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AC minijuegos-lechuck

Integrate Minijuegos.com (Miniplay) LeChuck JS SDK into HTML5 games. Use when a user wants to: (1) add Minijuegos/Miniplay platform integration to a game, (2) authenticate players via the LeChuck SDK, (3) implement achievements, stats, or leaderboards for Minijuegos.com, (4) troubleshoot LeChuck SDK issues (missing user ID, params lost on redirect, data not sent on game over), (5) register Minijuegos users in a custom backend. Covers full auth flow, nginx config, backend endpoint, achievements, stats, leaderboard, and the critical death/game-over data flush fix. Required env vars: MINIPLAY_API_ID (public numeric ID from Minijuegos dev panel), MINIPLAY_API_KEY (server-side secret for write operations — never expose in client code), JWT_SECRET (for signing session tokens).

ClawHub Agent Skills author: Alfredo Bárcena v1.0.5 MIT-0 4 files body ≈ 1 262 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process C 61/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationSoftware developmentInfrastructuretype and topics are labelled automatically from the skill text
Runs in: OpenClaw 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) Goose OpenCode
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
88
Run on models
none yet
Process rating
C
61/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
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 · 0

    ✓ No critical or high findings

    Files scanned: 4. 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 61/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 5 mutating operations with no state check
    • 100Tools and files. No external tools needed
    • 100Steps. 7 steps
    • 100Failures and branches. 3 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1262 tokens
    • low 11 top-level sections: this looks like several domains in one skill

    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
    • +1No license
    • +2Single-language instructions
    • +3Description length 781: enough signal without eating the budget
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 7 items
    • +4Has examples (10 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.

    External checks

    ClawHub: clean
    This is a plain guidance-only data pipeline skill with no executable code, credential handling, or hidden runtime behavior.
    LLM: benign (high) · VirusTotal: · 29 May 2026