RAG Source-to-Index Freshness Receipt
Compare local source and vector-index manifests to spot stale, orphaned and duplicate chunks.
$7
Works with the AI tools you already use
Claude Code
Cursor
Codex CLI
Muse
Dots+22 moreClaude CodeCursorCodex CLIMuseDots
OpenClaw
Manus
Grok
Grok Bot+18 agentsRAG Source-to-Index Freshness Receipt
Example session with this skill installed
Using the included invented source.jsonl and index.jsonl example manifests, run the local source-to-index freshness review. Show the exact exception counts and IDs, plus human checks. Do not connect to a vector store or claim live deletion.
- Read your context and instructions
- Compiled the rag source-to-index freshness
Synthetic RAG freshness review
Scope: three supplied source rows and four supplied active index rows.
Status: REVIEW. All IDs in this example are invented.
| Finding | Count | Supplied row to inspect |
|---|---|---|
| DELETION_NOT_PROPAGATED | 1 | old-policy / old-1 |
| DUPLICATE_ACTIVE_CHUNK_ID | 1 | faq-1, associated with unknown as well as a second active row |
| INDEX_ORPHAN | 1 | unknown / faq-1 |
| STALE_CHUNK | 1 | guide / guide-1 |
Human review queue: First confirm that the source and index exports cover the same snapshot window and serving index. Inspect the referenced rows in freshness-receipt.json and reconcile their ownership and revision data. Check the deletion marker for old-policy, resolve the duplicate faq-1 identifier, determine whether unknown is a valid source document, and compare guide-1 with the supplied source revision. Test live retrieval and deletion separately before claiming the index is fresh.
Limits: These findings come only from the submitted metadata manifests. Their completeness and authenticity have not been verified. A matching manifest would not prove actual vector search behavior, content hash correctness, backup deletion, or privacy-law compliance. No remote store was queried or modified.
Connects securely to your tools. The creator never sees your data.
What you get
About this skill
Export two metadata-only JSONL files from your own pipeline: one row per source document and one row per indexed chunk. The included local CLI or optional stdio MCP tool compares document IDs, revisions, supplied SHA-256 values, deletion markers and active chunk IDs. It writes a deterministic exception table, human-review checklist and integrity receipt. The companion SKILL.md guides an assistant through the review. The ZIP includes readable Python source, invented runnable manifests and a sample packet. Python 3.11–3.14 is required; optional MCP mode uses the pinned official SDK. No document bodies, vector-store connection, credentials or remote calls are needed. Results reconcile only supplied manifests; they do not verify content hashes, actual retrieval results, deletion from backups or privacy-law compliance.
How to install
Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.
- 1
Download the ZIP
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- 2
Unzip into your skills folder
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- 3
Ask your agent to use it
Restart the agent if it was already running. It picks the skill up automatically - no config needed.
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