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AD telegram-sticker-vibes

Automatically send Telegram stickers and emojis that match the mood and vibe of the conversation. Activates on every Telegram message to add expressive, human-like sticker and emoji reactions without being asked. Use this skill whenever you are chatting on Telegram to make conversations feel alive and fun.

modbender/skill-library-mcp Agent Skills author: modbender MIT 2 files · 1 script body ≈ 1 579 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Automatically send Telegram stickers and emojis that match the mood and vibe of the conversation.

As a process D 48/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, consistency

ProcedureTelegramCommercetype 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
93/100
safety, quality, tests
Safety 60%
99
Quality 40%
85
Run on models
none yet
Process rating
D
48/100
Unfinished process
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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Exfiltration exfil-webhook-url scripts/send_sticker.sh:18
      Webhook / callback URL commonly used for exfiltration (verify the destination) (placeholder value)
      API="https://api.telegram.org/bot${TELEGRAM_BOT_TOKEN}"
      placeholder

    Files scanned: 2. 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 48/100

    • 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
    • 30Running it twice. 28 mutating operations with no state check
    • 40Consistency. Frontmatter name (telegram-sticker-vibes) differs from the folder (tg-sticker-emoji-mood)
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 60Failures and branches. 2 branches
    • 70When it triggers. States when to use, but not when not to
    • 85Steps. 32 steps, 1 vague phrases
    • 100Execution cost. Instruction body is 1579 tokens

    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
    • -235 emoji in the instructions: noise for the model
    • +1No license
    • +2Single-language instructions
    • +3Description length 307: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 32 items
    • +4Has examples (3 code blocks)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.