Product Methodology
magnus919/hermes-profiles
Product management frameworks embedded in references/: RICE, MoSCoW, Opportunity Solution Trees, customer interviews, spec template, stakeholder communication, decision log.
Assess AI-system risk from architecture, intended use, affected actors, model limitations, data lineage, autonomy, human oversight, monitoring, and failure consequences.
Install Assess AI System Risk
$ npx skills add cyberful/cyberful --skill assess-ai-system-risk -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install cyberful/cyberful assess-ai-system-risk --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-ai-system-risk .claude/skills/assess-ai-system-risk && 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-ai-system-risk" agent skill from https://github.com/cyberful/cyberful/tree/main/cyberful/builtin/skills/assess-ai-system-risk into .claude/skills/assess-ai-system-risk/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "assess-ai-system-risk", 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-ai-system-riskType 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-ai-system-risk -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install cyberful/cyberful assess-ai-system-risk --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-ai-system-risk .agents/skills/assess-ai-system-risk && 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-ai-system-risk" agent skill from https://github.com/cyberful/cyberful/tree/main/cyberful/builtin/skills/assess-ai-system-risk into .agents/skills/assess-ai-system-risk/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "assess-ai-system-risk", 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-ai-system-risk -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install cyberful/cyberful assess-ai-system-risk --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-ai-system-risk .cursor/skills/assess-ai-system-risk && 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-ai-system-risk" agent skill from https://github.com/cyberful/cyberful/tree/main/cyberful/builtin/skills/assess-ai-system-risk into .cursor/skills/assess-ai-system-risk/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "assess-ai-system-risk", 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-ai-system-risk--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-ai-system-risk -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install cyberful/cyberful assess-ai-system-risk --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-ai-system-risk .gemini/skills/assess-ai-system-risk && 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-ai-system-risk" agent skill from https://github.com/cyberful/cyberful/tree/main/cyberful/builtin/skills/assess-ai-system-risk into .gemini/skills/assess-ai-system-risk/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "assess-ai-system-risk", 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-ai-system-riskInstalls 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-ai-system-risk -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-ai-system-risk .github/skills/assess-ai-system-risk && 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-ai-system-risk" agent skill from https://github.com/cyberful/cyberful/tree/main/cyberful/builtin/skills/assess-ai-system-risk into .github/skills/assess-ai-system-risk/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "assess-ai-system-risk", 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-ai-system-risk -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-ai-system-risk --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-ai-system-risk .opencode/skills/assess-ai-system-risk && 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-ai-system-risk" agent skill from https://github.com/cyberful/cyberful/tree/main/cyberful/builtin/skills/assess-ai-system-risk into .opencode/skills/assess-ai-system-risk/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "assess-ai-system-risk", 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-ai-system-risk- GitHub stars
- 135
- Token cost
- ~566 tokens
- SKILL.md length
- 163 words
- Files
- 4 (incl. references, assets)
- Skills in repo
- 85
- Repo updated
- First seen
- Licence
- AGPL-3.0
At a glance
Assess AI-system risk from architecture, intended use, affected actors, model limitations, data lineage, autonomy, human oversight, monitoring, and failure consequences.
- Evidence-based NIST AI RMF-aligned posture reviews
- SKILL.md covers Establish context and…, Reconcile controls and evidence and Deliver
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Design decisions
What it does
Assess AI System Risk is an agent skill from cyberful/cyberful. Assess AI-system risk from architecture, intended use, affected actors, model limitations, data lineage, autonomy, human oversight, monitoring, and failure consequences. Use for evidence-based NIST AI RMF-aligned posture reviews, design decisions, control gaps, and residual-risk prioritization without active exploitation.
Its SKILL.md is about 570 tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including reference files and assets (for example `agents/openai.yaml`, `assets/ai-risk-register.template.json` and `references/risk-evidence-method.md`).
It sits in Product & Project Management, covering Penetration testing, AI governance and Architecture decision records. 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
- Evidence-based NIST AI RMF-aligned posture reviews
- Design decisions
- Residual-risk prioritization without active exploitation
Example prompts
- “/assess-ai-system-risk”
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 AI System Risk loads about 566 tokens when it runs, and up to ~734 if it reads all its reference files. Until then it costs about 86 tokens; SKILL.md has 163 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). 163 words, ~566 tokens.
Download SKILL.mdSave it as
.claude/skills/assess-ai-system-risk/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.- name
- assess-ai-system-risk
- description
- Assess AI-system risk from architecture, intended use, affected actors, model limitations, data lineage, autonomy, human oversight, monitoring, and failure consequences. Use for evidence-based NIST AI RMF-aligned posture reviews, design decisions, control gaps, and residual-risk prioritization without active exploitation.
- metadata.domain
- ai-security
- metadata.subdomain
- risk-assessment
- metadata.triggers
- AI system risk assessment, NIST AI RMF review, AI control posture, model risk register, agent architecture risk
- metadata.tags
- AI-risk, NIST-AI-RMF, governance, impact-assessment, human-oversight, residual-risk
Assess AI System Risk
Assess the implemented socio-technical system, not the model in isolation. Keep facts, assumptions, test evidence, and policy claims distinct.
Establish context and consequences
Map intended and foreseeable use, affected actors, decision criticality, autonomy, reversibility, data sensitivity, model and provider routes, retrieval/memory, tools, human oversight, deployment environments, and incident ownership. Start from unacceptable outcomes and trace the capabilities and conditions required for each.
Copy assets/ai-risk-register.template.json into the workarea. Read references/risk-evidence-method.md before assigning likelihood, consequence, or confidence.
Reconcile controls and evidence
Evaluate governance, provenance, data quality, evaluation coverage, identity and authorization, isolation, output handling, monitoring, fallback, change management, incident response, recovery, and retirement. Route concrete tests to the relevant audit-, trace-, or test- skill; do not infer technical effectiveness from policy text.
Deliver
Produce scoped risks tied to assets and affected actors, evidence grade, existing controls, control owner, uncertainty, treatment decision, validation plan, residual risk, and review trigger. Avoid a single opaque score when likelihood or consequence depends on deployment conditions.
© 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 3 other files (references, assets) in cyberful/builtin/skills/assess-ai-system-risk of cyberful/cyberful.
- SKILL.md
- agents/openai.yaml
- assets/ai-risk-register.template.json
- references/risk-evidence-method.md
Open the folder on GitHubat commit ec598a6
Compare with similar skills
Assess AI System Risk 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 AI System Risk this skillcyberful/cyberful | 135 | — | ~566 | Automated safety check: Pass | AGPL-3.0 | |
| Product Methodologymagnus919/hermes-profiles | 289 | — | ~1.5k | Automated safety check: Pass | MIT | |
| Building Ioc Enrichment Pipeline With Openctimukul975/Anthropic-Cybersecurity-Skills | 34k | — | ~2.5k | Automated safety check: Pass | Apache-2.0 | |
| Threat Detectionalirezarezvani/claude-skills | 28k | — | ~3.5k | Automated safety check: Pass | MIT | |
| Oma Architecturefirst-fluke/oh-my-agent | 1.3k | — | ~2.1k | Automated safety check: Pass | MIT | |
| Securitytelagod/code-abyss | 244 | — | ~907 | Automated safety check: Pass | MIT |
Similar skills
Building Ioc Enrichment Pipeline With Opencti
mukul975/Anthropic-Cybersecurity-Skills
Build an automated IOC enrichment pipeline on OpenCTI (STIX 2.1 native threat intel platform) using its internal enrichment connectors to pull context from VirusTotal, Shodan, AbuseIPDB, and…
Threat Detection
alirezarezvani/claude-skills
A skill your agent uses when hunting for threats in an environment, analyzing IOCs, or detecting behavioral anomalies in telemetry.
Oma Architecture
first-fluke/oh-my-agent
Architecture specialist for software/system design, module and service boundaries, tradeoff analysis, and stakeholder synthesis.
Security
telagod/code-abyss
Defensive security engineering judgment, distilled from a stronger model - invoke when THREAT MODELING a system or feature; making security-relevant design decisions (auth, crypto, trust boundaries…
Vulnerability Management
cbrock84/headcount
Runs the loop from discovering a weakness to confirming it is fixed — scanning, triage, prioritization by real exploitability, remediation tracking, and patch policy.
More from cyberful/cyberful
All 85 skills in this repoAudit Infrastructure As Code
cyberful/cyberful
Audit infrastructure-as-code artifacts for unsafe defaults, policy gaps, privilege exposure, control drift, and deployment-impact evidence.
Audit Kubernetes Policy Enforcement
cyberful/cyberful
Audit Kubernetes admission and policy-as-code enforcement against local workload manifests, exception paths, namespace scope, and deployment evidence.
Audit Pci Dss Penetration Test Evidence
cyberful/cyberful
Audit PCI DSS penetration-test methodology, scope, internal and external reports, segmentation results, tester independence, remediation, retesting, retention, and multi-tenant support evidence.
Operate Content Discovery
cyberful/cyberful
Design and interpret advanced content discovery with ffuf and complementary web fuzzers.
Operate Network Recon
cyberful/cyberful
Build a high-fidelity network and service inventory using Nmap, Masscan, packet capture, DNS, and protocol-specific follow-up.
Operate Sast Toolchain
cyberful/cyberful
Operate Semgrep and source-oriented static analysis as a hypothesis, coverage, and regression system during advanced code audits.
Related
Questions about Assess AI System Risk
What does Assess AI System Risk do?
Assess AI-system risk from architecture, intended use, affected actors, model limitations, data lineage, autonomy, human oversight, monitoring, and failure consequences. Assess AI System Risk is an agent skill from cyberful/cyberful. Assess AI-system risk from architecture, intended use, affected actors, model limitations, data lineage, autonomy, human oversight, monitoring, and failure consequences.
When should I use Assess AI System Risk?
Assess AI System Risk fits situations like: evidence-based NIST AI RMF-aligned posture reviews; design decisions; residual-risk prioritization without active exploitation.
How do I install Assess AI System Risk in Claude Code?
Run `npx skills add cyberful/cyberful --skill assess-ai-system-risk -a claude-code`. Or copy the skill folder (cyberful/builtin/skills/assess-ai-system-risk in cyberful/cyberful) into .claude/skills/assess-ai-system-risk in your project. Claude Code loads it when a task matches its description.
How do I install Assess AI System Risk in Codex?
Run `npx skills add cyberful/cyberful --skill assess-ai-system-risk -a codex`. Or copy the skill folder (cyberful/builtin/skills/assess-ai-system-risk in cyberful/cyberful) into .agents/skills/assess-ai-system-risk in your project. Codex loads it when a task matches its description.
Can I use Assess AI System Risk 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-ai-system-risk -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-ai-system-risk, .gemini/skills/assess-ai-system-risk, .github/skills/assess-ai-system-risk and .opencode/skills/assess-ai-system-risk in your project.
What does Assess AI System Risk need to run?
SKILL.md names no scripts, command-line tools or credentials: Assess AI System Risk is instructions for the agent only.
Does Assess AI System Risk 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 AI System Risk 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 AI System Risk use?
Assess AI System Risk 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 AI System Risk use?
About 566 tokens (SKILL.md is roughly 2.3k 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 168 tokens, read only when the agent opens those files.
What are the alternatives to Assess AI System Risk?
Skills that share tags, products or a category with Assess AI System Risk: Product Methodology (magnus919/hermes-profiles, 289 stars), Building Ioc Enrichment Pipeline With Opencti (mukul975/Anthropic-Cybersecurity-Skills, 34k stars), Threat Detection (alirezarezvani/claude-skills, 28k stars) and Oma Architecture (first-fluke/oh-my-agent, 1.3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Assess AI System Risk?
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.