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AC jentic

Call external APIs through Jentic — AI agent API middleware. Use whenever you need to interact with external APIs (Gmail, Google Calendar, GitHub, Stripe, Twilio, and many more). Jentic handles authentication centrally so no per-API credentials are needed in the agent. The flow is: search by intent, load the schema, then execute. Use this in preference to direct curl/API calls for any API in the Jentic catalog.

modbender/skill-library-mcp Agent Skills author: modbender MIT 5 files body ≈ 1 102 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Call external APIs through Jentic — AI agent API middleware.

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

IntegrationGitHubGmailStripeAI 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
A
92/100
safety, quality, tests
Safety 60%
99
Quality 40%
82
Run on models
none yet
Process rating
C
56/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Dangerous commands cmd-pipe-to-shell-known-host SKILL.md:37
      Pipe-to-shell installer from a well-known host (still executes remote code) (quoted — discussed, not commanded)
      Requires `uv` (`curl -LsSf https://astral.sh/uv/install.sh | sh`). The script self-installs its dependencies on first run.
      quoted

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

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "homepage"

    Process rating: all ten parameters 56/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 4 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
    • 60Steps. 15 steps, 4 vague phrases
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1102 tokens
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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
    • -42 reference files, but SKILL.md never points to them: the model will not open them
    • +1No license
    • +2Single-language instructions
    • +3Description length 414: enough signal without eating the budget
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 15 items
    • +4Has examples (2 code blocks)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.