Dockerized Skill Scaffold Generator

You are tasked with generating a new Agent Skill resource using our deterministic backend scaffolding pipeline, specifically tailored for Containerized Computational Workloads (like bioinformatics, deep learning, or local db spinning).

Execution Steps

1. Requirements & Design Phase

Ask the user what specific external container or pipeline orchestrator is being targeted. Core Questions:

• Skill Name: Must be descriptive, kebab-case.
• Trigger Description: What exactly triggers this? Write in third person.
• Dependencies: What external binaries are required on the host? (e.g., docker, nextflow, nvidia-smi).
• Network Scope: Does this pull models from HuggingFace, data from NCBI, or containers from Docker Hub? (Required for the security whitelist).

2. Scaffold the Infrastructure

Execute the deterministic scaffold.py script to generate the compliant physical directories:

python3 ./scripts/scaffold.py --type skill --name <requested-name> --path <destination-directory> --desc "<short-description>"

3. Generate Pre-Flight Checker Script

Instead of a generic execute.py, generate a robust scripts/check_environment.py (referencing the required binaries). The script MUST explicitly verify the Docker daemon is running or the required orchestrator is present in PATH before ever attempting to execute work.

4. Generate Security Override Manifest

Because container orchestration fundamentally requires subprocess calls and often network fetches, this skill will fail deterministic security Phase 5 P0 checks unless whitelisted. Use file writing tools to inject a security_override.json at the root of the new skill:

{
  "justification": "Docker container orchestration requires host subprocess execution and image registry network calls.",
  "whitelisted_calls": ["subprocess.run", "requests", "urllib"]
}

5. Finalize SKILL.md

Populate the SKILL.md ensuring the flow forces the AI to run scripts/check_environment.py FIRST before ever attempting the containerized workload.

6. Iteration Governance (Autoresearch-Compatible)

If this dockerized skill will be tuned iteratively, require:

1. Baseline-first measurement.
2. Single-hypothesis iteration steps.
3. Keep/discard decisions with explicit rollback on regressions.
4. Crash/timeout logging to preserve failure learnings.
5. Persistent experiment ledger in evals/results.tsv.

Next Actions

• Continuous Improvement: Run ./scripts/benchmarking/run_loop.py --results-dir evals/experiments to tune trigger quality over iterations.
• Review Loop: Run ./scripts/eval-viewer/generate_review.py to inspect run quality and failure patterns.
• Audit: Offer to run audit-plugin to validate the generated artifacts.