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CC hunt-sqli

Hunting skill for sqli vulnerabilities.

sickn33/agentic-awesome-skills Agent Skills author: sickn33 MIT 1 file body ≈ 5 851 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Hunting skill for sqli vulnerabilities.

As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches

ProcedureGitHubPostgreSQLMySQLMongoDBSecuritytype 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
83/100
safety, quality, tests
Safety 60%
98
Quality 40%
61
Run on models
none yet
Process rating
C
51/100
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
This is a copy of a skill from another catalog; the rating counts the canonical one: hunt-sqli (sickn33/agentic-awesome-skills)

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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:42
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (detector / deny-list definition)
    SQL injection remains one of the highest-paying vulnerability classes in bug bounty because it directly threatens data confidentiality, integrity, and availability at scale.
    detector
  • low Risky intent intent-offensive-security SKILL.md:387
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (detector / deny-list definition)
    A company's marketing site ran WordPress with the Huge IT Video Gallery plugin. The plugin's `album_id` parameter was unparameterized. Because the site shared database credentials with other services,
    detector

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

Against the Agent Skills spec

  • warning description-short description under 40 chars: too little signal for triggering
  • warning body-long SKILL.md body ≈ 5851 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "risk"
  • note frontmatter-key unknown frontmatter key "source"
  • note frontmatter-key unknown frontmatter key "source_repo"
  • note frontmatter-key unknown frontmatter key "source_type"
  • note frontmatter-key unknown frontmatter key "date_added"
  • note frontmatter-key unknown frontmatter key "license_source"
  • note frontmatter-key unknown frontmatter key "sources"
  • note frontmatter-key unknown frontmatter key "report_count"
  • note edit-residue the text marks something as outdated (lines 38, 246, 366, 367): check that old rules are not kept next to new ones — the full check reads the text for contradictions

Process rating: all ten parameters 51/100

  • 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
  • 30When it triggers. No condition that starts the skill
  • 30Running it twice. 8 mutating operations with no state check
  • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 70Execution cost. Instruction body is 5851 tokens
  • 100Steps. 85 steps
  • 100Consistency. Name and required fields are in place
  • low 14 top-level sections: this looks like several domains in one skill

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)
  • +3Description length 39: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +2Single-language instructions
  • +4Structure: 15 headings
  • +3Step-by-step instructions: 85 items
  • +4Has examples (19 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 61.