Nemo Retriever

nemo-retriever

The retriever CLI indexes a folder of PDFs into LanceDB (retriever ingest) and serves vector search over it (retriever query). For any task about searching/answering questions across a folder of PDFs, use this CLI — do not write a custom RAG.

Beyond PDFs and beyond semantic search. retriever ingest also handles images, Office, HTML, TXT, audio, and video — see references/setup.md for the per-format recipe and references/install.md for the install extras ([multimedia], libreoffice, ffmpeg). For non-semantic operations — page filter, verbatim quote with citation, corpus-level aggregate, chart/image caption hits — see references/query.md. Don't fall back to native Read/Grep/Python on non-PDF inputs.

Install (if retriever is missing)

If command -v retriever returns nothing, follow references/install.md to install the NeMo Retriever Library before proceeding. It prints RETRIEVER_VENV=<path>; substitute that path for <RETRIEVER_VENV> in every example in this skill (setup, query, troubleshooting, and the CLI references).

Workflow — read the reference for the current phase, then execute

For the full retriever ingest / retriever query CLI specs, see references/cli/ingest.md and references/cli/query.md. You do not need these for routine turns — <RETRIEVER_VENV>/bin/retriever <subcommand> --help is faster.

Before ingesting a mixed folder, inventory extensions (find <dir> -name '*.*' | sed 's/.*\.//' | sort -u) — --input-type=auto silently drops anything outside the supported set. See references/troubleshooting.md "Unsupported file types".

Hard limits (apply to every turn)

• Setup turn: build the index in one shell command (see references/setup.md). STOP after the index lands.
• Query turn: at most 2 Bash calls — 1 retriever query, +1 optional targeted text-extract per references/query.md. Reply and then STOP.
• No narration between tool calls. Tokens you emit between calls become input + cached input for every later turn — quadratic cost. Go straight from reading the summary to writing the JSON file.
• Banned: TodoWrite, Glob, Grep, Read of whole PDFs, re-running setup, spawning subagents, speculative "confirmation" calls.

Long query turns (5+ tool calls, 1M+ cache-read tokens) cost ~5× a disciplined turn and almost always still produce the wrong answer. Answering partially beats timing out.

Cross-Client Portability

This skill is written to stay usable across GitHub Copilot, Claude Code, and Codex.

• GitHub Copilot: keep the folder in a Copilot-visible skill path or wrap the workflow in project instructions when folder discovery is unavailable.
• Claude Code: keep the folder in a local skills directory or a compatible plugin source.
• Codex: install or sync the folder into $CODEX_HOME/skills/nemo-retriever and restart Codex after major changes.

MCP Availability And Fallback

Preferred MCP Server: None required

• Fallback prompt: "Use the nemo-retriever skill without MCP. Rely on its local instructions, bundled resources, standard shell or editor tools, and direct verification. Show the evidence used before concluding."
• Do not claim an MCP operation was used when the active host does not expose it.
• Treat local files, tests, rendered outputs, logs, or screenshots as the fallback evidence path.

Anti-Patterns

• Indexing content before clarifying corpus boundaries, freshness, or ownership: Retrieval quality collapses when the source of truth is unstable.
• Treating embedding, chunking, and backend choices as invisible defaults: They change recall, latency, and storage cost in user-visible ways.
• Claiming grounded answers without checking the retrieved passages that supported them.

Verification Protocol

Before claiming "skill applied successfully":

1. Pass/fail: The workflow names the corpus, index or backend choice, and the query path before answering deployment or QA questions.
2. Pass/fail: Retrieval checks include at least one real query and inspection of the supporting passages or scores.
3. Pass/fail: Ingestion or indexing advice keeps corpus freshness and reindex cost visible instead of implicit.
4. Pressure-test scenario: Apply the workflow to a retriever that answers quickly but returns stale passages after a corpus update.
5. Success metric: The user gets a reproducible retriever setup or debugging path with live retrieval evidence.

Related Skills

• notebooklm-management: Use it when retrieval-backed research needs a notebook-style grounding workflow.
• development-workflow: Use it when the retriever work also needs scoped implementation and validation checkpoints.
• cloud-design-patterns: Use it when the retriever deployment choice also needs storage, scaling, or service-boundary analysis.