Analytics And Advisory Intelligence
LeoYeAI/openclaw-master-skills
Cross-client analytics for Greek accounting firms. An agent skill from LeoYeAI/openclaw-master-skills.
Apply Benford's Law to detect anomalies in numerical datasets by analyzing first-digit frequency distributions.
Install Algo Risk Benford
$ npx skills add asgard-ai-platform/skills --skill algo-risk-benford -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install asgard-ai-platform/skills algo-risk-benford --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-benford .claude/skills/algo-risk-benford && 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-benford" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-risk-benford into .claude/skills/algo-risk-benford/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-risk-benford", 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-benfordType 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-benford -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install asgard-ai-platform/skills algo-risk-benford --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-benford .agents/skills/algo-risk-benford && 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-benford" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-risk-benford into .agents/skills/algo-risk-benford/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-risk-benford", 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-benford -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install asgard-ai-platform/skills algo-risk-benford --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-benford .cursor/skills/algo-risk-benford && 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-benford" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-risk-benford into .cursor/skills/algo-risk-benford/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-risk-benford", 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-benford--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-benford -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install asgard-ai-platform/skills algo-risk-benford --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-benford .gemini/skills/algo-risk-benford && 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-benford" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-risk-benford into .gemini/skills/algo-risk-benford/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-risk-benford", 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-benfordInstalls 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-benford -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-benford .github/skills/algo-risk-benford && 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-benford" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-risk-benford into .github/skills/algo-risk-benford/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-risk-benford", 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-benford -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-benford --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-benford .opencode/skills/algo-risk-benford && 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-benford" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-risk-benford into .opencode/skills/algo-risk-benford/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-risk-benford", 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-benford- GitHub stars
- 242
- Token cost
- ~1.1k tokens
- SKILL.md length
- 421 words
- Files
- 4 (incl. references)
- Skills in repo
- 207
- Repo updated
- First seen
- Licence
- MIT
At a glance
Apply Benford's Law to detect anomalies in numerical datasets by analyzing first-digit frequency distributions.
- Works in 4 steps: Input Validation → Core Algorithm → Verification → …
- The user needs to audit financial data for fraud indicators
- 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 Benford is an agent skill from asgard-ai-platform/skills. Apply Benford's Law to detect anomalies in numerical datasets by analyzing first-digit frequency distributions. Use this skill when the user needs to audit financial data for fraud indicators, validate data integrity, or detect fabricated numbers — even if they say 'data manipulation detection', 'first digit test', or 'accounting fraud screening'.
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/fraud-case-studies.md` and `references/higher-digit-tests.md`).
It sits in Business, Finance & HR, covering Accounting and bookkeeping, DataFrames and Anomaly detection. 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 audit financial data for fraud indicators
- Validate data integrity
- Detect fabricated numbers — even if they say data manipulation detection
- First digit test
Example prompts
- “data manipulation detection”
- “first digit test”
- “accounting fraud screening”
- “/algo-risk-benford”
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 Benford loads about 1.1k tokens when it runs, and up to ~6.4k if it reads all its reference files. Until then it costs about 92 tokens; SKILL.md has 421 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). 421 words, ~1,122 tokens.
Download SKILL.mdSave it as
.claude/skills/algo-risk-benford/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.- name
- algo-risk-benford
- description
- Apply Benford's Law to detect anomalies in numerical datasets by analyzing first-digit frequency distributions. Use this skill when the user needs to audit financial data for fraud indicators, validate data integrity, or detect fabricated numbers — even if they say 'data manipulation detection', 'first digit test', or 'accounting fraud screening'.
- metadata.category
- WP-40 風險演算法
- metadata.tags
- risk, benfords-law, fraud-detection, data-audit
Benford's Law Analysis
Overview
Benford's Law predicts that in naturally occurring datasets, the leading digit d appears with probability P(d) = log₁₀(1 + 1/d). Digit 1 appears ~30.1% of the time, digit 9 only ~4.6%. Deviations from this distribution may indicate data fabrication or manipulation. Analysis runs in O(n).
When to Use
Trigger conditions:
- Auditing financial data (expenses, invoices, tax returns) for manipulation
- Screening large datasets for data integrity issues
- Detecting fabricated or artificially rounded numbers
When NOT to use:
- For assigned/sequential numbers (zip codes, phone numbers, IDs)
- For datasets with constrained ranges (e.g., human ages, percentages)
- For small datasets (< 500 records — insufficient statistical power)
Algorithm
IRON LAW: Benford's Law Applies to NATURALLY OCCURRING Data Spanning Orders of Magnitude
Data that doesn't span multiple orders of magnitude (e.g., temperatures
in Celsius, human heights) will NOT follow Benford's Law. Deviation from
Benford's in such data is EXPECTED, not suspicious. Always verify the
data type is appropriate before concluding fraud.Phase 1: Input Validation
Extract leading digits from dataset. Filter: remove zeros, negatives (take absolute value), values < 10. Verify dataset spans multiple orders of magnitude. Gate: 500+ records, data spans at least 2 orders of magnitude.
Phase 2: Core Algorithm
- Extract first digit of each number
- Count frequency of each digit (1-9)
- Compare observed frequencies against Benford's expected: P(d) = log₁₀(1 + 1/d)
- Statistical tests: chi-squared test, MAD (Mean Absolute Deviation), KS test
Phase 3: Verification
MAD thresholds: < 0.006 (close conformity), 0.006-0.012 (acceptable), 0.012-0.015 (marginal), > 0.015 (non-conforming). Flag specific digits with large deviations. Gate: MAD computed, non-conforming digits identified.
Phase 4: Output
Return conformity assessment with digit-level analysis.
Output Format
{
"conformity": "marginal",
"mad": 0.013,
"chi_squared": {"statistic": 18.5, "p_value": 0.018, "df": 8},
"digit_analysis": [{"digit": 1, "observed_pct": 25.1, "expected_pct": 30.1, "deviation": -5.0}],
"metadata": {"records": 5000, "dataset": "Q4 expense reports"}
}Examples
Sample I/O
Input: 1000 invoice amounts from a company's AP ledger Expected: First digits should approximate 30.1%, 17.6%, 12.5%, 9.7%, 7.9%, 6.7%, 5.8%, 5.1%, 4.6%. MAD < 0.012 for legitimate data.
Show full SKILL.md (174 more words)Show less
Edge Cases
| Input | Expected | Why |
|---|---|---|
| All amounts $90-$99 | Digit 9 dominates | Constrained range — Benford's doesn't apply |
| Round number spike (digit 1, 5) | Flag for review | May indicate round-number estimation or threshold manipulation |
| Government budget data | Typically conforms well | Large naturally-occurring financial datasets fit Benford's |
Gotchas
- Not proof of fraud: Non-conformity is a RED FLAG, not evidence. Many legitimate processes produce non-Benford distributions. Always investigate further.
- Second-digit test: First digit test catches gross fabrication. Second-digit analysis catches more subtle manipulation (e.g., rounding to approval thresholds).
- Combining datasets: Mixing datasets from different processes may artificially create or destroy Benford conformity. Analyze homogeneous datasets.
- Approval thresholds: If expenses over $5,000 require VP approval, expect a spike of amounts just below $5,000 (digit 4 in the $4,9xx range). This is a behavioral pattern, flagged by second-digit analysis.
- Sample size matters: Chi-squared test is sensitive to sample size. With 100K+ records, even trivial deviations become statistically significant. Use MAD as primary metric.
References
- For second and third digit extensions, see
references/higher-digit-tests.md - For case studies in fraud detection, see
references/fraud-case-studies.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-benford of asgard-ai-platform/skills.
- SKILL.md
- examples/sample_scenario.md
- references/fraud-case-studies.md
- references/higher-digit-tests.md
Open the folder on GitHubat commit 4e7f4f8
Compare with similar skills
Algo Risk Benford 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 Benford this skillasgard-ai-platform/skills | 242 | — | ~1.1k | Automated safety check: Pass | MIT | |
| Analytics And Advisory IntelligenceLeoYeAI/openclaw-master-skills | 2.2k | — | ~4.5k | Automated safety check: Notes | MIT | |
| Bio Proteomics Data ImportGPTomics/bioSkills | 1.2k | 1 repos | ~4.5k | Automated safety check: Pass | MIT | |
| Finance Opsericosiu/ai-marketing-skills | 3.6k | 1 repos | ~1.4k | Automated safety check: Pass | MIT | |
| Data Throughput Acceleratoraffaan-m/ECC | 277k | 1 repos | ~707 | Automated safety check: Pass | MIT | |
| Funding Rate ArbitrageSuperior-Trade/superior-skills | 215 | — | ~1.9k | Automated safety check: Pass | MIT |
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Related
Categories
Questions about Algo Risk Benford
What does Algo Risk Benford do?
Apply Benford's Law to detect anomalies in numerical datasets by analyzing first-digit frequency distributions. Algo Risk Benford is an agent skill from asgard-ai-platform/skills. Apply Benford's Law to detect anomalies in numerical datasets by analyzing first-digit frequency distributions.
When should I use Algo Risk Benford?
Algo Risk Benford fits situations like: the user needs to audit financial data for fraud indicators; validate data integrity; detect fabricated numbers — even if they say data manipulation detection; first digit test.
How do I install Algo Risk Benford in Claude Code?
Run `npx skills add asgard-ai-platform/skills --skill algo-risk-benford -a claude-code`. Or copy the skill folder (algo-risk-benford in asgard-ai-platform/skills) into .claude/skills/algo-risk-benford in your project. Claude Code loads it when a task matches its description.
How do I install Algo Risk Benford in Codex?
Run `npx skills add asgard-ai-platform/skills --skill algo-risk-benford -a codex`. Or copy the skill folder (algo-risk-benford in asgard-ai-platform/skills) into .agents/skills/algo-risk-benford in your project. Codex loads it when a task matches its description.
Can I use Algo Risk Benford 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-benford -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-benford, .gemini/skills/algo-risk-benford, .github/skills/algo-risk-benford and .opencode/skills/algo-risk-benford in your project.
What does Algo Risk Benford need to run?
SKILL.md names no scripts, command-line tools or credentials: Algo Risk Benford is instructions for the agent only.
Does Algo Risk Benford 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 Benford 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 Benford use?
Algo Risk Benford 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 Benford use?
About 1.1k tokens (SKILL.md is roughly 4.5k 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 5.3k tokens, read only when the agent opens those files.
What are the alternatives to Algo Risk Benford?
Skills that share tags, products or a category with Algo Risk Benford: Analytics And Advisory Intelligence (LeoYeAI/openclaw-master-skills, 2.2k stars), Bio Proteomics Data Import (GPTomics/bioSkills, 1.2k stars), Finance Ops (ericosiu/ai-marketing-skills, 3.6k stars) and Data Throughput Accelerator (affaan-m/ECC, 277k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Algo Risk Benford?
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.