research-engine

Autonomous research pipeline — topic to structured documents with HTML/PDF output. Proven at HEL (28 docs, 91K words) and OOPS (9 docs, 20K words) scale.

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Repositorio: Tibsfox/gsd-skill-creator
Última actividad en el origen: 14 de abril de 2026 a las 07:57
Idioma detectado de SKILL.md: inglés
Estrellas: 69
Forks: 9

Opciones de instalación

De forma predeterminada está seleccionado el prompt que primero revisa el origen. Puedes cambiar a un comando directo o descargar una copia local.

Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.

Un comando directo omite el prompt de revisión. Revisa el origen antes de ejecutarlo.

npx skills add https://github.com/Tibsfox/gsd-skill-creator --skill research-engine

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Lee SKILL.md y los archivos complementarios que muestra SkillsMP antes de decidir si quieres instalarlo.

SKILL.md

Instrucciones de origen · Vista previa de solo lectura

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goal-ambiguity-gate

Use the moment a request is underspecified and you are about to act on an ASSUMED goal. Enumerate the candidate goals the request could mean, project the action or artifact each one implies, and check whether they diverge: if two or more plausible goals lead to materially different actions or artifacts, ask exactly ONE targeted clarifying question; otherwise proceed on the most-likely goal and state the assumption in one line. Clarification is an evidence-producing action, not a delay — but one question, never an interrogation. Distinct from gsd-spec-phase (GSD-phase-bound, emits a heavy SPEC.md) and intent-router (fetch strategy, not goal disambiguation). Backed by agent goal-state inference (arxiv 2606.16813v1). Triggers on acting under an assumed goal when the request admits more than one materially different reading.

2026-07-19

memory-hubness-gate

Use at write time to vector/embedding memory — Grove content-addressed insertion, chroma/pgvector upserts, memory-consolidation promoting session traces to MEMORY.md, or embedding externally-ingested content. Scores each candidate record against a fixed panel of sentinel queries and quarantines any record that would become the nearest neighbor of too many unrelated queries — a hub — whether from adversarial poisoning or accidental over-generality. This is the memory-record-side sibling of skill-injection-guardian (file-side) and the write-side complement of memory-use-warrant (read-side). Quarantine, never silently drop; a human reviews. Backed by the admission-time hubness gate (arxiv 2606.19692v1). Triggers on inserting into vector memory, consolidating memory, and embedding stranger content.

2026-07-19

memory-use-warrant

Run this appropriateness check the moment you are about to integrate a retrieved long-term memory — a Grove content-addressed hit, a chroma/pgvector neighbour, a memory-consolidation digest, or a MEMORY.md line — into a response, especially anything touching private origins, Fox Companies IP, credentials, or Center Camp / consent-governed content. It answers a question intent-router never asks: not WHAT to fetch or HOW, but WHETHER a correctly-retrieved item should reach output. Relevance is not appropriateness — a perfect similarity match can still be a boundary violation. Default is FAIL-CLOSED: if in-context authorization is uncertain, the memory may inform behaviour but must not be surfaced. Backed by RBI-Eval (arxiv 2606.06055v1). Triggers on surfacing recalled sensitive memory into a response.

2026-07-19

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name	research-engine
description	Autonomous research pipeline — topic to structured documents with HTML/PDF output. Proven at HEL (28 docs, 91K words) and OOPS (9 docs, 20K words) scale.
version	1.0.0
type	skill
category	research
status	stable
origin	tibsfox
modified	false
first_seen	"2026-03-31T00:00:00.000Z"
first_path	.claude/skills/research-engine/SKILL.md
superseded_by	null

Research Engine

Activates when user requests deep research, investigation, or analysis of a topic that will produce multiple structured documents.

Pipeline

TOPIC → DECOMPOSE → PARALLEL RESEARCH → AGGREGATE → STRUCTURE → BUILD → PUBLISH

Stage 1: Decompose

Break the research topic into 4-12 investigation tracks. Each track becomes one document.

Stage 2: Parallel Research

Launch research agents (2-6 in parallel) with specific investigation briefs. Use run_in_background: true for independence.

Stage 3: Aggregate

Collect findings. Check for contradictions. Identify cross-references.

Stage 4: Structure

Organize into a numbered document series with:

Index page (index.html)
Individual research documents (research/NN-slug.md)
Knowledge graph (knowledge-nodes.json)
Retrospective (retrospective.md)

Stage 5: Build

Convert markdown to HTML and PDF using pandoc + xelatex:

bash build.sh  # Uses template.tex and html-template.html

Stage 6: Publish

Commit, tag, merge to main, push, create GitHub release, FTP sync to configured remote host.

Quality Standards

Minimum 1,500 words per document
Every claim needs evidence or explicit reasoning
Cross-references between documents
Fact-check pass before final publish
Three output formats: markdown (source), HTML (reading), PDF (sharing)

Project Codes

Each research project gets a 3-4 letter code added to series.js:

HEL (Helium Supply Chain)
OOPS (Ecosystem Alignment)
OPEN (Unsolved Problems)

Build Infrastructure

Templates at the project level:

template.tex — LaTeX template for branded PDFs
html-template.html — Standalone HTML template
build.sh — Automated pandoc build script

research-engine

Datos de origen

Opciones de instalación

Revisa los archivos de origen

Más de este repositorio

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Research Engine

Pipeline

Stage 1: Decompose

Stage 2: Parallel Research

Stage 3: Aggregate

Stage 4: Structure

Stage 5: Build

Stage 6: Publish

Quality Standards

Project Codes

Build Infrastructure

Research Engine

Pipeline

Stage 1: Decompose

Stage 2: Parallel Research

Stage 3: Aggregate

Stage 4: Structure

Stage 5: Build

Stage 6: Publish

Quality Standards

Project Codes

Build Infrastructure