data-fidelity

Fact-checking and source verification workflow for research documents. Launches parallel fact-checkers, aggregates findings, applies corrections systematically.

الانتقال إلى التثبيت

المستودع: Tibsfox/gsd-skill-creator
آخر نشاط في المصدر: ١٤ أبريل ٢٠٢٦ في ٠٧:٥٧
لغة SKILL.md المكتشفة: الإنجليزية
النجوم: ٦٩
التفرعات: ٩

خيارات التثبيت

يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.

التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.

يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.

npx skills add https://github.com/Tibsfox/gsd-skill-creator --skill data-fidelity

يبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.

تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.

مراجعة ملفات المصدر

اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.

SKILL.md

تعليمات المصدر · معاينة للقراءة فقط

العودة إلى البحث العودة إلى التثبيت

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

عرض الكل من هذا المستودع

name	data-fidelity
description	Fact-checking and source verification workflow for research documents. Launches parallel fact-checkers, aggregates findings, applies corrections systematically.
version	1.0.0
type	skill
category	research
status	stable
origin	tibsfox
modified	false
first_seen	"2026-03-31T00:00:00.000Z"

name	data-fidelity
description	Fact-checking and source verification workflow for research documents. Launches parallel fact-checkers, aggregates findings, applies corrections systematically.
version	1.0.0
type	skill
category	research
status	stable
origin	tibsfox
modified	false
first_seen	"2026-03-31T00:00:00.000Z"

data-fidelity

خيارات التثبيت

مراجعة ملفات المصدر

Data Fidelity

When to Use

Workflow

Phase 1: Fact-Check Fleet

Phase 2: Aggregate Findings

Phase 3: Data Refresh

Phase 4: Apply Corrections

Phase 5: Rebuild

Quality Standards

data-fidelity

معلومات المصدر

خيارات التثبيت

مراجعة ملفات المصدر

المزيد من هذا المستودع

المزيد من هذا المستودع

Data Fidelity

When to Use

Workflow

Phase 1: Fact-Check Fleet

Phase 2: Aggregate Findings

Phase 3: Data Refresh

Phase 4: Apply Corrections

Phase 5: Rebuild

Quality Standards