ORCA Agent Skills
VerifiedTurn agent reasoning patterns into reusable executable skills.
What is ORCA Agent Skills?
Agent Skills Runtime is the reference implementation of ORCA, an architecture for packaging cognitive workflows as skills. It separates reusable logic from prompts by using capabilities as stable contracts and bindings that connect to execution environments such as Python, OpenAPI, or MCP.
The runtime schedules DAGs, applies policy controls, maintains cognitive state, and produces full execution traces. Skills can be tested, versioned, and moved across model providers or frameworks without rewriting core logic.
It targets developers building production agents who need explicit, auditable workflows instead of logic hidden inside prompts.
Capabilities
What you can build with ORCA Agent Skills
Automated code review
Define a multi-step skill that parses diffs, detects risks, scores confidence, and produces structured review output with validation.
Decision workflows under uncertainty
Run decision.make to generate options, analyze tradeoffs, assign confidence levels, and record uncertainties with auditable stages.
Text summarization pipelines
Execute language-summary skills on input text using deterministic local baselines for offline testing and consistent results.
Install ORCA Agent Skills
pip install orca-agent-skillsgit clone https://github.com/gfernandf/agent-skills.git
cd agent-skills
make bootstrap
python skills.py doctor
python skills.py run text.language-summary \
--input '{"text": "ORCA turns agent reasoning into reusable executable skills."}'- 1Clone the agent-skills repository from GitHub.
- 2Run make bootstrap or pip install the package with optional extras.
- 3Execute the doctor command to verify the environment.
- 4Invoke a skill such as text.language-summary with sample input.
- 5Inspect the generated trace and structured outputs.
Works with
ORCA Agent Skills: pros & cons
Pros
- +Skills become reusable and testable outside of prompts
- +Portable across multiple execution backends and providers
- +Built-in traceability, policy enforcement, and offline support
- +Explicit contracts reduce brittle prompt engineering
Cons
- –Requires learning the skill definition format and DAG concepts
- –Initial setup involves repository cloning and registry access
- –Advanced backends may still need separate configuration
Frequently asked questions
No, local runs use deterministic Python baselines without any keys.
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