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 ahmedawan-oracle/claude-code-plugins --skill aidp-fusion-configgit clone --depth 1 https://github.com/ahmedawan-oracle/claude-code-pluginsWrote 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-fusion-config)<a href="https://agentmods.dev/skills/ahmedawan-oracle/claude-code-plugins/aidp-fusion-config"><img src="https://agentmods.dev/badge/skills/ahmedawan-oracle/claude-code-plugins/aidp-fusion-config/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ahmedawan-oracle/claude-code-plugins/aidp-fusion-config"><img src="https://agentmods.dev/badge/skills/ahmedawan-oracle/claude-code-plugins/aidp-fusion-config.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00144 | $0.01445 |
| Opus 5 | $0.00072 | $0.00723 |
| Sonnet 5 | $0.00029 | $0.00289 |
| Haiku 4.5 | $0.00014 | $0.00145 |
Grade A, and why
aidp-fusion-config 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 10d 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.
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.
How it starts
The opening of the file, as written. The whole thing — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
aidp-fusion-config — fill aidp.config.yaml from names, not OCIDs
The worst part of fusion-autopilot setup is aidp.config.yaml: it wants three opaque identifiers —
aiDataPlatformId (a long OCID), workspaceKey, and clusterKey (UUIDs). Customers should not hunt those
out of console URLs and REST responses by hand. This skill collects human-friendly names and resolves the
keys live. This is a control-plane skill — no MCP, no notebook session required.
When to use
- "Set up aidp.config.yaml", "configure the fusion autopilot connection", "what do I put for workspaceKey/clusterKey", "I don't have the OCIDs", "fill in the AIDP coords by name".
- The user ran
init, seesaidp.config.yamlfull of*-PLACEHOLDERvalues, and asks what goes there. - A precondition check (e.g. the
aidp-fusion-seedladder) reports the config is missing or has placeholders.
When NOT to use
- Authoring
bundle.yaml'sfusion:connectivity / credentials — customer-supplied policy the CLI can't discover (useinit+ hand-edit). This skill only fills the coordinates inaidp.config.yaml. - Resolving tenant data variation (column aliases, semantic variants) — that's
bootstrap/medallion-author.
Engine — the self-contained aidp-fusion-autopilot init-config command
The skill shells out to one CLI command; it never re-implements OCI signing or REST calls. The command reuses
the plugin's own AidpRestClient discovery primitives (find_workspace_by_name / find_cluster_by_name,
OCI-signed) to turn names into keys, validates against the AidpConfig schema, then writes the env block.
| Op | What you give | What it resolves / writes |
|---|---|---|
| Resolve workspace | --workspace "<display name>" |
→ workspaceKey |
| Resolve cluster | --cluster "<display name>" |
→ clusterKey + live state (warns if not ACTIVE) |
| Anchor | --aidp-id <DATALAKE_OCID> |
the one root id (copied once from the console URL) |
| Target env | top-level --env <name> (default dev) |
writes environments.<name>, preserves siblings |
| Preview | --dry-run |
prints resolved keys + YAML, writes nothing |
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.
- 10d ago First seen · 86 lines · 144 tokens per session scan A f5af0e284c36
aidp-fusion-config is a skill published in the GitHub repository ahmedawan-oracle/claude-code-plugins (2 stars, last pushed 1mo ago), licensed MIT. It adds 144 tokens to every session and 1,445 once invoked, about $0.0007 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 2026-08-31.
Other skills, from other repositories
tensorrt-llm
High-throughput LLM inference on NVIDIA GPUs.
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-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).
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.
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…