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CF maps-geography

Accurate maps from real geo data — use for any map, or whenever geography would make a good graphic for a deliverable

asgeirtj/system_prompts_leaks Agent Skills author: asgeirtj CC0-1.0 1 file body ≈ 671 tokens Open the sourcegithub.com↗ analyzed 6 d ago

Accurate maps from real geo data — use for any map, or whenever geography would make a good graphic for a deliverable

As a process F 34/100 · No process to follow — weak spots: steps, result and completion, inputs and preconditions

GeneratorInfrastructuretype 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
87/100
safety, quality, tests
Safety 60%
98
Quality 40%
70
Run on models
none yet
Process rating
F
34/100
No process to follow
Steps w 15
0
Result and completion w 14
0
Inputs and preconditions w 11
0
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 · 2

    ✓ No critical or high findings

    Medium and low: 2
    • low Secrets in code secret-high-entropy-token SKILL.md:16
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      <script src="https://unpkg.com/to…@….1.0/dist/topojson-client.min.js" integrity="sha3…v1p/xTma6P4/2bY5KzWBw+ydSp…H67" crossorigin="anonymous"></script>
      quoted
    • low Secrets in code secret-high-entropy-token SKILL.md:23
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      <script src="https://unpkg.com/le…@….9.4/dist/leaflet.js" integrity="sha3…BmS+qpjv…OrM+YHwZ…HTH" crossorigin="anonymous"></script>
      quoted

    Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 34/100

    • 0Steps. Prose only: no discrete steps
    • 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
    • 30Running it twice. 1 mutating operations with no state check
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 671 tokens
    • 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 117: 120–800 characters recommended
    • +4Structure: 1 headings, hard to scan
    • +3No numbered steps or checklist
    • +3Output format is not stated: the model decides each time
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions

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