": 12.5, "days_tested": 250, "pass": true},
  "metadata": {"method": "historical_simulation", "portfolio_value": 50000000}
}

Examples

Sample I/O

Input: Portfolio value = $1,000,000. Last 20 sorted daily returns (descending loss):

[-0.050, -0.040, -0.035, -0.030, -0.025, -0.020, -0.015, -0.010, -0.005, 0.000,
  0.005,  0.010,  0.015,  0.020,  0.025,  0.030,  0.035,  0.040,  0.045,  0.050]

Confidence = 95%, horizon = 1 day.

Expected (Historical Simulation):

5th percentile index = floor(20 × 0.05) = 1 → return[1] = -0.040
VaR = $1,000,000 × 0.040 = $40,000
CVaR (Expected Shortfall) = mean of returns worse than VaR = (-0.050) × $1M = $50,000

Verify: VaR ≤ CVaR always (tail loss ≥ threshold loss). Count of losses > VaR should be ≤ 5% of observations (1 of 20).

Show full SKILL.md (165 more words)Show less

Edge Cases

Input	Expected	Why
Normal market conditions	VaR looks adequate	But misses tail events
2008-like crisis in history	Higher VaR from historical method	Captures fat tails if crisis is in window
Very short history (30 days)	Unreliable VaR	Insufficient data for tail estimation

Gotchas

Normality assumption: Parametric VaR assumes normal returns. Financial returns have fat tails — parametric VaR UNDERESTIMATES tail risk.
Historical window: Historical simulation is only as good as the history. If the past 250 days were calm, VaR will be low even if a crisis is coming.
Time scaling: VaR scales with √T only under independence and normality. For volatile or trending markets, this approximation is poor.
Diversification illusion: VaR from correlated assets using normal-times correlations understates risk. Correlations spike during crises (correlation breakdown).
Gaming VaR: Traders can structure positions that look safe under VaR but have catastrophic tail risk. This is why regulators also require stress testing.

References

For Expected Shortfall (CVaR) calculation, see references/expected-shortfall.md
For VaR backtesting methods, see references/backtesting.md

Files

SKILL.md and 3 other files (references) in algo-risk-var of asgard-ai-platform/skills.

SKILL.md
examples/sample_scenario.md
references/backtesting.md
references/expected-shortfall.md

Open the folder on GitHubat commit 4e7f4f8

Compare with similar skills

Algo Risk Var 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.

Algo Risk Var compared with similar skills
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Portfolio Risk Metricswshobson/agents	40k	12 repos	~502	Automated safety check: Pass	MIT	6 days ago
Task Effort EstimatorDonchitos/Claude-Code-Game-Studios	26k	—	~1.2k	Automated safety check: Pass	MIT	3 days ago
Progressive Estimationsickn33/agentic-awesome-skills	47k	2 repos	~863	Automated safety check: Pass	MIT	2 days ago

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Questions about Algo Risk Var

What does Algo Risk Var do?

Calculate Value at Risk to estimate maximum portfolio loss at a given confidence level. Algo Risk Var is an agent skill from asgard-ai-platform/skills. Calculate Value at Risk to estimate maximum portfolio loss at a given confidence level.

When should I use Algo Risk Var?

Algo Risk Var fits situations like: the user needs to quantify downside risk; set risk limits; report regulatory risk measures — even if they say worst case loss; how much could we lose.

How do I install Algo Risk Var in Claude Code?

Run `npx skills add asgard-ai-platform/skills --skill algo-risk-var -a claude-code`. Or copy the skill folder (algo-risk-var in asgard-ai-platform/skills) into .claude/skills/algo-risk-var in your project. Claude Code loads it when a task matches its description.

How do I install Algo Risk Var in Codex?

Run `npx skills add asgard-ai-platform/skills --skill algo-risk-var -a codex`. Or copy the skill folder (algo-risk-var in asgard-ai-platform/skills) into .agents/skills/algo-risk-var in your project. Codex loads it when a task matches its description.

Can I use Algo Risk Var 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 asgard-ai-platform/skills --skill algo-risk-var -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/algo-risk-var, .gemini/skills/algo-risk-var, .github/skills/algo-risk-var and .opencode/skills/algo-risk-var in your project.

What does Algo Risk Var need to run?

SKILL.md names no scripts, command-line tools or credentials: Algo Risk Var is instructions for the agent only.

Does Algo Risk Var 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 Algo Risk Var 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. Review the folder before installing.

What licence does Algo Risk Var use?

Algo Risk Var is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Algo Risk Var use?

About 1.2k tokens (SKILL.md is roughly 4.7k 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 4.3k tokens, read only when the agent opens those files.

What are the alternatives to Algo Risk Var?

Skills that share tags, products or a category with Algo Risk Var: Portfolio (anthropics/claude-for-legal, 9.6k stars), Trader Portfolio (ruvnet/ruflo, 74k stars), Portfolio Risk Metrics (wshobson/agents, 40k stars) and Task Effort Estimator (Donchitos/Claude-Code-Game-Studios, 26k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Algo Risk Var?

asgard-ai-platform (a GitHub organization) maintains it in asgard-ai-platform/skills, which has 242 GitHub stars. The repository holds 207 skills in this directory. The repository was last updated on June 6, 2026.

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

Install Algo Risk Var

Facts

At a glance

What it does

When your agent uses it

Example prompts

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Value at Risk (VaR)

Overview

When to Use

Algorithm

Phase 1: Input Validation

Phase 2: Core Algorithm

Phase 3: Verification

Phase 4: Output

Output Format

Examples

Sample I/O

Edge Cases

Gotchas

References

Files

Compare with similar skills

Similar skills

More from asgard-ai-platform/skills

Questions about Algo Risk Var