node_resolver

Resolve free-text hierarchy references to numeric node IDs via CSV lookup.

Updated Aug 27, 2026
One-click install
npx skills add https://github.com/SpecForgeAI/deepagent-bot --skill node-resolver
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: node_resolver
Source: https://github.com/SpecForgeAI/deepagent-bot/tree/main/skills/node_resolver
Command: npx skills add https://github.com/SpecForgeAI/deepagent-bot --skill node-resolver

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Resolves free-text business hierarchy references (desk names, team names, org paths) to numeric node IDs deterministically by searching a pre-indexed CSV. No LLM interpretation — this is pure deterministic lookup with fuzzy fallback.

Core Features & Use Cases

  • Exact-match mapping against a pre-indexed CSV to derive node_id.
  • Fuzzy matching and token-overlap for noisy input, returning candidate lists with confidence.
  • Output is a structured JSON payload containing node_id, confidence, candidates, and supporting evidence for downstream workflows that require an identifier.

Quick Start

Pass a desk name, team name, or hierarchy text to the Node Resolver to obtain the best node_id and candidate matches.

Frequently Asked Questions about node_resolver

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I resolve free-text desk names to numeric node IDs using a CSV lookup?▼

To resolve desk names to numeric node IDs, this Skill performs a deterministic CSV lookup by detecting ID and label columns, then returning a JSON payload containing the matched node_id, confidence, and evidence. It uses no LLM interpretation for the primary match.

What happens when exact hierarchy text matching fails during node_id resolution?▼

When exact hierarchy text matching fails, node_id resolution applies an optional fuzzy fallback using token-overlap to generate candidate matches. It returns these candidates with confidence scores and supporting evidence to ensure downstream processing still has data.

Does node_id resolution require any dependencies or LLM integration to process organization paths?▼

No, node_id resolution does not require any dependencies or LLM integration to process organization paths. It is a pure deterministic lookup mechanism that searches a pre-indexed CSV to map free-text organization paths to numeric identifiers.

What is the best way to map noisy team names to identifiers for downstream data processing?▼

The best way to map noisy team names to identifiers for downstream data processing is using a deterministic lookup with fuzzy fallback. This approach returns a structured JSON payload with node_id, confidence, and candidates, avoiding the inconsistency of LLM interpretation.

What is included in the JSON output when resolving hierarchy references to identifiers?▼

The JSON output for resolving hierarchy references includes the resolved node_id, a confidence score, a list of fuzzy candidates, and supporting evidence. This structured format provides the exact data needed for downstream workflows requiring an identifier.