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AC domain-recon

Passive domain/infra OSINT over five keyless public APIs — subdomains, RDAP/WHOIS, DNS-over-HTTPS, IP geo/ISP, and ASN/prefix ownership.

ClawHub Agent Skills author: maggiedev-bot v0.6.0 MIT-0 35 files body ≈ 2 825 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

Passive domain/infra OSINT over five keyless public APIs — subdomains, RDAP/WHOIS, DNS-over-HTTPS, IP geo/ISP, and ASN/prefix ownership.

As a process C 54/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

ProcedureCloudflareData and analyticstype and topics are labelled automatically from the skill text
Runs in: OpenClaw 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) Goose OpenCode
JSON
Technical rating
A
91/100
safety, quality, tests
Safety 60%
99
Quality 40%
80
Run on models
none yet
Process rating
C
54/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Secrets in code secret-high-entropy-token scripts/tests/test_recon.py:1493
    High-entropy token-like string (may be an id, hash or a credential) (test fixture / example file)
    def test…ken(self):
    fixture

Files scanned: 35. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 54/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 2 mutating operations with no state check
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
  • 70When it triggers. States when to use, but not when not to
  • 100Steps. 27 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2825 tokens
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low The response is described with custom markup (9 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
  • +2Single-language instructions
  • +3Description length 136: enough signal without eating the budget
  • +4Structure: 6 headings
  • +3Step-by-step instructions: 27 items
  • +4Has examples (4 code blocks)
  • +4Reference files are cited in the instructions (3 of 6)
  • +3All 2 scripts are documented
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.

External checks

ClawHub: clean
This is a coherent passive reconnaissance skill, with disclosed third-party lookups and no evidence of credential access, persistence, or destructive behavior.
LLM: benign (high) · VirusTotal: · 28 Jul 2026