SKILLEMALL.ai
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BC telegram

Full personal Telegram control over MTProto (Telethon) with the user's own account — list/search chats, read & summarize history, see unread, look up contacts & chat info, list a forum group's topics, download media, and send / reply / forward / edit / delete / react / send files / mark read / join & leave groups, including posting into a forum topic thread. Use when the user mentions Telegram, a Telegram chat/group/contact, "我的 Telegram", reading/replying/forwarding/summarizing Telegram messages, their unread Telegram, joining or leaving a Telegram group/channel, posting into a forum group's topic, or sending a file/message on Telegram.

AceDataCloud/Skills Agent Skills author: AceDataCloud 2 files · 1 script body ≈ 1 934 tokens Open the sourcegithub.com↗ analyzed 2 h ago

Full personal Telegram control over MTProto (Telethon) with the user's own account — list/search chats, read & summarize history, see unread, look up contacts…

As a process C 56/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

GeneratorTelegramWriting and documentstype 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
B
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
86
Run on models
none yet
Process rating
C
56/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Running it twice w 4
30
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: 2. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "connections"
    • note frontmatter-key unknown frontmatter key "allowed_tools"

    Process rating: all ten parameters 56/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 18 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 12 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1934 tokens
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low The response is described with custom markup (12 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
    • +2Single-language instructions
    • +3Description length 645: enough signal without eating the budget
    • +4Structure: 7 headings
    • +3Step-by-step instructions: 12 items
    • +4Has examples (5 code blocks)
    • +3All 1 scripts are documented
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.