SKILLEMALL.ai
RU Sign in

BC tms

Manage downloads via Telegram Media Server (TMS) REST API — add by URL (video/magnet/torrent), list, delete, search torrents.

modbender/skill-library-mcp Agent Skills author: modbender MIT 5 files body ≈ 2 780 tokens Open the sourcegithub.com↗ analyzed 34 h ago

Manage downloads via Telegram Media Server (TMS) REST API — add by URL (video/magnet/torrent), list, delete, search torrents.

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

IntegrationTelegramWriting and documentsMedia 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
B
87/100
safety, quality, tests
Safety 60%
99
Quality 40%
70
Run on models
none yet
Process rating
C
56/100
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

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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 net-credential-use README.md:102
    Credential used in a network call (verify the destination is the intended service) (quoted — discussed, not commanded)
    - **"API unreachable" / connection errors** — Check `TMS_API_URL` (no trailing slash), firewall, and that TMS is running with API enabled. Test: `curl "$TMS_API_URL/api/v1/health"` (from same host) or
    quoted

Files scanned: 5. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

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
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 7 mutating operations with no state check
  • 60Tools and files. Uses tools (web) that frontmatter does not declare
  • 65Failures and branches. 3 branches
  • 70When it triggers. States when to use, but not when not to
  • 100Steps. 7 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2780 tokens
  • low The response is described with custom markup (8 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
  • -2localhost URLs: will not work for another user
  • +1No license
  • +2Single-language instructions
  • +3Description length 125: enough signal without eating the budget
  • +4Structure: 5 headings
  • +3Step-by-step instructions: 7 items
  • +4Has examples (1 code blocks)

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