SKILLEMALL.ai

CD building-ioc-defanging-and-sharing-pipeline

Build an automated pipeline that ingests raw IOCs (URLs, IPs, domains, emails), normalizes and deduplicates them, then produces defanged renderings for safe human reading alongside canonical STIX 2.1 bundles distributed via TAXII servers, MISP, or email reports. Use when preparing indicators of compromise for safe analyst sharing or automating threat intel distribution to TAXII/MISP feeds.

mukul975/Anthropic-Cybersecurity-Skills Agent Skills author: mukul975 Apache-2.0 4 files · 1 script body ≈ 3 440 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Build an automated pipeline that ingests raw IOCs (URLs, IPs, domains, emails), normalizes and deduplicates them, then produces defanged renderings for safe…

As a process D 40/100 · Unfinished process — References files that are not bundled: ?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9]

ProcedurePeople and hiringtype and topics are labelled automatically from the skill text
JSON
Technical rating
C
87/100
safety, quality, tests
Safety 60%
99
Quality 40%
69
Run on models
none yet
Process rating
D
40/100
Unfinished process
References files that are not bundled: ?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9]
Tools and files w 18
0
Result and completion w 14
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

  1. The text references files that are not there: add them or drop the references.
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 Risky intent intent-offensive-security SKILL.md:326
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (detector / deny-list definition)
    "category": "Network activity" if ioc_type in ("ipv4", "domain", "url") else "Payload delivery",
    detector

Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: ?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9]
  • 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 40/100

Will not run. References files that are not bundled: ?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9]
  • 0Tools and files. 1 referenced file(s) missing: ?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9]
  • 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
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 1 mutating operations with no state check
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Steps. 21 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 3440 tokens

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
  • -41 reference files, but SKILL.md never points to them: the model will not open them
  • -31 of 1 scripts are never mentioned in SKILL.md
  • +2Single-language instructions
  • +3Description length 392: enough signal without eating the budget
  • +4Structure: 15 headings
  • +3Step-by-step instructions: 21 items
  • +4Has examples (4 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.