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AC apify-actorization

Convert existing projects into Apify Actors - serverless cloud programs. Actorize JavaScript/TypeScript (SDK with Actor.init/exit), Python (async context manager), or any language (CLI wrapper). Use when migrating code to Apify, wrapping CLI tools as Actors, or adding Actor SDK to existing projects.

apify/agent-skills Agent Skills author: apify 5 files body ≈ 2 951 tokens Open the sourcegithub.com↗ analyzed 6 d ago

Convert existing projects into Apify Actors - serverless cloud programs.

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

GeneratorDockerPlaywrightSoftware developmentAI and agentstype 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
95/100
safety, quality, tests
Safety 60%
99
Quality 40%
88
Run on models
none yet
Process rating
C
59/100
Has gaps
Result and completion w 14
0
When it triggers w 12
20
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 SKILL.md:44
      Downloads and executes remote code from an unrecognised host (pipe to shell) (security demo / example; quoted — discussed, not commanded)
      > (e.g. `curl ... | bash` or `irm ... | iex`). Always use a package manager.
      demoquoted

    Files scanned: 5. 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 59/100

    • 0Result and completion. Does not say what the result is
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 18 mutating operations with no state check
    • 60Tools and files. Uses tools (bash, web, python, node) that frontmatter does not declare
    • 60Failures and branches. 2 branches
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 63 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2951 tokens
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 16 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)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 300: enough signal without eating the budget
    • +4Structure: 21 headings
    • +3Step-by-step instructions: 63 items
    • +4Has examples (11 code blocks)
    • +4Reference files are cited in the instructions (4 of 4)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.