# Cross Source Gap Analysis

> Compare findings across Slack, GitHub, NVIDIA forums, and Outlook to identify alignment gaps, missing coverage, and follow-ups.

- Skill: `nvidia/cross-source-gap-analysis` (Agent Skill)
- Install (CLI): `npx skillmds@latest add nvidia/cross-source-gap-analysis`
- Raw SKILL.md: https://api.skillmd.com/api/skills/nvidia/cross-source-gap-analysis/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Integrations & APIs
- Author: NVIDIA (https://skillmd.com/u/nvidia)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/nvidia/cross-source-gap-analysis

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# cross-source-gap-analysis

Use this skill when the task is to compare or synthesize information across
multiple sources rather than merely access one source.

## When to use

- Compare Slack discussion against GitHub issues or PRs
- Compare Slack or GitHub findings against NVIDIA forum discussion
- Identify missing coverage, inconsistent narratives, or follow-up areas across sources
- Find external email discussions not reflected in internal meeting updates

## Inputs

Load the source skills you need first:

- `slack-channel-summarizer`
- `slack-channel-finder`
- `github-readonly-live`
- `source-etl-query`
- `outlook-email-search`

This skill does not define how to access those systems. It defines how to
combine the findings once you have them.

## Procedure

### 1. Gather the minimum useful evidence from each source

Prefer a small, relevant slice from each source over broad collection. For example:

- a recent Slack window for the relevant channel
- live GitHub issues or PRs from `$GITHUB_READONLY_REPO`, when current state
  matters
- mirrored GitHub discussions, or historical issue/PR mirror data for the repo
  or feature area, when the task is about the ETL mirror or discussions
- mirrored NVIDIA forum topics for the `nemoclaw` tag scope
- recent emails filtered to the relevant project and date range

### 2. Normalize what each source is saying

Reduce each source to short bullets such as:

- active topics
- reported problems
- decisions or planned work
- requests for help
- unresolved questions

### 3. Compare across sources

### 4. Present the result

A good default structure is:

- scope and time window
- what all sources agree on
- gaps or mismatches
- concrete follow-ups

Keep the comparison grounded in evidence from the sources you actually checked.
Do not invent gaps just because one source had less data available.

## Pitfalls

- Do not force a gap-analysis framing when the user only asked for source access
  or a plain summary.
- Do not over-collect. A narrow comparison is usually better than an exhaustive scrape.
- Distinguish between "not discussed" and "not observed in the sampled data".
- Distinguish between “not observed in the mirror” and “not present on the live source”.
- Keep live GitHub REST findings separate from source ETL mirror findings when
  the scopes or freshness differ.