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apify-actorization

Actorization converts existing software into reusable serverless applications compatible with the Apify platform. Actors are programs packaged as Docker images that accept well-defined JSON input, perform an action, and optionally produce structured JSON output.

Quality

74%

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SecuritybySnyk

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tessl review fix ./skills/apify-actorization/SKILL.md

The canonical home for this skill is apify-actorization in sickn33/agentic-awesome-skills

SKILL.md
Quality
Evals
Security

Quality

Content

75%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is a well-structured, actionable overview: a clear 8-step workflow with exact CLI commands, validation checkpoints, and appropriate delegation of language-specific detail to real reference files. Main improvements: remove duplication (repeated description, checklist restating step headers), add error-recovery guidance for failed local runs, and fix the dangling cross-bundle reference in js-ts-actorization.md.

DimensionReasoningScore

Conciseness

Largely efficient: commands are given bare with no library tutorials, and detail is delegated to reference files. Minor trimmable instances: the intro paragraph repeats the frontmatter description verbatim, the "Actorization Checklist" restates the Step 1-8 headers, and the Monetization section is peripheral to the core conversion workflow. Not noticeably verbose enough for 3, and clearly not the lean 5.

4 / 5

Actionability

Mostly executable guidance: exact commands for the whole flow (`apify init`, `apify run --input '{"key": "value"}'`, `apify push`, `apify login`, `apify info`), a quick-reference table with the actual SDK calls (`await Actor.init()` ... `await Actor.exit()`, `async with Actor:`), and validation against `@apify/json_schemas`. Minor gaps: no full wrap-code snippet in the body (deferred to references, which is acceptable) and `await Actor.charge('result')` appears without surrounding context.

4 / 5

Workflow Clarity

Eight explicitly sequenced steps with a progress checklist, a local-test checkpoint ("Always use `apify run`, not `npm start`"), and a thorough pre-deployment validation checklist covering schemas, Dockerfile build, and successful test run. Falls short of 5 because there is no error-recovery guidance (what to do when `apify run` fails or schema validation errors appear), and beyond 3 because validation checkpoints are clearly present rather than implicit.

4 / 5

Progressive Disclosure

Good structure: an overview-length SKILL.md (~186 lines) with well-signaled links to four real one-level-deep reference files (js-ts, python, cli, schemas-and-output), each 81-140 lines. Not a 5 because `references/js-ts-actorization.md:93` links outside the bundle to `../../apify-actor-development/references/standby-mode.md`, a second-level cross-skill reference whose target is missing in this skill's directory, which breaks the one-level-deep property and navigation.

4 / 5

Total

16

/

20

Passed

Description

62%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description clearly and specifically states what the skill does with concrete mechanisms (Docker images, JSON input/output), and occupies a distinct niche. Its main weakness is the complete absence of any 'when to use' trigger guidance and of natural user phrasings that would help a user (or Claude) select this skill at the right moment.

Suggestions

Add an explicit trigger clause, e.g. "Use when converting a project to run on Apify, deploying to the Apify platform, or when the user mentions Apify, actors, or apify push."

Include natural user phrasings and synonyms such as "deploy to Apify", "Apify actor", "wrap a script as an actor", and "apify push" so trigger matching works against what users actually say.

Consider mentioning the supported language paths (JavaScript/TypeScript, Python, CLI-based tools) since language is the first routing decision in this skill.

DimensionReasoningScore

Specificity

Names several concrete specifics: "converts existing software into reusable serverless applications", "programs packaged as Docker images", "accept well-defined JSON input", "optionally produce structured JSON output". Not a comprehensive list of multiple discrete actions like the 5 anchor, but well above the 1-2 action level of 3.

4 / 5

Completeness

Has a clear 'what' (converts software into Docker-packaged Apify actors with JSON input/output) but no 'Use when...' clause or equivalent explicit trigger guidance; the 'when' is only weakly implied by "existing software". Per judging guidelines, a missing 'Use when...' clause caps completeness at 3.

3 / 5

Trigger Term Quality

Contains relevant domain keywords ("Actorization", "Apify platform", "Actors", "Docker images", "JSON") but misses natural phrases users would actually say such as "deploy to Apify", "Apify actor", "wrap as an actor", or "apify push". Coverage is real but lacks common variations and synonyms, matching the 'some relevant keywords but missing variations' anchor.

3 / 5

Distinctiveness Conflict Risk

"Apify platform", "Actorization", and "Actors" are highly distinctive niche terms; virtually no other skill would trigger on this description, and there is minimal overlap risk with generic containerization or deployment skills.

5 / 5

Total

15

/

20

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository

administrakt0r/AI-Agents-Safe-Coding-Skills

Path

skills/apify-actorization/SKILL.md

Reviewed

Table of Contents

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