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CC testing-mobile-api-authentication

Tests authentication and authorization mechanisms in mobile application APIs to identify broken authentication, insecure token management, session fixation, privilege escalation, and IDOR vulnerabilities. Use when performing API security assessments against mobile app backends, testing JWT implementations, evaluating OAuth flows, or assessing session management. Activates for requests involving mobile API auth testing, token security assessment, OAuth mobile flow testing, or API authorization bypass.

mukul975/Anthropic-Cybersecurity-Skills Agent Skills author: mukul975 Apache-2.0 8 files · 2 scripts body ≈ 1 562 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Tests authentication and authorization mechanisms in mobile application APIs to identify broken authentication, insecure token management, session fixation…

As a process C 57/100 · Has gaps — weak spots: result and completion, failures and branches, progress reporting

IntegrationSecuritytype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
C
89/100
safety, quality, tests
Safety 60%
96
Quality 40%
79
Run on models
none yet
Process rating
C
57/100
Has gaps
Result and completion w 14
0
Failures and branches w 10
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 4

    ✓ No critical or high findings

    Medium and low: 4
    • low Risky intent intent-offensive-security references/standards.md:8
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      | M3 | Insecure Authentication/Authorization | Auth bypass, IDOR, privilege escalation |
    • low Risky intent intent-offensive-security references/workflows.md:17
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      [Privilege escalation]
    • low Risky intent intent-offensive-security SKILL.md:5
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      privilege escalation, and IDOR vulnerabilities. Use when performing API security
    • low Risky intent intent-offensive-security SKILL.md:137
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      # Test horizontal privilege escalation

    Files scanned: 8. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "domain"
    • note frontmatter-key unknown frontmatter key "subdomain"
    • note frontmatter-key unknown frontmatter key "nist_csf"
    • note frontmatter-key unknown frontmatter key "mitre_attack"

    Process rating: all ten parameters 57/100

    • 0Result and completion. Does not say what the result is
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 18 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1562 tokens
    • 100Running it twice. No mutating operations

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +3Output format is not stated: the model decides each time
    • -43 reference files, but SKILL.md never points to them: the model will not open them
    • -32 of 2 scripts are never mentioned in SKILL.md
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 505: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 18 items
    • +4Has examples (7 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.