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CD hunt-k8s

Hunt Kubernetes & Docker

sickn33/agentic-awesome-skills Agent Skills author: sickn33 MIT 1 file body ≈ 6 046 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Hunt Kubernetes & Docker

As a process D 42/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureDockerKubernetesAWSSecuritytype 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
79/100
safety, quality, tests
Safety 60%
93
Quality 40%
59
Run on models
none yet
Process rating
D
42/100
Unfinished process
Result and completion w 14
0
When it triggers w 12
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

The same skill appears in 2 more places: agentic-awesome-skills, 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 · 7

✓ No critical or high findings

Medium and low: 7
  • low Secrets in code secret-high-entropy-token SKILL.md:76
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    TOK=$(curl -s -X PUT "http://169.254.169.254/latest/api/token" -H "X-aw…ds: 60") # IMDSv2
    quoted
  • low Exfiltration net-credential-use SKILL.md:109
    Credential used in a network call (verify the destination is the intended service) (documentation of a security skill)
    curl -sS --cacert "$K8S_CA" "$SRV/api/v1/namespaces/$NS/secrets/$TEST_SECRET" | python3 -c 'import sys,json;d=json.load(sys.stdin);print(len(d.get("items",[])),"secrets")'
    security skill
  • low Exfiltration net-credential-use SKILL.md:163
    Credential used in a network call (verify the destination is the intended service) (documentation of a security skill)
    NODE=$(curl -sS --cacert "$K8S_CA" -H "Authorization: Bearer $TOKEN" "$SRV/api/v1/nodes" | grep -o '"name":"[^"]*"' | head -1 | cut -d'"' -f4)
    security skill
  • low Exfiltration net-credential-use SKILL.md:166
    Credential used in a network call (verify the destination is the intended service) (documentation of a security skill)
    curl -sS --cacert "$K8S_CA" -X POST -H "Authorization: Bearer $TOKEN" \
    security skill
  • low Exfiltration net-credential-use SKILL.md:170
    Credential used in a network call (verify the destination is the intended service) (documentation of a security skill)
    curl -sS --cacert "$K8S_CA" -H "Authorization: Bearer $TOKEN" "$SRV/api/v1/nodes/$NODE/proxy/pods"
    security skill
  • low Exfiltration net-credential-use SKILL.md:217
    Credential used in a network call (verify the destination is the intended service) (documentation of a security skill)
    curl -sS --cacert "$K8S_CA" "$API/api/v1/namespaces/$NS/secrets" -H "Authorization: Bearer $TOKEN"
    security skill
  • low Risky intent intent-offensive-security SKILL.md:282
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    | Kubelet 10250 `/run` | exec in any pod → steal SA token → API | Cluster privilege escalation |

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 ≈ 6046 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"

Process rating: all ten parameters 42/100

  • 0Result and completion. Does not say what the result is
  • 0When it triggers. No condition that starts the skill
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 6 mutating operations with no state check
  • 55Failures and branches. 1 branches
  • 60Tools and files. Uses tools (bash, web, node) that frontmatter does not declare
  • 70Execution cost. Instruction body is 6046 tokens
  • 85Steps. 38 steps, 1 vague phrases
  • 100Consistency. Name and required fields are in place
  • low 17 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (6 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)
  • +3Description length 24: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • -2localhost URLs: will not work for another user
  • +2Single-language instructions
  • +4Structure: 19 headings
  • +3Step-by-step instructions: 38 items
  • +4Has examples (10 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 59.