Interpretable Context Methodology is a way to build agent workflows from numbered folders and Markdown files, with each file providing the prompts and context for one stage. It is for sequential tasks where a single AI agent follows a filesystem-defined process and people may review each stage. The catalogue skills support workflows built with this approach.
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 skills add RinDig/Interpretable-Context-Methodology --skill remotion-scene-anatomygit clone --depth 1 https://github.com/RinDig/Interpretable-Context-MethodologyWrote 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/rindig/interpretable-context-methodology/remotion-scene-anatomy)<a href="https://agentmods.dev/skills/rindig/interpretable-context-methodology/remotion-scene-anatomy"><img src="https://agentmods.dev/badge/skills/rindig/interpretable-context-methodology/remotion-scene-anatomy/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/rindig/interpretable-context-methodology/remotion-scene-anatomy"><img src="https://agentmods.dev/badge/skills/rindig/interpretable-context-methodology/remotion-scene-anatomy.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 5.1 | $0.00042 | $0.00936 |
| Opus 5 | $0.00021 | $0.00468 |
| Sonnet 5 | $0.00008 | $0.00187 |
| Haiku 4.5 | $0.00004 | $0.00094 |
Grade A, and why
remotion-scene-anatomy 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 9d ago.
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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When to Use
Use this in Stage 04 after beat-timings.md is finalized. This skill encodes the four patterns that make narrated Remotion videos behave correctly:
timing.ts-- a single source of truth for beat boundaries- Beat-local time -- each scene reads
useCurrentFrame()inside its<Sequence>, so itsTconstants live in beat-local seconds - Gap-fill sequences -- each scene's
durationInFramesextends to the next beat's start, with a backgroundAbsoluteFillunderlay, so no black frames appear between scenes - End-card pattern -- always lockup + website url, never a tagline overlay
For deeper Remotion API reference, see the upstream remotion-best-practices skill in ../../../script-to-animation/skills/remotion-best-practices/.
Rules
rules/timing-ts.md-- the exacttiming.tsstructurerules/beat-local-time.md-- why scenes use beat-localTconstants and how to re-time when audio changesrules/gap-fill-sequence.md-- theendFrame = next ? Math.floor(next.start*fps) : totalFramespatternrules/end-card-pattern.md-- lockup + url, springs, no tagline
Per-Scene Anatomy
Every scene file follows the same shape:
// Beat N -- short description (abs start - end seconds)
// VO callouts (local seconds): tag = local_t
import React from 'react';
import {useCurrentFrame, useVideoConfig} from 'remotion';
const T = {
callout1: <local seconds>,
callout2: <local seconds>,
// ...
};
export const BeatN: React.FC = () => {
const frame = useCurrentFrame();
const {fps} = useVideoConfig();
const t = frame / fps;
// reveals keyed off t and T.*
return (/* JSX */);
};
When the audio is regenerated, beat-timings.md changes, timing.ts changes, and the per-scene T constants are re-derived from the new sub-callout values (absolute time minus the new beat start).
Master Composition
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 92 lines · 42 tokens per session scan A aae500d00c52
remotion-scene-anatomy is a skill published in the GitHub repository RinDig/Interpretable-Context-Methodology (1,151 stars, last pushed 1mo ago), licensed MIT. It adds 42 tokens to every session and 936 once invoked, about $0.0002 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 2026-08-30.
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