Audit Prep
ccashwell/evm-cortex
A skill your agent uses when preparing a codebase for security audit.
Normalize and compare authorized fraud-control observations from local evidence, highlighting decision drift, coverage gaps, and conflicting outcomes without making a fraud or vulnerability verdict.
Install Analyze Fraud Control Evidence
$ npx skills add cyberful/cyberful --skill analyze-fraud-control-evidence -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install cyberful/cyberful analyze-fraud-control-evidence --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/cyberful/cyberful.git skills-src && mkdir -p .claude/skills && cp -r skills-src/cyberful/builtin/skills/analyze-fraud-control-evidence .claude/skills/analyze-fraud-control-evidence && 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 "analyze-fraud-control-evidence" agent skill from https://github.com/cyberful/cyberful/tree/main/cyberful/builtin/skills/analyze-fraud-control-evidence into .claude/skills/analyze-fraud-control-evidence/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyze-fraud-control-evidence", 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/cyberful/cyberful/tree/main/cyberful/builtin/skills/analyze-fraud-control-evidenceType 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 cyberful/cyberful --skill analyze-fraud-control-evidence -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install cyberful/cyberful analyze-fraud-control-evidence --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/cyberful/cyberful.git skills-src && mkdir -p .agents/skills && cp -r skills-src/cyberful/builtin/skills/analyze-fraud-control-evidence .agents/skills/analyze-fraud-control-evidence && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "analyze-fraud-control-evidence" agent skill from https://github.com/cyberful/cyberful/tree/main/cyberful/builtin/skills/analyze-fraud-control-evidence into .agents/skills/analyze-fraud-control-evidence/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyze-fraud-control-evidence", 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 cyberful/cyberful --skill analyze-fraud-control-evidence -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install cyberful/cyberful analyze-fraud-control-evidence --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/cyberful/cyberful.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/cyberful/builtin/skills/analyze-fraud-control-evidence .cursor/skills/analyze-fraud-control-evidence && 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 "analyze-fraud-control-evidence" agent skill from https://github.com/cyberful/cyberful/tree/main/cyberful/builtin/skills/analyze-fraud-control-evidence into .cursor/skills/analyze-fraud-control-evidence/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyze-fraud-control-evidence", 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/cyberful/cyberful.git --path cyberful/builtin/skills/analyze-fraud-control-evidence--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 cyberful/cyberful --skill analyze-fraud-control-evidence -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install cyberful/cyberful analyze-fraud-control-evidence --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/cyberful/cyberful.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/cyberful/builtin/skills/analyze-fraud-control-evidence .gemini/skills/analyze-fraud-control-evidence && 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 "analyze-fraud-control-evidence" agent skill from https://github.com/cyberful/cyberful/tree/main/cyberful/builtin/skills/analyze-fraud-control-evidence into .gemini/skills/analyze-fraud-control-evidence/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyze-fraud-control-evidence", 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 cyberful/cyberful analyze-fraud-control-evidenceInstalls 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 cyberful/cyberful --skill analyze-fraud-control-evidence -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/cyberful/cyberful.git skills-src && mkdir -p .github/skills && cp -r skills-src/cyberful/builtin/skills/analyze-fraud-control-evidence .github/skills/analyze-fraud-control-evidence && 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 "analyze-fraud-control-evidence" agent skill from https://github.com/cyberful/cyberful/tree/main/cyberful/builtin/skills/analyze-fraud-control-evidence into .github/skills/analyze-fraud-control-evidence/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyze-fraud-control-evidence", 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 cyberful/cyberful --skill analyze-fraud-control-evidence -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install cyberful/cyberful analyze-fraud-control-evidence --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/cyberful/cyberful.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/cyberful/builtin/skills/analyze-fraud-control-evidence .opencode/skills/analyze-fraud-control-evidence && 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 "analyze-fraud-control-evidence" agent skill from https://github.com/cyberful/cyberful/tree/main/cyberful/builtin/skills/analyze-fraud-control-evidence into .opencode/skills/analyze-fraud-control-evidence/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyze-fraud-control-evidence", 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
analyze-fraud-control-evidence- GitHub stars
- 135
- Token cost
- ~649 tokens
- SKILL.md length
- 195 words
- Files
- 9 (incl. scripts, references, assets)
- Skills in repo
- 85
- Repo updated
- First seen
- Licence
- AGPL-3.0
At a glance
Normalize and compare authorized fraud-control observations from local evidence, highlighting decision drift, coverage gaps, and conflicting outcomes without making a fraud or vulnerability verdict.
- Tasks that involve Test coverage
- SKILL.md covers Prepare observations, Normalize deterministically and Interpret with control context
- Runs Python scripts from its folder
What it does
Analyze Fraud Control Evidence is an agent skill from cyberful/cyberful. Normalize and compare authorized fraud-control observations from local evidence, highlighting decision drift, coverage gaps, and conflicting outcomes without making a fraud or vulnerability verdict.
Its SKILL.md is about 650 tokens, which your agent loads only when the skill is triggered. The skill folder holds 13 other files, including scripts, reference files and assets (for example `agents/openai.yaml`, `assets/fraud-control-analysis.schema.json` and `assets/fraud-control-observations.example.json`).
It sits in Security, covering Test coverage. The repository describes itself as: Cyberful is an open-source AI Red Team for discovering, exploiting, verifying, and remediating vulnerabilities. The licence is AGPL-3.0.
When your agent uses it
- Tasks that involve Test coverage
Example prompts
- “/analyze-fraud-control-evidence”
Requirements
- Python 3
What it can do on your machine
Read from SKILL.md and the folder at commit ec598a6. 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 2 files 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
Analyze Fraud Control Evidence loads about 649 tokens when it runs, and up to ~1k if it reads all its reference files. Until then it costs about 57 tokens; SKILL.md has 195 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); the scripts in this folder are not scanned.
SKILL.md
The full file from cyberful/cyberful at commit ec598a6, republished under its AGPL-3.0 licence (© cyberful). 195 words, ~649 tokens.
Download SKILL.mdSave it as
.claude/skills/analyze-fraud-control-evidence/SKILL.md (or your agent's skills folder). This skill also uses 8 other files; get the full folder from GitHub.- name
- analyze-fraud-control-evidence
- description
- Normalize and compare authorized fraud-control observations from local evidence, highlighting decision drift, coverage gaps, and conflicting outcomes without making a fraud or vulnerability verdict.
- metadata.domain
- application-security
- metadata.subdomain
- fraud-control-evidence
- metadata.triggers
- fraud control evidence analysis, risk decision comparison, fraud decision drift, control reason code analysis, anti-fraud evidence ledger, fraud control…
- metadata.tags
- fraud, control-evidence, decision-analysis, reason-codes, offline-analysis
Analyze Fraud Control Evidence
Turn already-collected, authorized decision artifacts into a deterministic comparison ledger. Analyze control behavior and evidence quality; do not infer an actor's intent, label a customer as fraudulent, or promote a mismatch directly into a vulnerability finding.
Prepare observations
Read references/control-evidence.md before combining decisions from different policy versions, channels, or enforcement points. One observation must identify the scenario, control, lifecycle stage, actor, channel, expected and observed decision, reason codes, signal references, durable effect, and authoritative evidence reference. Use pseudonymous or synthetic identifiers.
Copy assets/fraud-control-observations.example.json and preserve assets/fraud-control-observations.schema.json. The input is a local evidence index, not raw customer data or credentials.
Normalize deterministically
Run scripts/run_fraud_control_analysis.py in the workarea. The offline analyzer validates and sorts observations, counts stage and decision coverage, records expected-versus-observed comparisons, and identifies conflicting decisions for the same scenario and control. Its bounded raw output follows assets/fraud-control-analysis.schema.json.
Interpret with control context
Reconcile mismatches against policy version, signal freshness, experiment assignment, review queues, fail-open behavior, and downstream enforcement. A challenge, denial, or review decision is not proof that value movement was prevented; a nominal allow is not proof of abuse. Link decision evidence to authoritative state or ledger effects before reporting impact.
© cyberful, AGPL-3.0. 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 8 other files (scripts, references, assets) in cyberful/builtin/skills/analyze-fraud-control-evidence of cyberful/cyberful.
- SKILL.md
- agents/openai.yaml
- assets/fraud-control-analysis.schema.json
- assets/fraud-control-observations.example.json
- assets/fraud-control-observations.schema.json
- references/control-evidence.md
- scripts/manifest.json
- scripts/run_fraud_control_analysis.py
- tests/test_run_fraud_control_analysis.py
Open the folder on GitHubat commit ec598a6
Compare with similar skills
Analyze Fraud Control Evidence 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 |
|---|---|---|---|---|---|---|
| Analyze Fraud Control Evidence this skillcyberful/cyberful | 135 | — | ~649 | Automated safety check: Pass | AGPL-3.0 | |
| Audit Prepccashwell/evm-cortex | 131 | — | ~1.4k | Automated safety check: Pass | MIT | |
| Analyzing Threat Actor Ttps With Mitre Navigatormukul975/Anthropic-Cybersecurity-Skills | 34k | — | ~667 | Automated safety check: Pass | Apache-2.0 | |
| Mavenskjolber/3d-bin-container-packing | 569 | — | ~886 | Automated safety check: Pass | Apache-2.0 | |
| Audit PrepPlamenTSV/plamen | 303 | — | ~3.7k | Automated safety check: Pass | MIT | |
| Audit Prep Assistanttrailofbits/skills | 7.5k | — | ~2.5k | Automated safety check: Pass | CC-BY-SA-4.0 |
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Categories
Questions about Analyze Fraud Control Evidence
What does Analyze Fraud Control Evidence do?
Normalize and compare authorized fraud-control observations from local evidence, highlighting decision drift, coverage gaps, and conflicting outcomes without making a fraud or vulnerability verdict. Analyze Fraud Control Evidence is an agent skill from cyberful/cyberful. Normalize and compare authorized fraud-control observations from local evidence, highlighting decision drift, coverage gaps, and conflicting outcomes without making a fraud or vulnerability verdict.
When should I use Analyze Fraud Control Evidence?
Analyze Fraud Control Evidence fits situations like: tasks that involve Test coverage.
How do I install Analyze Fraud Control Evidence in Claude Code?
Run `npx skills add cyberful/cyberful --skill analyze-fraud-control-evidence -a claude-code`. Or copy the skill folder (cyberful/builtin/skills/analyze-fraud-control-evidence in cyberful/cyberful) into .claude/skills/analyze-fraud-control-evidence in your project. Claude Code loads it when a task matches its description.
How do I install Analyze Fraud Control Evidence in Codex?
Run `npx skills add cyberful/cyberful --skill analyze-fraud-control-evidence -a codex`. Or copy the skill folder (cyberful/builtin/skills/analyze-fraud-control-evidence in cyberful/cyberful) into .agents/skills/analyze-fraud-control-evidence in your project. Codex loads it when a task matches its description.
Can I use Analyze Fraud Control Evidence 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 cyberful/cyberful --skill analyze-fraud-control-evidence -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/analyze-fraud-control-evidence, .gemini/skills/analyze-fraud-control-evidence, .github/skills/analyze-fraud-control-evidence and .opencode/skills/analyze-fraud-control-evidence in your project.
What does Analyze Fraud Control Evidence need to run?
Going by SKILL.md and its folder, Analyze Fraud Control Evidence needs Python for the scripts in its folder. Our summary lists: Python 3.
Does Analyze Fraud Control Evidence 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 Analyze Fraud Control Evidence 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 Analyze Fraud Control Evidence use?
Analyze Fraud Control Evidence is published under the AGPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Analyze Fraud Control Evidence use?
About 649 tokens (SKILL.md is roughly 2.6k 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 377 tokens, read only when the agent opens those files.
What are the alternatives to Analyze Fraud Control Evidence?
Skills that share tags, products or a category with Analyze Fraud Control Evidence: Audit Prep (ccashwell/evm-cortex, 131 stars), Analyzing Threat Actor Ttps With Mitre Navigator (mukul975/Anthropic-Cybersecurity-Skills, 34k stars), Maven (skjolber/3d-bin-container-packing, 569 stars) and Audit Prep (PlamenTSV/plamen, 303 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Analyze Fraud Control Evidence?
cyberful (a GitHub organization) maintains it in cyberful/cyberful, which has 135 GitHub stars. The repository holds 85 skills in this directory. The repository was last updated on August 24, 2026.
Source: cyberful/cyberful on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.