SKILLEMALL.ai

BC AgentVibes Clawdbot Skill - local-gen-tts Integration

Automatically integrates AgentVibes with Clawdbot for local TTS generation on remote devices (Android/Termux, Linux, macOS) via SSH.

modbender/skill-library-mcp Agent Skills author: modbender MIT 3 files · 1 script body ≈ 2 267 tokens Open the sourcegithub.com analyzed 2 d ago

Automatically integrates AgentVibes with Clawdbot for local TTS generation on remote devices (Android/Termux, Linux, macOS) via SSH.

As a process C 59/100 · Has gaps — weak spots: result and completion, when it triggers, consistency

IntegrationGitHubAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
85/100
safety, quality, tests
Safety 60%
98
Quality 40%
65
Run on models
none yet
Process rating
C
59/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
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 Dangerous commands cmd-pipe-to-shell-known-host setup.sh:304
    Pipe-to-shell installer from a well-known host (still executes remote code) (string literal in code, not executed)
    echo "   curl -sSL https://raw.githubusercontent.com/paulpreibisch/AgentVibes/main/scripts/install-ssh-receiver.sh | bash"
    code literal
  • low Dangerous commands cmd-pipe-to-shell-known-host SKILL.md:321
    Pipe-to-shell installer from a well-known host (still executes remote code) (quoted — discussed, not commanded)
    ssh android "curl -sSL https://raw.githubusercontent.com/paulpreibisch/AgentVibes/main/scripts/install-ssh-receiver.sh | bash"
    quoted

Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 59/100

  • 0Result and completion. Does not say what the result is
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 2 mutating operations with no state check
  • 40Consistency. Frontmatter name (AgentVibes Clawdbot Skill - local-gen-tts Integration) differs from the folder (agentvibesclawdbot)
  • 50Failures and branches. 0 branches, has a failure section
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Tools and files. No external tools needed
  • 100Steps. 44 steps
  • 100Execution cost. Instruction body is 2267 tokens
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 17 top-level sections: this looks like several domains in one skill
  • 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
  • -217 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 132: enough signal without eating the budget
  • +4Structure: 36 headings
  • +3Step-by-step instructions: 44 items
  • +4Has examples (21 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 65.