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BC Pocket AI Skill

Pocket AI captures your meetings, calls, and thoughts via a wearable device, then transcribes and indexes everything for semantic search.

modbender/skill-library-mcp Agent Skills author: modbender MIT 6 files · 1 script body ≈ 1 645 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Pocket AI captures your meetings, calls, and thoughts via a wearable device, then transcribes and indexes everything for semantic search.

As a process C 57/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, consistency

IntegrationSoftware developmentAI and agentstype 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
83/100
safety, quality, tests
Safety 60%
95
Quality 40%
65
Run on models
none yet
Process rating
C
57/100
Has gaps
Inputs and preconditions w 11
0
When it triggers w 12
20
Consistency w 8
40
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 · 5

✓ No critical or high findings

Medium and low: 5
  • low Exfiltration net-credential-use examples.md:8
    Credential used in a network call (verify the destination is the intended service) (test fixture / example file)
    curl -s -X POST -H "Authorization: Bearer $API_KEY" -H "Content-Type: application/json" \
    fixture
  • low Exfiltration net-credential-use examples.md:16
    Credential used in a network call (verify the destination is the intended service) (test fixture / example file)
    curl -s -X POST -H "Authorization: Bearer $API_KEY" -H "Content-Type: application/json" \
    fixture
  • low Exfiltration net-credential-use examples.md:23
    Credential used in a network call (verify the destination is the intended service) (test fixture / example file)
    curl -s -X POST -H "Authorization: Bearer $API_KEY" -H "Content-Type: application/json" \
    fixture
  • low Exfiltration net-credential-use examples.md:31
    Credential used in a network call (verify the destination is the intended service) (test fixture / example file)
    curl -s -X POST -H "Authorization: Bearer $API_KEY" -H "Content-Type: application/json" \
    fixture
  • low Exfiltration net-credential-use examples.md:38
    Credential used in a network call (verify the destination is the intended service) (test fixture / example file)
    curl -s -X POST -H "Authorization: Bearer $API_KEY" -H "Content-Type: application/json" \
    fixture

Files scanned: 6. 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 57/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 20When it triggers. No condition that starts the skill
  • 40Consistency. Frontmatter name (Pocket AI Skill) differs from the folder (pocket-ai)
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 100Steps. 27 steps
  • 100Execution cost. Instruction body is 1645 tokens
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 10 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)
  • -5TODO / placeholder text left in the skill
  • +1No license
  • +2Single-language instructions
  • +3Description length 137: enough signal without eating the budget
  • +4Structure: 30 headings
  • +3Step-by-step instructions: 27 items
  • +3Output format is stated explicitly
  • +4Has examples (14 code blocks)

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