create-docker-skill · diff
git:20260321.b056f9f to git:20260328.04e9e1c
19 added, 55 removed. Audit A to A.
---
name: create-docker-skill
- description: Interactive initialization script that generates a compliant Agent Skill containing pre-flight environment checks, subprocess execution scaffolding, and a security-override config. Use when authoring new workflow routines that depend on external containerized runtimes (e.g., Docker, Nextflow, HPC).
+ description: Scaffold an agent skill with Docker runtime support
+ argument-hint: "[skill-name]"
allowed-tools: Bash, Read, Write
---
- ## Dependencies
-
- This skill requires **Python 3.8+** and standard library only. No external packages needed.
-
- **To install this skill's dependencies:**
- ```bash
- pip-compile ./requirements.in
- pip install -r ./requirements.txt
- ```
-
- See `../../requirements.txt` for the dependency lockfile (currently empty — standard library only).
-
- ---
- # 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).
+ Follow the `create-docker-skill` skill workflow to scaffold a compliant agent skill
+ that depends on containerized runtimes (Docker, Nextflow, HPC).
- ## Execution Steps
+ ## Inputs
- ### 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).
+ - `$ARGUMENTS` — optional skill name or use-case description. Omit to start with discovery.
- ### 2. Scaffold the Infrastructure
- Execute the deterministic `scripts/scaffold.py` script to generate the compliant physical directories:
- ```bash
- python3 ./../scripts/scaffold.py --type skill --name <requested-name> --path <destination-directory> --desc "<short-description>"
- ```
+ ## Steps
- ### 3. Generate Pre-Flight Checker Script
- Instead of a generic `execute.py`, generate a robust `scripts/check_environment.py` (referencing the required binaries).
- The script MUST explicitly verify the Docker daemon is running or the required orchestrator is present in PATH before ever attempting to execute work.
+ 1. If `$ARGUMENTS` provides a skill name, use it to seed the discovery phase
+ 2. Follow the create-docker-skill phased workflow: determine container runtime and
+ workflow type, gather environment check requirements, design pre-flight validation
+ and subprocess execution scaffolding, then generate the skill directory
+ 3. Report the created skill path and Docker environment setup instructions
- ### 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:
- ```json
- {
- "justification": "Docker container orchestration requires host subprocess execution and image registry network calls.",
- "whitelisted_calls": ["subprocess.run", "requests", "urllib"]
- }
- ```
+ ## Output
- ### 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.
+ Skill directory with `SKILL.md` containing pre-flight environment checks, subprocess
+ execution patterns, security-override config, and Docker-aware error handling.
- ### 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`.
+ ## Edge Cases
- ## Next Actions
- - **Continuous Improvement**: Run `./scripts/run_loop.py --results-dir evals/experiments` to tune trigger quality over iterations.
- - **Review Loop**: Run `scripts/generate_review.py` to inspect run quality and failure patterns.
- - **Audit**: Offer to run `audit-plugin` to validate the generated artifacts.
+ - If `$ARGUMENTS` is empty: begin with discovery — do not assume Docker is available
+ - If Docker is not installed in the target environment: generate graceful degradation
+ - If the workflow uses HPC or Nextflow instead of Docker: adapt scaffolding accordingly