SKILLEMALL.ai

AC hunt-dispatch

Skill-set loader for /hunt orchestrator. Fingerprints the target, picks the right platform attack skills, and loads the Red Team or WAPT skill set. Use when /hunt has just received a mode answer (redteam or wapt + blackbox|greybox) and needs to load the appropriate skills and print the taxonomy. Not for direct user invocation.

elementalsouls/Claude-BugHunter Agent Skills author: elementalsouls 1 file body ≈ 4 287 tokens Open the sourcegithub.com analyzed 3 h ago

Skill-set loader for /hunt orchestrator.

As a process C 52/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

ProcedureKubernetesSoftware developmentInfrastructureData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
91/100
safety, quality, tests
Safety 60%
94
Quality 40%
86
Run on models
none yet
Process rating
C
52/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Running it twice w 4
30
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 · 6

    ✓ No critical or high findings

    Medium and low: 6
    • low Risky intent intent-offensive-security SKILL.md:3
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (detector / deny-list definition)
      description: Skill-set loader for /hunt orchestrator. Fingerprints the target, picks the right platform attack skills, and loads the Red Team or WAPT skill set. Use when /hunt has just received a mode
      detector
    • low Risky intent intent-offensive-security SKILL.md:72
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      ## step 1 — fingerprint (red team only)
    • low Risky intent intent-offensive-security SKILL.md:151
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      (credential → lateral movement)
    • low Exfiltration net-credential-use SKILL.md:269
      Credential used in a network call (verify the destination is the intended service) (documentation of a security skill)
      curl -sS -m 12 -H "Authorization: Bearer $TOKEN" \
      security skill
    • low Risky intent intent-offensive-security SKILL.md:299
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      loaded for red team: {N} skills
    • low Risky intent intent-offensive-security SKILL.md:372
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      - **`bb-methodology`** — When PART 0 mode confirmation completes. Workflow primitive: `bb-methodology` confirms engagement type (red team vs WAPT vs bug bounty); the answer feeds directly into this sk

    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 52/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 10 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
    • 70Execution cost. Instruction body is 4287 tokens
    • 100Steps. 21 steps
    • 100Consistency. Name and required fields are in place
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 328: enough signal without eating the budget
    • +4Structure: 15 headings
    • +3Step-by-step instructions: 21 items
    • +4Has examples (13 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.