Deepsec Documentation Guide
vercel-labs/deepsec
Points the agent at deepsec's own docs to answer questions about initializing, configuring, resuming, scanning with and extending the vulnerability scanner.
Model fraud and abuse threats across actors, account states, value flows, controls, and monetization paths.
Install Assess Fraud Abuse Model
$ npx skills add cyberful/cyberful --skill assess-fraud-abuse-model -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install cyberful/cyberful assess-fraud-abuse-model --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/assess-fraud-abuse-model .claude/skills/assess-fraud-abuse-model && 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 "assess-fraud-abuse-model" agent skill from https://github.com/cyberful/cyberful/tree/main/cyberful/builtin/skills/assess-fraud-abuse-model into .claude/skills/assess-fraud-abuse-model/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "assess-fraud-abuse-model", 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/assess-fraud-abuse-modelType 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 assess-fraud-abuse-model -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install cyberful/cyberful assess-fraud-abuse-model --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/assess-fraud-abuse-model .agents/skills/assess-fraud-abuse-model && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "assess-fraud-abuse-model" agent skill from https://github.com/cyberful/cyberful/tree/main/cyberful/builtin/skills/assess-fraud-abuse-model into .agents/skills/assess-fraud-abuse-model/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "assess-fraud-abuse-model", 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 assess-fraud-abuse-model -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install cyberful/cyberful assess-fraud-abuse-model --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/assess-fraud-abuse-model .cursor/skills/assess-fraud-abuse-model && 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 "assess-fraud-abuse-model" agent skill from https://github.com/cyberful/cyberful/tree/main/cyberful/builtin/skills/assess-fraud-abuse-model into .cursor/skills/assess-fraud-abuse-model/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "assess-fraud-abuse-model", 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/assess-fraud-abuse-model--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 assess-fraud-abuse-model -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install cyberful/cyberful assess-fraud-abuse-model --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/assess-fraud-abuse-model .gemini/skills/assess-fraud-abuse-model && 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 "assess-fraud-abuse-model" agent skill from https://github.com/cyberful/cyberful/tree/main/cyberful/builtin/skills/assess-fraud-abuse-model into .gemini/skills/assess-fraud-abuse-model/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "assess-fraud-abuse-model", 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 assess-fraud-abuse-modelInstalls 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 assess-fraud-abuse-model -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/assess-fraud-abuse-model .github/skills/assess-fraud-abuse-model && 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 "assess-fraud-abuse-model" agent skill from https://github.com/cyberful/cyberful/tree/main/cyberful/builtin/skills/assess-fraud-abuse-model into .github/skills/assess-fraud-abuse-model/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "assess-fraud-abuse-model", 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 assess-fraud-abuse-model -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 assess-fraud-abuse-model --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/assess-fraud-abuse-model .opencode/skills/assess-fraud-abuse-model && 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 "assess-fraud-abuse-model" agent skill from https://github.com/cyberful/cyberful/tree/main/cyberful/builtin/skills/assess-fraud-abuse-model into .opencode/skills/assess-fraud-abuse-model/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "assess-fraud-abuse-model", 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
assess-fraud-abuse-model- GitHub stars
- 135
- Token cost
- ~716 tokens
- SKILL.md length
- 252 words
- Files
- 5 (incl. references, assets)
- Skills in repo
- 85
- Repo updated
- First seen
- Licence
- AGPL-3.0
At a glance
Model fraud and abuse threats across actors, account states, value flows, controls, and monetization paths.
- An engagement needs a scoped abuse model and coverage plan rather than active testing
- SKILL.md covers Bound the model, Model paths and controls and Produce a coverage decision
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Assess Fraud Abuse Model is an agent skill from cyberful/cyberful. Model fraud and abuse threats across actors, account states, value flows, controls, and monetization paths. Use when an engagement needs a scoped abuse model and coverage plan rather than active testing.
Its SKILL.md is about 720 tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including reference files and assets (for example `agents/openai.yaml`, `assets/fraud-abuse-model.schema.json` and `assets/fraud-abuse-model.template.json`).
It sits in Security. 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
- An engagement needs a scoped abuse model and coverage plan rather than active testing
Example prompts
- “/assess-fraud-abuse-model”
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
No scripts in the folder and no shell commands in SKILL.md.
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
Assess Fraud Abuse Model loads about 716 tokens when it runs, and up to ~1.2k if it reads all its reference files. Until then it costs about 57 tokens; SKILL.md has 252 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 cyberful/cyberful at commit ec598a6, republished under its AGPL-3.0 licence (© cyberful). 252 words, ~716 tokens.
Download SKILL.mdSave it as
.claude/skills/assess-fraud-abuse-model/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- assess-fraud-abuse-model
- description
- Model fraud and abuse threats across actors, account states, value flows, controls, and monetization paths. Use when an engagement needs a scoped abuse model and coverage plan rather than active testing.
- metadata.domain
- application-security
- metadata.subdomain
- fraud-abuse-modeling
- metadata.triggers
- fraud threat model, abuse case modeling, fraud actor analysis, monetization path analysis, fraud control coverage, F3 coverage assessment
- metadata.tags
- fraud, abuse-model, threat-modeling, value-flow, trust-boundary, MITRE-F3
Assess Fraud Abuse Model
Build a falsifiable model that connects permitted actors and starting states to protected value, trust transitions, fraud controls, and durable outcomes. This skill plans coverage; it does not authorize active transactions or treat a framework mapping as evidence.
Bound the model
Record the engagement authorization, products, channels, geographies, tester identities, synthetic instruments, prohibited effects, and evidence sources. Distinguish customer harm, platform loss, merchant loss, regulatory exposure, and operational cost. Read references/model-construction.md when the product spans multiple actors or value ledgers.
Copy assets/fraud-abuse-model.template.json and preserve assets/fraud-abuse-model.schema.json when a durable model is needed. Replace every synthetic field and keep hypotheses separate from observed facts.
Model paths and controls
Trace acquisition, enrollment, funding, authentication, account change, transaction initiation, authorization, execution, settlement, reversal, dispute, payout, recovery, and review where present. For each abuse path record prerequisites, controlled actor, target asset, trust-boundary crossings, product invariants, existing controls, expected evidence, monetization or benefit, and safe stopping conditions.
Use MITRE F3 as a coverage lens only when an abuse behavior genuinely matches a pinned technique. Preserve product-specific paths that have no framework equivalent. Route control artifacts to analyze-fraud-control-evidence, causal state to trace-transaction-state, and active mechanisms to the appropriate payment, entitlement, automation, authorization, or concurrency specialist.
Produce a coverage decision
Prioritize paths by reachable value, control uncertainty, blast radius, reversibility, observability, and evidence quality. State what is covered, deferred, prohibited, or unknown. A model is complete enough when every high-value path has a named invariant, permitted test method, control owner, authoritative evidence source, and cleanup plan.
© 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 4 other files (references, assets) in cyberful/builtin/skills/assess-fraud-abuse-model of cyberful/cyberful.
- SKILL.md
- agents/openai.yaml
- assets/fraud-abuse-model.schema.json
- assets/fraud-abuse-model.template.json
- references/model-construction.md
Open the folder on GitHubat commit ec598a6
Compare with similar skills
Assess Fraud Abuse Model 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 |
|---|---|---|---|---|---|---|
| Assess Fraud Abuse Model this skillcyberful/cyberful | 135 | — | ~716 | Automated safety check: Pass | AGPL-3.0 | |
| Deepsec Documentation Guidevercel-labs/deepsec | 8.1k | — | ~956 | Automated safety check: Pass | Apache-2.0 | |
| Skill Scannergetsentry/skills | 1k | 4 repos | ~2.5k | Automated safety check: Warn | Apache-2.0 | |
| Serenity Aleabitoreddityan-labs/serenity-aleabitoreddit | 481 | 1 repos | ~3.3k | Automated safety check: Pass | None | |
| Security Alert Triageelastic/agent-skills | 592 | 1 repos | ~3.5k | Automated safety check: Notes | Apache-2.0 | |
| Shiro Attack CLISummerSec/ShiroAttack2 | 2.6k | — | ~945 | Automated safety check: Pass | MIT |
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Related
Categories
Questions about Assess Fraud Abuse Model
What does Assess Fraud Abuse Model do?
Model fraud and abuse threats across actors, account states, value flows, controls, and monetization paths. Assess Fraud Abuse Model is an agent skill from cyberful/cyberful. Model fraud and abuse threats across actors, account states, value flows, controls, and monetization paths.
When should I use Assess Fraud Abuse Model?
Assess Fraud Abuse Model fits situations like: an engagement needs a scoped abuse model and coverage plan rather than active testing.
How do I install Assess Fraud Abuse Model in Claude Code?
Run `npx skills add cyberful/cyberful --skill assess-fraud-abuse-model -a claude-code`. Or copy the skill folder (cyberful/builtin/skills/assess-fraud-abuse-model in cyberful/cyberful) into .claude/skills/assess-fraud-abuse-model in your project. Claude Code loads it when a task matches its description.
How do I install Assess Fraud Abuse Model in Codex?
Run `npx skills add cyberful/cyberful --skill assess-fraud-abuse-model -a codex`. Or copy the skill folder (cyberful/builtin/skills/assess-fraud-abuse-model in cyberful/cyberful) into .agents/skills/assess-fraud-abuse-model in your project. Codex loads it when a task matches its description.
Can I use Assess Fraud Abuse Model 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 assess-fraud-abuse-model -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/assess-fraud-abuse-model, .gemini/skills/assess-fraud-abuse-model, .github/skills/assess-fraud-abuse-model and .opencode/skills/assess-fraud-abuse-model in your project.
What does Assess Fraud Abuse Model need to run?
SKILL.md names no scripts, command-line tools or credentials: Assess Fraud Abuse Model is instructions for the agent only.
Does Assess Fraud Abuse Model 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 Assess Fraud Abuse Model 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 Assess Fraud Abuse Model use?
Assess Fraud Abuse Model 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 Assess Fraud Abuse Model use?
About 716 tokens (SKILL.md is roughly 2.9k 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 477 tokens, read only when the agent opens those files.
What are the alternatives to Assess Fraud Abuse Model?
Skills that share tags, products or a category with Assess Fraud Abuse Model: Deepsec Documentation Guide (vercel-labs/deepsec, 8.1k stars), Skill Scanner (getsentry/skills, 1k stars), Serenity Aleabitoreddit (yan-labs/serenity-aleabitoreddit, 481 stars) and Security Alert Triage (elastic/agent-skills, 592 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Assess Fraud Abuse Model?
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.