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/connector-audit

Deep reliability audit for OpenMetadata connectors — runs 7 investigation prompts (metadata, errors, auth, lineage, scale, synthesis, implementation) against connector standards

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$ npx -y skills add open-metadata/OpenMetadata --skill connector-audit --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/connector-audit

Context preview

The summary Claude sees to decide when to auto-load this skill.

Deep reliability audit for OpenMetadata connectors — runs 7 investigation prompts (metadata, errors, auth, lineage, scale, synthesis, implementation) against connector standards

SKILL.md

connector-audit.SKILL.md
name: connector-audit
description: Deep reliability audit for OpenMetadata connectors — runs 7 investigation prompts (metadata, errors, auth, lineage, scale, synthesis, implementation) against connector standards

OpenMetadata Connector Reliability Audit

When to Activate

When a user asks to audit a connector, run a reliability audit, or investigate connector quality in depth.

STEP 1 — DO THIS FIRST, BEFORE ANYTHING ELSE

When this skill is invoked, your VERY FIRST action — before responding to the user, before summarizing anything, before checking any state — is:

1. List all files in `.claude/audit-results/` (if the directory exists) 2. List `.claude/connector-audit.json` (if it exists) 3. Present the file list to the user and ask: *"These files exist from a previous audit. Which should I keep and which should I delete?"* 4. Wait for the user's answer. Do NOT proceed until they respond.

If no files exist, skip to Step 2.

**NEVER** summarize existing results, say "the audit is complete", or suggest the user doesn't need to run anything. The user invoked the skill — execute it.

Arguments

  • **Connector name** (e.g., `mysql`, `snowflake`, `tableau`): Full 7-prompt audit
  • **`--prompt N`** (e.g., `--prompt 3`): Run a single prompt (1-7) — useful for re-running after fixes
  • **`--prompts N,M`** (e.g., `--prompts 1,4`): Run specific prompts only
  • **`--from N`** (e.g., `--from 6`): Run prompts N through 7 — useful for continuing after P1-P5
  • **`--setup-only`**: Run setup only (writes connector-audit.json)
  • **`--dry-run`**: When used with P7, produce a detailed implementation plan (before/after diffs, tests, risk flags) without actually writing code — for review before execution

Relationship to connector-review

| | connector-audit | connector-review | |---|---|---| | **Purpose** | Deep reliability investigation | Breadth check for PRs | | **Depth** | 7 focused prompts, hours of analysis | 5 parallel agents, minutes | | **Output** | `.claude/audit-results/` (7 reports) | PR comment or local report | | **When** | Before major work on a connector | During PR review | | **Scope** | Full connector + base classes + shared code | Changed files only |

Workflow Overview

Setup → P1-P5 (investigation, parallelizable) → P6 (synthesis) → P7 (implementation)

Phase 1: Setup

After the stale results check (Step 1 above), run the setup prompt to establish connector context. This writes `.claude/connector-audit.json` which all subsequent prompts read.

Phase 2: Static Pre-Check

Run the static analyzer from the connector-review skill to get a mechanical baseline:

python skills/connector-review/scripts/analyze_connector.py {service_type} {name} --json

Save the output — prompts reference it to avoid duplicating mechanical checks.

Phase 3: Investigation (P1-P5)

These 5 prompts are **independent** and can run in any order. For efficiency, dispatch them in parallel:

  • **Pair A**: P1 (Metadata & Ingestion) + P2 (Error Handling)
  • **Pair B**: P3 (Connection & Auth) + P4 (Lineage)
  • **Solo**: P5 (Scale & Performance)

Each prompt: 1. Reads `.claude/connector-audit.json` for context 2. Loads connector standards via `/connector-standards` 3. Investigates its focus area in depth 4. Presents a summary for user review 5. Saves its report under `.claude/audit-results/` using the fixed filename for that prompt described in the **Output Structure** section (for example, `01-metadata-ingestion.md` for P1) after user approval

**User review gate**: Each prompt presents findings and asks for confirmation before saving. This catches errors early instead of propagating them to P6.

Phase 4: Synthesis (P6)

Reads all 5 reports from `.claude/audit-results/`, cross-validates findings, clusters root causes, checks git history, and produces a prioritized implementation plan with PR scoping.

Phase 5: Implementation (P7)

Reads the P6 plan and implements the fixes — writes code, runs tests, creates commits. With `--dry-run`, produces a detailed plan (before/after diffs, test code, risk flags) without writing code.

Prompt Files

Each prompt is a self-contained investigation guide in `prompts/`:

| # | File | Focus | Standards | |---|---|---|---| | 0 | `00-setup.md` | Set target connector, write context file | — | | 1 | `01-metadata-ingestion.md` | Metadata coverage by tier, ingestion completeness | Tiers 1-3, Standard 1 | | 2 | `02-error-handling.md` | Error handling, fault tolerance, observability | Standards 4, 5, 7 | | 3 | `03-connection-auth.md` | Auth methods, test connection, SSL/TLS | Standard 3 | | 4 | `04-lineage.md` | SQL dialect, FQN resolution, column lineage | Standard 2 | | 5 | `05-scale-performance.md` | Memory patterns, pagination, generators, lookups | Standard 6 | | 6 | `06-refactor-plan.md` | Cross-validate, cluster root causes, PR scoping | All standards | | 7 | `07-implementation.md` | Implement fixes (or `--dry-run` for plan only) | All standards |

How to Run

Full Audit

/connector-audit mysql

1. **Stale results check**: List existing files in `.claude/audit-results/`, ask user what to keep/delete, wait for answer 2. **Setup**: Ask user for connector name (if not provided), find source directory, write `.claude/connector-audit.json` 3. **Static pre-check**: Run `analyze_connector.py` for mechanical baseline 4. **P1-P5**: For each prompt: a. Read the prompt file from `prompts/0N-*.md` b. Follow its instructions — read the actual connector source code, analyze it against the standards, produce findings with file:line references c. Present a summary to the user for review d. Save the report to `.claude/audit-results/` after user approval 5. **P6**: Read all reports from `.claude/audit-results/`, synthesize, present implementation plan, save after approval 6. **P7**: Read P6 plan, implement fixes (or `--dry-run` for plan only), save after approval

Each prompt is a detailed investigation guide — it tells you

Read more
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The Open Context Layer for Data and AI , OpenMetadata is the open platform for building trusted data context and business semantics for humans, AI assistants, and agents.

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