augmented-reality-tracking

Tracking and registration fundamentals for augmented reality. Covers the six classes of tracking (marker, markerless, inertial, SLAM, outside-in, inside-out), registration error sources (calibration, latency, drift, distortion), the Azuma definition of AR, display configurations (optical see-through, video see-through, projection, handheld), and the interaction between tracking precision and user perception of "being there." Use when designing AR experiences, diagnosing registration issues, or selecting tracking technology.

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Repository: Tibsfox/gsd-skill-creator
Letzte Quellaktivität: 14. April 2026 um 07:57
Erkannte Sprache von SKILL.md: Englisch
Sterne: 69
Forks: 9

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npx skills add https://github.com/Tibsfox/gsd-skill-creator --skill augmented-reality-tracking

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SKILL.md

Quellanweisungen · Schreibgeschützte Vorschau

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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

Alle aus diesem Repository ansehen

name	augmented-reality-tracking
description	Tracking and registration fundamentals for augmented reality. Covers the six classes of tracking (marker, markerless, inertial, SLAM, outside-in, inside-out), registration error sources (calibration, latency, drift, distortion), the Azuma definition of AR, display configurations (optical see-through, video see-through, projection, handheld), and the interaction between tracking precision and user perception of "being there." Use when designing AR experiences, diagnosing registration issues, or selecting tracking technology.
type	skill
category	spatial-computing
status	stable
origin	tibsfox
modified	false
first_seen	"2026-04-12T00:00:00.000Z"
first_path	examples/skills/spatial-computing/augmented-reality-tracking/SKILL.md
superseded_by	null

Class	Mechanism	Precision	Range	Cost	Failure modes
Marker-based	Recognized fiducial (ARTag, ARUCO, QR)	High	Line-of-sight to marker	Low	Occlusion, lighting
Markerless feature	Track natural features (corners, edges)	Medium-high	Feature-rich scenes	Medium	Textureless surfaces
Inertial (IMU)	Gyroscope + accelerometer	Medium (drifts)	Unlimited	Low	Drift over seconds
SLAM	Simultaneous localization and mapping	Medium-high	Unlimited	High	Dynamic scenes, low texture
Outside-in	External cameras watch trackers	Very high	Room-scale	High	Occlusion, range
Inside-out	Cameras on HMD watch environment	High	Unlimited	Medium	Feature loss in empty rooms

name	augmented-reality-tracking
description	Tracking and registration fundamentals for augmented reality. Covers the six classes of tracking (marker, markerless, inertial, SLAM, outside-in, inside-out), registration error sources (calibration, latency, drift, distortion), the Azuma definition of AR, display configurations (optical see-through, video see-through, projection, handheld), and the interaction between tracking precision and user perception of "being there." Use when designing AR experiences, diagnosing registration issues, or selecting tracking technology.
type	skill
category	spatial-computing
status	stable
origin	tibsfox
modified	false
first_seen	"2026-04-12T00:00:00.000Z"
first_path	examples/skills/spatial-computing/augmented-reality-tracking/SKILL.md
superseded_by	null

Class	Mechanism	Precision	Range	Cost	Failure modes
Marker-based	Recognized fiducial (ARTag, ARUCO, QR)	High	Line-of-sight to marker	Low	Occlusion, lighting
Markerless feature	Track natural features (corners, edges)	Medium-high	Feature-rich scenes	Medium	Textureless surfaces
Inertial (IMU)	Gyroscope + accelerometer	Medium (drifts)	Unlimited	Low	Drift over seconds
SLAM	Simultaneous localization and mapping	Medium-high	Unlimited	High	Dynamic scenes, low texture
Outside-in	External cameras watch trackers	Very high	Room-scale	High	Occlusion, range
Inside-out	Cameras on HMD watch environment	High	Unlimited	Medium	Feature loss in empty rooms

Mistake	Why it fails	Fix
Ignoring latency budget	Content lags head motion, user notices	Target 20ms or below for HMD
Testing only in ideal lighting	SLAM fails in real use	Test in diverse lighting
Assuming markers work everywhere	Occlusion, distance, angle fail them	Add markerless fallback
Not calibrating per user	IPD and eye offset vary	Provide a calibration step
Placing content mid-air without anchors	Nothing to register against, drift obvious	Anchor to natural fiducials
Ignoring occlusion mismatch in OST	Ghostly content, breaks illusion	Design for the display type

augmented-reality-tracking

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