Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add ahmedawan-oracle/claude-code-plugins/plugin install oracle-ai-data-platform-workbench-databricks-migratorWrote 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/ahmedawan-oracle/claude-code-plugins/aidp-migrator-bootstrap)<a href="https://agentmods.dev/skills/ahmedawan-oracle/claude-code-plugins/aidp-migrator-bootstrap"><img src="https://agentmods.dev/badge/skills/ahmedawan-oracle/claude-code-plugins/aidp-migrator-bootstrap.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00073 | $0.01671 |
| Opus 5 | $0.00036 | $0.00835 |
| Sonnet 5 | $0.00015 | $0.00334 |
| Haiku 4.5 | $0.00007 | $0.00167 |
Grade A, and why
aidp-migrator-bootstrap scanned grade A with 1 finding 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 6d ago.
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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s -H "..." \ How it starts
The opening of the file, as written. The whole thing — 149 lines — stays where its author put it; the contents beside it link to each section on GitHub.
aidp-migrator-bootstrap — environment readiness check
Confirms everything the migrator needs is in place before any of the other skills attempt real work. Idempotent — re-runnable whenever you suspect drift.
When to use
- The user is running the migrator for the first time on this workstation.
- Any other skill fails with an OCI auth, network, or cluster-not-found error.
- The user moved to a new region / DataLake / workspace and you need to re-verify.
Required env-coords
Before any check below works, the user MUST have an env-coords.md (or any plain notes file) listing all of these. Refuse to proceed and ask for them if any are missing — never guess.
| Coordinate | Example shape (do NOT use any of these literal values — these are placeholders) |
|---|---|
| AIDP REST base URL | https://aidp.<region>.oci.oraclecloud.com/20240831 |
| DataLake OCID | ocid1.aidataplatform.oc1.<region>.<id> |
| Workspace UUID | <8-4-4-4-12> UUID format |
| Cluster ID | <8-4-4-4-12> UUID format |
| OCI profile name | <your-profile> (e.g. the section name in ~/.oci/config) |
| Output workspace path | Shared/aidp-migration-tool-output/ (or your team's path) |
Save these into a env-coords.md file at the project root, gitignored. Every other skill in this plugin threads these through verbatim. See references/env-coords.template.md for a complete scaffold.
Step-by-step
1. Python prereqs
The migrator engine ships bundled with this plugin under ${CLAUDE_PLUGIN_ROOT}/engine/. Install its Python dependencies once:
python3 --version # 3.10+
pip install -r ${CLAUDE_PLUGIN_ROOT}/engine/requirements.txt
Expected packages: oci, requests, websocket-client, anthropic, cryptography. If any is missing, install + retry.
2. ANTHROPIC_API_KEY env var
echo $ANTHROPIC_API_KEY | wc -c # should be >50 chars
If empty, the migrator's Pass-2 cell-by-cell loop will crash. Ask the user to export ANTHROPIC_API_KEY=sk-ant-....
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.
- 6d ago First seen · 149 lines · 73 tokens per session scan A 8ab84fb9dde6
aidp-migrator-bootstrap is a skill published in the GitHub repository ahmedawan-oracle/claude-code-plugins (2 stars, last pushed 1mo ago), licensed MIT. It adds 73 tokens to every session and 1,671 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
tensorrt-llm
High-throughput LLM inference on NVIDIA GPUs.
agent-platform-tuning
Agent Platform Model Tuning. Use when you need to fine-tune open models or Gemini models using Agent Platform infrastructure. Don't use for model training outside Agent Platform, model deployment to endpoints (use agent-platform-deploy), or managing serving endpoints (use agent-platform-endpoint-management).
google-cloud-solution-guided-gke-ai-migration
Guides the migration of existing AI workloads (Cloud Run, Gemini API, Gemini Enterprise Agent Platform) to self-hosted GKE inference using gcloud and kubectl. Use when the user has an existing AI inference workload (on Cloud Run, the Gemini API, Gemini Enterprise Agent Platform, or a custom VM) and wants to move it to…
agent-platform-endpoint-management
Manages Agent Platform serving endpoints. Use when you need to create, list, describe, update, or delete serving endpoints for model deployment on Agent Platform. Also use when troubleshooting endpoint permission, quota, or resource busy errors. Don't use for deploying models to endpoints or for running model…
gke-inference
Deploys and optimizes AI/ML inference workloads on GKE, using GPUs, TPUs, and model servers. Use when deploying GKE inference servers, configuring GKE GPU resources for inference, or deploying LLMs on GKE. Don't use for generic batch jobs or HPC task queues (use gke-batch-hpc instead).
modal
Modal is a serverless cloud platform for running Python on demand, including on-demand GPUs. Use when deploying or serving AI/ML models, running GPU-accelerated workloads (training, fine-tuning, inference), serving web endpoints, scheduling batch jobs, or scaling Python code to cloud containers with the Modal SDK.