Swapper Deposit
swapperfinance/swapper-toolkit
Deposit and bridge funds into a wallet or protocol using Swapper Finance.
Build credit scoring models to predict default probability from borrower characteristics.
Install Algo Risk Credit
$ npx skills add asgard-ai-platform/skills --skill algo-risk-credit -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install asgard-ai-platform/skills algo-risk-credit --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/asgard-ai-platform/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/algo-risk-credit .claude/skills/algo-risk-credit && 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 "algo-risk-credit" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-risk-credit into .claude/skills/algo-risk-credit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-risk-credit", 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/asgard-ai-platform/skills/tree/main/algo-risk-creditType 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 asgard-ai-platform/skills --skill algo-risk-credit -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install asgard-ai-platform/skills algo-risk-credit --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/algo-risk-credit .agents/skills/algo-risk-credit && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "algo-risk-credit" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-risk-credit into .agents/skills/algo-risk-credit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-risk-credit", 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 asgard-ai-platform/skills --skill algo-risk-credit -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install asgard-ai-platform/skills algo-risk-credit --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/algo-risk-credit .cursor/skills/algo-risk-credit && 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 "algo-risk-credit" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-risk-credit into .cursor/skills/algo-risk-credit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-risk-credit", 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/asgard-ai-platform/skills.git --path algo-risk-credit--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 asgard-ai-platform/skills --skill algo-risk-credit -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install asgard-ai-platform/skills algo-risk-credit --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/algo-risk-credit .gemini/skills/algo-risk-credit && 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 "algo-risk-credit" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-risk-credit into .gemini/skills/algo-risk-credit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-risk-credit", 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 asgard-ai-platform/skills algo-risk-creditInstalls 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 asgard-ai-platform/skills --skill algo-risk-credit -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/algo-risk-credit .github/skills/algo-risk-credit && 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 "algo-risk-credit" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-risk-credit into .github/skills/algo-risk-credit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-risk-credit", 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 asgard-ai-platform/skills --skill algo-risk-credit -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install asgard-ai-platform/skills algo-risk-credit --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/algo-risk-credit .opencode/skills/algo-risk-credit && 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 "algo-risk-credit" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-risk-credit into .opencode/skills/algo-risk-credit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-risk-credit", 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-credit- GitHub stars
- 242
- Token cost
- ~1.1k tokens
- SKILL.md length
- 417 words
- Files
- 4 (incl. references)
- Skills in repo
- 207
- Repo updated
- First seen
- Licence
- MIT
At a glance
Build credit scoring models to predict default probability from borrower characteristics.
- Works in 4 steps: Input Validation → Core Algorithm → Verification → …
- The user needs to assess creditworthiness
- 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 Credit is an agent skill from asgard-ai-platform/skills. Build credit scoring models to predict default probability from borrower characteristics. Use this skill when the user needs to assess creditworthiness, build a credit scorecard, or evaluate lending risk — even if they say 'predict default risk', 'credit scoring', or 'loan approval model'.
Its SKILL.md is about 1.1k 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/reject-inference.md` and `references/woe-binning.md`).
It sits in Business, Finance & HR, covering Banking and insurance. 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 assess creditworthiness
- Build a credit scorecard
- Evaluate lending risk — even if they say predict default risk
- Loan approval model
Example prompts
- “predict default risk”
- “credit scoring”
- “loan approval model”
- “/algo-risk-credit”
Workflow steps
4 steps, taken from the step headings in SKILL.md.
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 Credit loads about 1.1k tokens when it runs, and up to ~6.6k if it reads all its reference files. Until then it costs about 77 tokens; SKILL.md has 417 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.
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). 417 words, ~1,089 tokens.
Download SKILL.mdSave it as
.claude/skills/algo-risk-credit/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.- name
- algo-risk-credit
- description
- Build credit scoring models to predict default probability from borrower characteristics. Use this skill when the user needs to assess creditworthiness, build a credit scorecard, or evaluate lending risk — even if they say 'predict default risk', 'credit scoring', or 'loan approval model'.
- metadata.category
- WP-40 風險演算法
- metadata.tags
- risk, credit-scoring, default-prediction, lending
Credit Scoring Model
Overview
Credit scoring models predict the probability of default (PD) from borrower characteristics using logistic regression or gradient boosting. Output: a score (300-850 range) or PD (0-1). Used for loan approval, pricing, and portfolio risk management.
When to Use
Trigger conditions:
- Building a scorecard for loan/credit approval decisions
- Predicting default probability for risk-based pricing
- Evaluating existing credit models for discriminatory power
When NOT to use:
- For corporate bankruptcy prediction (use Altman Z-Score)
- For market risk measurement (use VaR)
Algorithm
IRON LAW: A Credit Model Must Discriminate AND Be Calibrated
Discrimination (AUC): correctly ranking good vs bad borrowers.
Calibration: predicted PD matches actual default rates.
A model with AUC=0.85 but predicted PD 2x actual default rate will
cause systematic over/under-pricing. Need BOTH properties.Phase 1: Input Validation
Collect: borrower features (income, debt ratio, credit history length, delinquency count, utilization), outcome variable (default within 12-24 months). Handle: missing values, class imbalance (typically 2-5% default rate). Gate: Sufficient defaults (300+ events), features available at decision time.
Phase 2: Core Algorithm
- Feature engineering: WOE (Weight of Evidence) binning for logistic regression, or direct encoding for GBDT
- Train model: logistic regression (interpretable, regulatory-preferred) or GBDT (higher accuracy)
- Calibrate: Platt scaling on holdout, ensure predicted PD matches actual default rate by decile
- Convert to score: Score = offset + factor × log(odds), scaled to 300-850 range
Phase 3: Verification
Evaluate: AUC (>0.70 acceptable, >0.80 good), KS statistic, Gini coefficient. Population stability index (PSI) for monitoring drift. Gate: AUC > 0.70, calibration acceptable, no discriminatory bias in protected attributes.
Phase 4: Output
Return score, PD, and key risk drivers.
Output Format
{
"score": 680,
"pd": 0.035,
"risk_grade": "B",
"top_risk_factors": [{"factor": "high_utilization", "impact": -45}, {"factor": "short_history", "impact": -30}],
"metadata": {"model": "logistic_regression", "auc": 0.78, "vintage": "2024-Q3"}
}Examples
Sample I/O
Input: Borrower: income=$60K, DTI=35%, 5yr credit history, 0 delinquencies, 60% utilization Expected: Score ~680, PD ~3.5%, Grade B (some risk from high utilization)
Show full SKILL.md (167 more words)Show less
Edge Cases
| Input | Expected | Why |
|---|---|---|
| No credit history (thin file) | High uncertainty, default to conservative | Insufficient data for scoring |
| All features identical | Same score regardless of outcome | Model can't differentiate — need more features |
| Major economy shift | PSI > 0.25, model needs recalibration | Population has shifted from training distribution |
Gotchas
- Reject inference: Training data only includes approved applicants. Rejected applicants' outcomes are unknown, creating selection bias. Use reject inference techniques.
- Fair lending: Models must not discriminate by protected attributes (race, gender, age). Even proxy variables (zip code ≈ race) can create disparate impact. Test with fairness metrics.
- Through-the-door vs on-the-books: TTD samples include all applicants; OTB only approved ones. Model purpose determines which sample to use.
- Vintage analysis: Default rates vary by economic conditions. A 2019-trained model may not predict well in a recession. Track model performance by vintage.
- Regulatory requirements: Financial regulators (Basel, OCC, FDIC) have specific requirements for model validation, documentation, and fair lending testing.
References
- For WOE binning methodology, see
references/woe-binning.md - For reject inference techniques, see
references/reject-inference.md
© asgard-ai-platform, 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 3 other files (references) in algo-risk-credit of asgard-ai-platform/skills.
- SKILL.md
- examples/sample_scenario.md
- references/reject-inference.md
- references/woe-binning.md
Open the folder on GitHubat commit 4e7f4f8
Compare with similar skills
Algo Risk Credit 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 |
|---|---|---|---|---|---|---|
| Algo Risk Credit this skillasgard-ai-platform/skills | 242 | — | ~1.1k | Automated safety check: Pass | MIT | |
| Swapper Depositswapperfinance/swapper-toolkit | 852 | — | ~1.8k | Automated safety check: Pass | MIT | |
| Okx Cex Earnokx/agent-skills | 187 | 2 repos | ~3.3k | Automated safety check: Pass | MIT | |
| Buffettdigoal/blog | 8.6k | 1 repos | ~2.7k | Automated safety check: Pass | GPL-2.0 | |
| Solana Payments Wallets Tradingnpc-live/clawfirm | 156 | 1 repos | ~4.7k | Automated safety check: Pass | MIT | |
| Oracle Flashloan Analysisquillai-network/quillshield_skills | 130 | — | ~2.8k | Automated safety check: Pass | MIT |
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Related
Categories
Questions about Algo Risk Credit
What does Algo Risk Credit do?
Build credit scoring models to predict default probability from borrower characteristics. Algo Risk Credit is an agent skill from asgard-ai-platform/skills. Build credit scoring models to predict default probability from borrower characteristics.
When should I use Algo Risk Credit?
Algo Risk Credit fits situations like: the user needs to assess creditworthiness; build a credit scorecard; evaluate lending risk — even if they say predict default risk; loan approval model.
How do I install Algo Risk Credit in Claude Code?
Run `npx skills add asgard-ai-platform/skills --skill algo-risk-credit -a claude-code`. Or copy the skill folder (algo-risk-credit in asgard-ai-platform/skills) into .claude/skills/algo-risk-credit in your project. Claude Code loads it when a task matches its description.
How do I install Algo Risk Credit in Codex?
Run `npx skills add asgard-ai-platform/skills --skill algo-risk-credit -a codex`. Or copy the skill folder (algo-risk-credit in asgard-ai-platform/skills) into .agents/skills/algo-risk-credit in your project. Codex loads it when a task matches its description.
Can I use Algo Risk Credit 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-credit -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-credit, .gemini/skills/algo-risk-credit, .github/skills/algo-risk-credit and .opencode/skills/algo-risk-credit in your project.
What does Algo Risk Credit need to run?
SKILL.md names no scripts, command-line tools or credentials: Algo Risk Credit is instructions for the agent only.
Does Algo Risk Credit 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 Credit 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 Credit use?
Algo Risk Credit 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 Credit use?
About 1.1k tokens (SKILL.md is roughly 4.4k 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 5.5k tokens, read only when the agent opens those files.
What are the alternatives to Algo Risk Credit?
Skills that share tags, products or a category with Algo Risk Credit: Swapper Deposit (swapperfinance/swapper-toolkit, 852 stars), Okx Cex Earn (okx/agent-skills, 187 stars), Buffett (digoal/blog, 8.6k stars) and Solana Payments Wallets Trading (npc-live/clawfirm, 156 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Algo Risk Credit?
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.