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AC telegram-stickers

Create Telegram stickers from images — static PNG stickers or animated WebM video stickers. Use when the user wants to make, process, or package Telegram stickers from photos or NFT art. Handles background removal, resizing to Telegram spec (512x512), animation (sway, bounce, shake), and upload. Knows exact @Stickers bot workflow for both static and video sticker packs.

ClawHub Agent Skills author: saintsola13 v1.0.0 MIT-0 9 files body ≈ 891 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process C 60/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

GeneratorTelegramMedia and videoInfrastructuretype 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%
99
Quality 40%
91
Run on models
none yet
Process rating
C
60/100
Has gaps
Result and completion w 14
0
Failures and branches w 10
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 Secrets in code secret-high-entropy-token scripts/make_webm.py:87
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      boundary = "----…0gW"
      quoted

    Files scanned: 9. 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 60/100

    • 0Result and completion. Does not say what the result is
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 30Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 1 mutating operations with no state check
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. No external tools needed
    • 100Steps. 7 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 891 tokens
    • 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

    • +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 372: enough signal without eating the budget
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 7 items
    • +4Has examples (6 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)
    • +3All 6 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.

    External checks

    ClawHub: clean
    This skill is a disclosed Telegram sticker-making tool, but animated sticker creation uploads the generated WebM to tmpfiles.org by default unless the user disables it.
    LLM: benign (high) · VirusTotal: benign · 28 May 2026