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CD 3d-object

three.js model, downloadable as OBJ or GLB

asgeirtj/system_prompts_leaks Agent Skills author: asgeirtj CC0-1.0 1 file body ≈ 1 046 tokens Open the sourcegithub.com↗ analyzed 6 d ago

three.js model, downloadable as OBJ or GLB

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureInfrastructureWriting and documentstype 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
D
46/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
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 · 2

✓ No critical or high findings

Medium and low: 2
  • low Secrets in code secret-high-entropy-token SKILL.md:40
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "https://unpkg.com/th…@….184.0/examples/jsm/controls/OrbitControls.js": "sha3…69i+w+f89K…JC9",
    detector
  • low Secrets in code secret-high-entropy-token SKILL.md:41
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "https://unpkg.com/th…@….184.0/examples/jsm/exporters/OBJExporter.js": "sha3…3Vq+ACK0…YJN+N3",
    quoted

Files scanned: 1. 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 46/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 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
  • 60Tools and files. Uses tools (node) that frontmatter does not declare
  • 100Steps. 5 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1046 tokens
  • 100Running it twice. No mutating operations
  • low The response is described with custom markup (5 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 42: 120–800 characters recommended
  • +4Structure: 1 headings, hard to scan
  • +3Output format is not stated: the model decides each time
  • +4No input/output examples
  • +1No license
  • +2Single-language instructions
  • +3Step-by-step instructions: 5 items

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