Agent skill

Test AI Tool Authorization

by cyberful in cyberful/cyberful

Test deterministic authorization around AI tool discovery, selection, canonical arguments, credentials, tenants, destinations, approvals, delegation, retries, and effects.

AGPL-3.0Auto-check passedBackend & APIs

Install Test AI Tool Authorization

skills CLI
$ npx skills add cyberful/cyberful --skill test-ai-tool-authorization -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install cyberful/cyberful test-ai-tool-authorization --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/cyberful/cyberful.git skills-src && mkdir -p .claude/skills && cp -r skills-src/cyberful/builtin/skills/test-ai-tool-authorization .claude/skills/test-ai-tool-authorization && rm -rf skills-src

Use ~/.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/

Facts

Skill name
test-ai-tool-authorization
GitHub stars
135
Token cost
~549 tokens
SKILL.md length
149 words
Files
9 (incl. scripts, references, assets)
Skills in repo
85
Repo updated
First seen
Licence
AGPL-3.0

At a glance

Test deterministic authorization around AI tool discovery, selection, canonical arguments, credentials, tenants, destinations, approvals, delegation, retries, and effects.

  • Model refusal is insufficient and the host
  • SKILL.md covers Build the action matrix and Confirm the enforcement gap
  • Runs Python scripts from its folder
  • Gateway must enforce an authorized capability boundary

What it does

Test AI Tool Authorization is an agent skill from cyberful/cyberful. Test deterministic authorization around AI tool discovery, selection, canonical arguments, credentials, tenants, destinations, approvals, delegation, retries, and effects. Use when model refusal is insufficient and the host or gateway must enforce an authorized capability boundary.

Its SKILL.md is about 550 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/tool-authorization-evidence.schema.json` and `assets/tool-authorization-probe.example.json`).

It sits in Backend & APIs, covering Authorization and RBAC. 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

  • Model refusal is insufficient and the host
  • Gateway must enforce an authorized capability boundary

Example prompts

  • “/test-ai-tool-authorization”

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

Test AI Tool Authorization loads about 549 tokens when it runs, and up to ~774 if it reads all its reference files. Until then it costs about 77 tokens; SKILL.md has 149 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~77
When it runs · the whole SKILL.md, loaded when a task matches
~549
With references · SKILL.md plus every file in references/, read only if the agent opens them
~774

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). 149 words, ~549 tokens.

Download SKILL.mdSave it as .claude/skills/test-ai-tool-authorization/SKILL.md (or your agent's skills folder). This skill also uses 8 other files; get the full folder from GitHub.
name
test-ai-tool-authorization
description
Test deterministic authorization around AI tool discovery, selection, canonical arguments, credentials, tenants, destinations, approvals, delegation, retries, and effects. Use when model refusal is insufficient and the host or gateway must enforce an authorized capability boundary.
metadata.domain
ai-security
metadata.subdomain
tool-authorization
metadata.triggers
test AI tool authorization, agent capability boundary, MCP tool authorization, tool approval binding, delegated agent authority
metadata.tags
tool-calling, authorization, MCP, approvals, canonicalization, least-privilege

Test AI Tool Authorization

Treat authorization as a host decision over a canonical action. Model refusal, hidden instructions, and tool descriptions are not enforcement.

Build the action matrix

Record actor, tenant, session, model route, tool identity/schema, requested arguments, resolved resource, destination, credential, approval, effect, retry/delegation context, and expected decision. Read references/tool-authorization-matrix.md for negative cases.

Stage scripts/run_tool_authorization_probe.py, its manifest, and the probe example for matched allowed/denied HTTP actions. Its JSON carries defense-in-depth campaign constraints, never authority. Actual authorization and non-loopback routing stay in Cyberful's mission-bound gateway or ZAP route, using only runtime standard proxy and CA environment. Reflected credentials are redacted before cumulative-bounded evidence is retained.

Confirm the enforcement gap

Compare canonical action and external effect, not response prose. Test resource, tenant, destination, recipient, amount, scope, credential, approval freshness, retry, fallback, and delegation boundaries using tester-owned fixtures. Report the smallest unauthorized effect, the missing enforcement owner, control comparison, and cleanup.

© 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/test-ai-tool-authorization of cyberful/cyberful.

  • SKILL.md
  • agents/openai.yaml
  • assets/tool-authorization-evidence.schema.json
  • assets/tool-authorization-probe.example.json
  • assets/tool-authorization-probe.schema.json
  • references/tool-authorization-matrix.md
  • scripts/manifest.json
  • scripts/run_tool_authorization_probe.py
  • tests/test_run_tool_authorization_probe.py

Open the folder on GitHubat commit ec598a6

Compare with similar skills

Test AI Tool Authorization 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.

Test AI Tool Authorization compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Test AI Tool Authorization this skillcyberful/cyberful135—~549Automated safety check: PassAGPL-3.0
Implementing Device Posture Assessment In Zero Trustmukul975/Anthropic-Cybersecurity-Skills34k—~4.1kAutomated safety check: PassApache-2.0
Audit Reconccashwell/evm-cortex131—~1.5kAutomated safety check: PassMIT
Executing Nist Rmf Authorization To Operatemukul975/Anthropic-Cybersecurity-Skills34k—~2.2kAutomated safety check: PassApache-2.0
Sec Checkwaynesutton/markdown-site628—~753Automated safety check: PassMIT
Security Threat Modelmajiayu000/spellbook287—~561Automated safety check: PassMIT

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Questions about Test AI Tool Authorization

What does Test AI Tool Authorization do?

Test deterministic authorization around AI tool discovery, selection, canonical arguments, credentials, tenants, destinations, approvals, delegation, retries, and effects. Test AI Tool Authorization is an agent skill from cyberful/cyberful. Test deterministic authorization around AI tool discovery, selection, canonical arguments, credentials, tenants, destinations, approvals, delegation, retries, and effects.

When should I use Test AI Tool Authorization?

Test AI Tool Authorization fits situations like: model refusal is insufficient and the host; gateway must enforce an authorized capability boundary.

How do I install Test AI Tool Authorization in Claude Code?

Run `npx skills add cyberful/cyberful --skill test-ai-tool-authorization -a claude-code`. Or copy the skill folder (cyberful/builtin/skills/test-ai-tool-authorization in cyberful/cyberful) into .claude/skills/test-ai-tool-authorization in your project. Claude Code loads it when a task matches its description.

How do I install Test AI Tool Authorization in Codex?

Run `npx skills add cyberful/cyberful --skill test-ai-tool-authorization -a codex`. Or copy the skill folder (cyberful/builtin/skills/test-ai-tool-authorization in cyberful/cyberful) into .agents/skills/test-ai-tool-authorization in your project. Codex loads it when a task matches its description.

Can I use Test AI Tool Authorization 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 test-ai-tool-authorization -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/test-ai-tool-authorization, .gemini/skills/test-ai-tool-authorization, .github/skills/test-ai-tool-authorization and .opencode/skills/test-ai-tool-authorization in your project.

What does Test AI Tool Authorization need to run?

Going by SKILL.md and its folder, Test AI Tool Authorization needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Test AI Tool Authorization 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 Test AI Tool Authorization 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 Test AI Tool Authorization use?

Test AI Tool Authorization 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 Test AI Tool Authorization use?

About 549 tokens (SKILL.md is roughly 2.2k 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 225 tokens, read only when the agent opens those files.

What are the alternatives to Test AI Tool Authorization?

Skills that share tags, products or a category with Test AI Tool Authorization: Implementing Device Posture Assessment In Zero Trust (mukul975/Anthropic-Cybersecurity-Skills, 34k stars), Audit Recon (ccashwell/evm-cortex, 131 stars), Executing Nist Rmf Authorization To Operate (mukul975/Anthropic-Cybersecurity-Skills, 34k stars) and Sec Check (waynesutton/markdown-site, 628 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Test AI Tool Authorization?

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.