skills.rest
kamilseghrouchni
kamilseghrouchniCOMMUNITY
Explore all skills by this author →
GitHub

query-deliver

Assemble request, candidate, and discovery data into buyer-facing recommendation files.

Updated Mar 31, 2026
Search Tags:#workflow#markdown#evidence#recommendation#jsonl#data-integration#assemble
One-click install
npx skills add https://github.com/kamilseghrouchni/vcro-sourcing --skill query-deliver
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: query-deliver
Source: https://github.com/kamilseghrouchni/vcro-sourcing/tree/main/.claude/skills/query/deliver
Command: npx skills add https://github.com/kamilseghrouchni/vcro-sourcing --skill query-deliver

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Assemble final recommendations by stitching together request, scored candidates, and discovery reports into a single buyer-facing document. This ensures transparency and deterministic output without re-scoring.

Core Features & Use Cases

  • Assemble and format a buyer-facing recommendation by compiling request.json, candidates.json, scored_candidates.json, and discover_report.md.
  • Generate publication-ready outputs: recommendation.md and listings.jsonl, with explicit evidence and traceability.
  • Operate deterministically by not re-scoring and by reusing the source data exactly as provided.

Quick Start

Place input artifacts in store/queries/{date_slug} and run the deliver skill to generate recommendation.md and listings.jsonl.

Frequently Asked Questions about query-deliver

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

FAQPage Schema
How do I assemble a final recommendation from multiple JSON and markdown files?▼

Generate a publication-ready recommendation by compiling request.json, scored_candidates.json, and discover_report.md into recommendation.md and listings.jsonl. This deterministic assembly reuses source data exactly as provided without re-scoring.

How do I generate recommendation.md and listings.jsonl from scored candidate data?▼

Generate a publication-ready recommendation by compiling request.json, scored_candidates.json, and discover_report.md into recommendation.md and listings.jsonl. This deterministic assembly reuses source data exactly as provided without re-scoring.

When do I need to consolidate request data and discovery findings into a formal deliverable?▼

Place input artifacts in the store/queries/{date_slug} directory and run the deliver skill to generate recommendation.md and listings.jsonl. The skill requires request.json, candidates.json, scored_candidates.json, and discover_report.md as inputs.

What's the best way to format a buyer-facing recommendation with traceable evidence?▼

Format a buyer-facing recommendation by stitching together request data, candidate scores, and discovery reports into recommendation.md and listings.jsonl. This ensures explicit evidence traceability and deterministic output without altering source scores.

Do I need to re-score candidates when assembling a final recommendation document?▼

No, you do not re-score candidates when assembling a final recommendation document. The process operates deterministically by reusing the supplied inputs from scored_candidates.json exactly as provided to ensure output integrity.

How to compile request.json and discover_report.md into publication-ready output files?▼

Compile request.json and discover_report.md by placing all input artifacts in store/queries/{date_slug} and running the deliver skill. This generates publication-ready recommendation.md and listings.jsonl files with explicit evidence and traceability.

Related Skills

Explore More Data & Analytics Skills→
kamilseghrouchnikamilseghrouchni

query-discover

Search the wiki with a request.json filter and produce candidates.json and discover_report.md.

Community
Intermediate
kuro0107kuro0107

publish-step7

Generate delivery inventory and _meta.md distribution metadata for a PUBLISH_V version.

Community
Basic
umairalipathan1980umairalipathan1980
19

resume-screener

Screen candidate resumes against job requirements with rubric-based scoring.

Community
Advanced
hongmaple0820hongmaple0820
9

daily-review

Generate standardized daily review documents from logs, conversations, and code commits.

Community
Intermediate