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AC hunt-graphql

Hunting skill for graphql vulnerabilities. Built from 12 public bug bounty reports across IDOR via node() / GID, mutation IDOR including AI/LLM features, cross-tenant IDOR, SSRF via argument, batching-DoS, query-cost-bypass, SQLi via argument, broken-object-level-authz, auth-bypass via unscoped mutations, and PII exposure from missing field-level authz. Use when hunting graphql on any target.

elementalsouls/Claude-BugHunter Agent Skills author: elementalsouls 1 file body ≈ 4 987 tokens Open the sourcegithub.com↗ analyzed 2 h ago

Hunting skill for graphql vulnerabilities.

As a process C 58/100 · Has gaps — weak spots: result and completion, when it triggers, progress reporting

ProcedureGitHubShopifyGitLabStripeData and analyticsResearchAI and agentstype 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
A
92/100
safety, quality, tests
Safety 60%
98
Quality 40%
82
Run on models
none yet
Process rating
C
58/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
0
When it triggers w 12
20
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 · 2

    ✓ No critical or high findings

    Medium and low: 2
    • low Risky intent intent-offensive-security SKILL.md:3
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      description: Hunting skill for graphql vulnerabilities. Built from 12 public bug bounty reports across IDOR via node() / GID, mutation IDOR including AI/LLM features, cross-tenant IDOR, SSRF via argum
    • low Risky intent intent-offensive-security SKILL.md:10
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      GraphQL vulnerabilities are high-value because the attack surface is both broad and deep — a single endpoint can expose entire data models, privilege escalation paths, and cross-API state confusion. H

    Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "sources"
    • note frontmatter-key unknown frontmatter key "report_count"

    Process rating: all ten parameters 58/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 55Failures and branches. 1 branches
    • 60Tools and files. Uses tools (web, node) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 70Execution cost. Instruction body is 4987 tokens
    • 100Steps. 67 steps
    • 100Consistency. Name and required fields are in place
    • 100Running it twice. Mutating operations check current state
    • low 10 top-level sections: this looks like several domains in one skill
    • low The response is described with custom markup (4 tags): a typed call is more reliable

    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
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 395: enough signal without eating the budget
    • +4Structure: 11 headings
    • +3Step-by-step instructions: 67 items
    • +4Has examples (16 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.