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AC igot-karmayogi

Automates iGOT Karmayogi portal (portal.igotkarmayogi.gov.in) using OpenClaw's built-in Playwright managed browser. Use this skill whenever the user mentions iGOT, Karmayogi, government courses, civil servant training, Mission Karmayogi, or wants to: play course videos, enroll in courses, complete assessments, download certificates, or track learning progress. Trigger for phrases like "do my iGOT courses", "complete karmayogi", "play the videos", "get my certificate from igot", "finish my assigned courses", "continue my paused course". The skill launches its own browser, runs fully autonomously, and only contacts the user for login and genuine errors after 3 retries.

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 4 files body ≈ 3 781 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Automates iGOT Karmayogi portal (portal.igotkarmayogi.gov.in) using OpenClaw's built-in Playwright managed browser. Use this skill whenever the user mentions…

As a process C 57/100 · Has gaps — weak spots: result and completion, inputs and preconditions, progress reporting

ProcedurePlaywrightLearningMedia and videotype 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
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
90
Run on models
none yet
Process rating
C
57/100
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 57/100

    • 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
    • 50Steps. 2 steps
    • 50Failures and branches. 0 branches, has a failure section
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. No external tools needed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3781 tokens
    • 100Running it twice. No mutating operations
    • low 18 top-level sections: this looks like several domains in one skill
    • low The response is described with custom markup (3 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

    • +4Description does not say when NOT to use the skill (false activations)
    • +3No numbered steps or checklist
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 6 example trigger phrases
    • +3Description length 675: enough signal without eating the budget
    • +4Structure: 19 headings
    • +4Has examples (14 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 90.