Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/generous-corp/pulp/motionnpx skills add Generous-Corp/pulp --skill motiongit clone --depth 1 https://github.com/Generous-Corp/pulpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/generous-corp/pulp/motion)<a href="https://agentmods.dev/skills/generous-corp/pulp/motion"><img src="https://agentmods.dev/badge/skills/generous-corp/pulp/motion.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00212 | $0.09813 |
| Opus 5 | $0.00106 | $0.04907 |
| Sonnet 5 | $0.00042 | $0.01963 |
| Haiku 4.5 | $0.00021 | $0.00981 |
Grade A, and why
motion scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 870 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Motion
Pulp's agent-first motion observability — sample view geometry / scalar values / scroll state over time, emit epsilon-bounded events with monotonic timestamps and burst framing, and route them to log lines + inspector events + JSONL fixtures. You are reading this skill because an agent needs to debug, validate, or reproduce a motion behavior.
When to rope this skill in
Trigger this skill the moment a user (or your own reasoning) describes any
of these symptoms — don't reach for grep or git log, attach a trace:
- "this fade / slide / scale is too fast / too slow / starts late / ends early"
- "the knob value isn't reaching its target" / "what value at frame N?"
- "two elements drift apart during the transition"
- "scroll position jumps when I restore state"
- "imported design's motion doesn't match the source intent"
- "is this animation monotonic / does it overshoot / how long to settle?"
- "which animation is expensive and why?" → Path F (cost attribution)
- "reduced-motion path is broken" → Path B + assert under MotionPolicy
- "I want to scrub through a recorded fixture" → Path E (scrubber)
If the user says any of those, this skill applies. Don't suggest reading source; attach a trace and read the numbers.
Quick decision
Eight paths — pick by what you have, all eight terminate at the same
motion::Coordinator so fixtures, scrubber, cost, reduced-motion, and
provenance work identically across surfaces.
| You have | Path | Tool |
|---|---|---|
| C++ fixture code + a node id + a scalar / geometry of interest | A — In-process trace | motion::Coordinator / MotionInspector fixture APIs; an admitted exact T1 host may inject ControlMotionExecutor |
A ScrollView whose offset / visible rect / content size you need to observe |
A — Runtime trace (scroll) | Trace.scroll_geometry(name, scroll_view, props) — emits contentOffsetX/Y, visibleRect*, contentSize*, scrollableMax*, inset* |
| A captured frame sequence (no app instrumentation available) | B — Visual analysis | tools/motion/visual/analyze_sequence.py |
A previously recorded .motion.jsonl fixture |
C — Replay + assert | motion::replay_fixture + motion::assert_matches |
| An interaction that drives the suspect motion | D — Input record + replay | motion::make_input_recorder + motion::replay_inputs |
| A fixture available to C++ host/test code (design review / CI triage) | E — Timeline scrubber | Direct MotionScrubber fixture load; canonical T1 scrub/play/pause is capped at 4,096 emitted events |
| "Which animation is expensive and why?" | F — Cost attribution | CostAttributor + make_render_cost_probe; canonical T1 returns at most 64 redacted finite samples |
| SwiftUI / UIKit / AppKit / iOS / macOS / AUv3 host code path | G — Swift native | View.pulpMotionTrace { Trace.* } / PulpMotionGeometryProbe |
Jetpack Compose or Android View code path |
H — Android native | Modifier.pulpMotionGeometry { +Trace.* } / View.pulpMotionTrace |
| Imported design + intent doc (e.g. "fade in 350 ms ease-out") | Both A + C | Record a fixture from the import, assert timing / monotonicity |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- today First seen · 870 lines · 212 tokens per session scan A 8d51ea60765f
motion is a skill published in the GitHub repository Generous-Corp/pulp (16 stars, last pushed today), licensed MIT. It adds 212 tokens to every session and 9,813 once invoked, about $0.0011 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-04.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
chronicle
Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…
imagegen
Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…