aidp-connectors-bootstrap
First-time setup. Use when the user wants to install/upload the AIDP Spark connectors helper package into their AIDP workspace, or has just installed this plugin and asks "how do I set it up", "first-time setup", "install the helpers", "bootstrap aidp connectors". Drives the AIDP MCP tools to push the helper package to /Workspace/Shared/ and runs a sanity import.
What this skill does
# `aidp-connectors-bootstrap` — first-time setup of the helper package in AIDP
## When to use
- The user just installed the plugin and asks "how do I set this up?", "what's the first step?", "install the helpers".
- The user runs a connector skill for the first time and gets `ModuleNotFoundError: No module named 'oracle_ai_data_platform_connectors'`.
- The user explicitly asks to upload the helper package to AIDP.
## Outcome of running this skill
- `/Workspace/Shared/oracle_ai_data_platform_connectors/scripts/oracle_ai_data_platform_connectors/` exists in the user's AIDP workspace, populated from the plugin's local `scripts/` directory.
- The user has run `examples/00_bootstrap_helpers.ipynb` once and it printed `BOOTSTRAP OK`.
- From that point on, every connector skill works without further setup.
## What you (Claude) should do
### Step 1 — locate the plugin's `scripts/` directory on disk
The plugin lives wherever the user installed it. Common locations:
- `~/.claude/plugins/<marketplace>/oracle-ai-data-platform-workbench-spark-connectors/scripts/oracle_ai_data_platform_connectors/`
- A local clone (when developing): the path printed by `claude --plugin-dir <path>`.
Run `find ~/.claude/plugins -type d -name oracle_ai_data_platform_connectors 2>/dev/null` (or the platform equivalent) to discover it. Confirm with the user before uploading.
### Step 2 — create the destination directory in AIDP
Use the AIDP MCP tools:
```
mcp__aidp__create_directory(
workspace_id="<user's workspace id>",
path="/Workspace/Shared/oracle_ai_data_platform_connectors"
)
mcp__aidp__create_directory(
workspace_id=...,
path="/Workspace/Shared/oracle_ai_data_platform_connectors/scripts"
)
```
(If the workspace_id isn't already known from the conversation, ask the user.)
### Step 3 — upload the package files
For each `.py` file under the local `scripts/oracle_ai_data_platform_connectors/`, upload to the matching path under `/Workspace/Shared/oracle_ai_data_platform_connectors/scripts/`. Use `mcp__aidp__upload_file` (or the equivalent in this MCP server).
The package layout to preserve:
```
oracle_ai_data_platform_connectors/
├── __init__.py
├── auth/{__init__,wallet,dbtoken,oci_config,user_principal,secrets}.py
├── jdbc/{__init__,oracle,hive}.py
├── rest/{__init__,fusion,epm,essbase}.py
└── streaming/{__init__,kafka}.py
```
### Step 4 — push the bootstrap notebook to AIDP and run it
Upload `examples/00_bootstrap_helpers.ipynb` to `Shared/connectors-tests/00_bootstrap_helpers.ipynb` via `mcp__aidp__nb_save_file`. Then `mcp__aidp__nb_create_session` against the user's chosen cluster (typically `tpcds`), and `mcp__aidp__nb_execute_code` for each cell. The final cell prints `BOOTSTRAP OK` if everything works.
### Step 5 — confirm
Tell the user:
- Where the helpers landed.
- That every connector skill in this plugin will now work.
- The next step is to pick a connector (e.g., `aidp-atp`) and supply that connector's env vars / Vault secrets.
## Alternative: ask the user to install via PyPI (v1.0+)
Once the package is published to PyPI, this skill should pivot to telling the user to run `%pip install oracle-ai-data-platform-connectors` in any AIDP cell instead of uploading. Until v1.0 ships, the Workspace-upload path above is the only way.
## What NOT to do
- Do not upload anything to `/Workspace/Shared/` without confirming the path with the user (in case they have an existing convention).
- Do not write secrets, .env contents, or PEM keys anywhere in `/Workspace/Shared/`. The helper package is code-only.
- Do not skip the sanity-import notebook — it's how you (and the user) confirm the upload worked, not just that files exist.
## References
- Bootstrap notebook: [`examples/00_bootstrap_helpers.ipynb`](../../examples/00_bootstrap_helpers.ipynb)
- Plugin README install section: [`README.md`](../../README.md)
Related in AI Agents
skill-development
IncludedComprehensive meta-skill for creating, managing, validating, auditing, and distributing Claude Code skills and slash commands (unified in v2.1.3+). Provides skill templates, creation workflows, validation patterns, audit checklists, naming conventions, YAML frontmatter guidance, progressive disclosure examples, and best practices lookup. Use when creating new skills, validating existing skills, auditing skill quality, understanding skill architecture, needing skill templates, learning about YAML frontmatter requirements, progressive disclosure patterns, tool restrictions (allowed-tools), skill composition, skill naming conventions, troubleshooting skill activation issues, creating custom slash commands, configuring command frontmatter, using command arguments ($ARGUMENTS, $1, $2), bash execution in commands, file references in commands, command namespacing, plugin commands, MCP slash commands, Skill tool configuration, or deciding between skills vs slash commands. Delegates to docs-management skill for official documentation.
reprompter
IncludedTransform messy prompts into well-structured, effective prompts — single or multi-agent. Use when: "reprompt", "reprompt this", "clean up this prompt", "structure my prompt", rough text needing XML tags and best practices, "reprompter teams", "repromptception", "run with quality", "smart run", "smart agents", multi-agent tasks, audits, parallel work, anything going to agent teams. Don't use when: simple Q&A, pure chat, immediate execution-only tasks. See "Don't Use When" section for details. Outputs: Structured XML/Markdown prompt, quality score (before/after), optional team brief + per-agent sub-prompts, agent team output files. Success criteria: Single mode quality score ≥ 7/10; Repromptception per-agent prompt quality score 8+/10; all required sections present, actionable and specific.
adaptive-compaction
IncludedAdaptive add-on policy and recovery layer that decides WHEN to compact, prune, snapshot, or fork -- replacing fixed-percent auto-compaction across Claude Code, Codex, and MCP-capable hosts. Trigger on auto-compact timing or damage: "when should I compact", "is it safe to compact now or start a fresh session", "auto-compact fires too early/mid-task", "switching to an unrelated task but the window still has space", "context rot", "answers get worse the longer the session runs", "the agent forgot the plan or my decisions after it summarized", "add a layer on top that manages context without changing the agent", raising autoCompactWindow to give the policy room, or installing/tuning a cross-tool compaction policy or PreCompact hook -- even when "compaction" is never said but the problem is context-window pressure or post-summarization memory loss. Do NOT use to summarize a conversation, build RAG, write a summarization prompt (decides WHEN not HOW), or answer max-context-length trivia.
agent-skill-creator
IncludedCreate cross-platform agent skills from workflow descriptions. Activates when users ask to create an agent, automate a repetitive workflow, create a custom skill, or need advanced agent creation. Triggers on phrases like create agent for, automate workflow, create skill for, every day I have to, daily I need to, turn process into agent, need to automate, create a cross-platform skill, validate this skill, export this skill, migrate this skill. Supports single skills, multi-agent suites, transcript processing, template-based creation, interactive configuration, cross-platform export, and spec validation.
llm-wiki
IncludedUse when building or maintaining a persistent personal knowledge base (second brain) in Obsidian where an LLM incrementally ingests sources, updates entity/concept pages, maintains cross-references, and keeps a synthesis current. Triggers include "second brain", "Obsidian wiki", "personal knowledge management", "ingest this paper/article/book", "build a research wiki", "compound knowledge", "Memex", or whenever the user wants knowledge to accumulate across sessions instead of being re-derived by RAG on every query.
skill-master
IncludedAgent Skills authoring, evaluation, and optimization. Create, edit, validate, benchmark, and improve skills following the agentskills.io specification. Use when designing SKILL.md files, structuring skill folders (references, scripts, assets), ingesting external documentation into skills, running trigger evals, benchmarking skill quality, optimizing descriptions, or performing blind A/B comparisons. Keywords: agentskills.io, SKILL.md, skill authoring, eval, benchmark, trigger optimization.