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AC apify-actor-development

Create, modify, debug, and deploy Apify Actors, and write their input and output schemas. Use when building an Actor from scratch, changing or troubleshooting Actor code, generating or updating .actor schema files, or pushing an Actor to the Apify platform. To wrap an existing non-Actor project, use apify-actorization instead.

apify/agent-skills Agent Skills author: apify 7 files body ≈ 2 817 tokens Open the sourcegithub.com↗ analyzed 6 d ago

Create, modify, debug, and deploy Apify Actors, and write their input and output schemas.

As a process C 62/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

GeneratorPlaywrightSoftware developmentAI and agentsInfrastructuretype 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%
100
Quality 40%
88
Run on models
none yet
Process rating
C
62/100
Has gaps
Inputs and preconditions w 11
0
Running it twice w 4
30
Result and completion w 14
40
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: 7. 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 62/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 22 mutating operations with no state check
    • 40Result and completion. Does not say what the result is
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash, web, git, python, node) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 28 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2817 tokens
    • 100Progress reporting. Reports progress

    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 328: enough signal without eating the budget
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 28 items
    • +4Has examples (4 code blocks)
    • +4Reference files are cited in the instructions (6 of 6)

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