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Telegram 频道/群组舆情采集工具 v3.12。支持四种模式：hybrid（发现+回溯）、discover（仅频道发现）、backfill（仅消息回溯）、monitor（实时监控）。内置数据保留策略，自动清理过期数据，防止磁盘膨胀。

ClawHub Agent Skills author: yingle v3.12.0 MIT-0 24 files body ≈ 1 922 tokens Open the sourceclawhub.ai↗ analyzed 14 h ago

Telegram 频道/群组舆情采集工具 v3.12。支持四种模式：hybrid（发现+回溯）、discover（仅频道发现）、backfill（仅消息回溯）、monitor（实时监控）。内置数据保留策略，自动清理过期数据，防止磁盘膨胀。

As a process C 58/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureTelegramSoftware developmentData and analyticstype 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
86/100
safety, quality, tests
Safety 60%
98
Quality 40%
69
Run on models
none yet
Process rating
C
58/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
20
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 · 2

✓ No critical or high findings

Medium and low: 2
  • low Exfiltration read-dotenv README.md:38
    Reads a .env file
    cp .env.example .env
  • low Dangerous commands cmd-background-process references/proxy-pool-setup.md:95
    Starts a background / autostarted process
    systemctl enable danted

Files scanned: 24. 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 58/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 7 mutating operations with no state check
  • 50Failures and branches. 0 branches, has a failure section
  • 100Tools and files. Tools declared in frontmatter
  • 100Steps. 26 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1922 tokens
  • 100Progress reporting. Reports progress
  • low 11 top-level sections: this looks like several domains in one skill

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)
  • +3Description length 119: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • -224 emoji in the instructions: noise for the model
  • -37 of 8 scripts are never mentioned in SKILL.md
  • +2Single-language instructions
  • +4Structure: 14 headings
  • +3Step-by-step instructions: 26 items
  • +4Has examples (3 code blocks)
  • +4Reference files are cited in the instructions (4 of 4)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.

External checks

ClawHub: suspicious
This is a powerful Telegram collection skill with real monitoring uses, but its default targets, proxy guidance, bot discovery, and data handling are too broad and under-scoped for automatic trust.
LLM: suspicious (high) · VirusTotal: · 5 Jun 2026