AAS Core is a local control plane for coding agents that lets them search a large catalogue of skills, choose a stack, validate it, and create a reproducible plan. It is used to assemble and review agent workflows through its CLI, local MCP server, catalogue, plugins, and Workbench. The catalogue add-ons provide the skills, plugins, bundles, and workflows that AAS Core helps agents select and validate.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add sickn33/agentic-awesome-skills --skill apify-actor-developmentgit clone --depth 1 https://github.com/sickn33/agentic-awesome-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/sickn33/agentic-awesome-skills/apify-actor-development)<a href="https://agentmods.dev/skills/sickn33/agentic-awesome-skills/apify-actor-development"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/apify-actor-development.svg" alt="Measured on agentmods" height="20"></a>- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Prompt Injection · line 95 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
- medium MCP Rug Pull · line 122 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00071 | $0.02888 |
| Opus 5 | $0.00036 | $0.01444 |
| Sonnet 5 | $0.00014 | $0.00578 |
| Haiku 4.5 | $0.00007 | $0.00289 |
Grade A, and why
apify-actor-development scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
8 near-identical copies found in the catalogue:
- apify-actor-development — 100% identical, 1 lines differ
- apify-actor-development — 100% identical, 1 lines differ
- apify-actor-development — 98% identical, 3 lines differ
- apify-actor-development — 98% identical, 3 lines differ
- apify-actor-development — 94% identical, 13 lines differ
- apify-actor-development — 94% identical, 5 lines differ
- apify-actor-development — 94% identical, 7 lines differ
- apify-actor-development — 94% identical, 7 lines differ
How it starts
The opening of the file, as written. The whole thing — 231 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Apify Actor Development
Important: Before you begin, fill in the generatedBy property in the meta section of .actor/actor.json. Replace it with the tool and model you're currently using, such as "Claude Code with Claude Sonnet 4.5". This helps Apify monitor and improve AGENTS.md for specific AI tools and models.
When to Use
- You need to create, modify, or debug an Apify Actor project.
- The task involves choosing an Apify template, wiring actor inputs/outputs, or implementing actor runtime logic.
- You need safe setup guidance for
apifyCLI authentication, project bootstrap, or deployment workflow.
What are Apify Actors?
Actors are serverless programs inspired by the UNIX philosophy - programs that do one thing well and can be easily combined to build complex systems. They're packaged as Docker images and run in isolated containers in the cloud.
Core Concepts:
- Accept well-defined JSON input
- Perform isolated tasks (web scraping, automation, data processing)
- Produce structured JSON output to datasets and/or store data in key-value stores
- Can run from seconds to hours or even indefinitely
- Persist state and can be restarted
Prerequisites & Setup (MANDATORY)
Before creating or modifying actors, verify that apify CLI is installed apify --help.
If it is not installed, use one of these methods (listed in order of preference):
# Preferred: install via a package manager (provides integrity checks)
npm install -g apify-cli
# Or (Mac): brew install apify-cli
Security note: Do NOT install the CLI by piping remote scripts directly into a shell. Always use a package manager.
When the apify CLI is installed, check that it is logged in with:
apify info # Should return your username
If it is not logged in, check if the APIFY_TOKEN environment variable is defined (if not, ask the user to generate one on https://console.apify.com/settings/integrations and then define APIFY_TOKEN with it).
Then authenticate using one of these methods:
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday Changed · +1 lines c1de520d351a
- 3d ago First seen · 230 lines · 71 tokens per session scan A 20224b4ba5ff
apify-actor-development is a skill published in the GitHub repository sickn33/agentic-awesome-skills (46,133 stars, last pushed yesterday), licensed MIT. It adds 71 tokens to every session and 2,888 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-05.
Other skills, from other repositories
kubernetes-operator
Deploy and manage applications on Kubernetes. Covers deployments, services, ingress, HPA, secrets, and production-grade cluster configuration.
cost-optimizer
Professional Cost Optimizer Expert skill. Build performant, secure, and scalable backend logic and RESTful or GraphQL APIs.
infrastructure-as-code
Professional Infrastructure As Code Expert skill. Build performant, secure, and scalable backend logic and RESTful or GraphQL APIs.
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.
apify-actor-development
Important: Before you begin, fill in the generatedBy property in the meta section of .actor/actor.json. Replace it with the tool and model you're currently using, such as "Claude Code with Claude Sonnet 4.5". This helps Apify monitor and improve AGENTS.md for specific AI tools and models.
k8s-manifests
When writing K8s YAML, designing Helm charts, setting resource limits, configuring probes, or reviewing pod security.