SKILLEMALL.ai
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AC instrument-agent

Set up Raindrop AI traces for an agent and verify they flow into Workshop. Use when the user says "set up traces", "add tracing", "instrument my agent", "wire Raindrop", "Workshop is empty", or "make traces show up in Workshop." Guides unknown repos by discovering runtime and telemetry setup, using current Raindrop docs/package types, making the smallest safe change, and proving one useful Workshop run. Not for repos that already have working Raindrop instrumentation — use for initial setup or when Workshop shows no data.

raindrop-ai/workshop Agent Skills author: raindrop-ai 1 file body ≈ 4 536 tokens Open the sourcegithub.com↗ analyzed 6 d ago

Set up Raindrop AI traces for an agent and verify they flow into Workshop.

As a process C 64/100 · Has gaps — weak spots: result and completion, inputs and preconditions

AnalyzerAI and agentsSoftware developmenttype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw Goose OpenCode
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
91
Run on models
none yet
Process rating
C
64/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
30
Tools and files w 18
60
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 · 0

    ✓ No critical or high findings

    Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 64/100

    • 0Result and completion. Does not say what the result is
    • 30Inputs and preconditions. Does not say what the process needs to start
    • 60Tools and files. Uses tools (bash, web, python, node) that frontmatter does not declare
    • 65Failures and branches. 3 branches
    • 70Execution cost. Instruction body is 4536 tokens
    • 85Steps. 89 steps, 1 vague phrases
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 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
    • low The response is described with custom markup (4 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

    • +3Output format is not stated: the model decides each time
    • -2localhost URLs: will not work for another user
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 6 example trigger phrases
    • +4Description says when NOT to use the skill
    • +3Description length 527: enough signal without eating the budget
    • +4Structure: 17 headings
    • +3Step-by-step instructions: 89 items
    • +4Has examples (2 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.