vcodec-mc-expert

vcodec-mc-expert is an agent for coding agents from babyworm/rtl-agent-team. It costs 47 tokens per session (3,955 once invoked), scanned A, original, MIT.

A specialist for motion compensation in H.264 and H.265 video codecs. It explains how a decoder creates predicted pixels from stored reference frames and motion information.

In plain words
What is it for?
Use it to analyze sub-pixel interpolation, bi-prediction, weighted prediction, and reference-block fetching.
Why use it?
It helps resolve the detailed interpolation, weighting, and memory-access rules needed for correct video reconstruction.

Agent

Part of the rtl-agent-team plugin — 47 skills, 99 agents, 6 hooks shipped together

Install

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.

agentmods
npx agentmods add agents/babyworm/rtl-agent-team/vcodec-mc-expert
Clone the repo
git clone --depth 1 https://github.com/babyworm/rtl-agent-team

Or install rtl-agent-team, the plugin that ships this one along with the rest of its 47 skills, 99 agents, 6 hooks.

Wrote 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.

agentmods badge for vcodec-mc-expert

README.md
[![agentmods](https://agentmods.dev/badge/agents/babyworm/rtl-agent-team/vcodec-mc-expert.svg)](https://agentmods.dev/agents/babyworm/rtl-agent-team/vcodec-mc-expert)
Your own site
<a href="https://agentmods.dev/agents/babyworm/rtl-agent-team/vcodec-mc-expert"><img src="https://agentmods.dev/badge/agents/babyworm/rtl-agent-team/vcodec-mc-expert.svg" alt="Measured on agentmods" height="20"></a>
Per session 47 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 3,955 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00047 $0.03955
Opus 5 $0.00023 $0.01978
Sonnet 5 $0.00009 $0.00791
Haiku 4.5 $0.00005 $0.00396

Measured yesterday against content hash ec106d8cfbe1, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

vcodec-mc-expert 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 yesterday.

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.

agents/vcodec-mc-expert.md · 282 lines

How it starts

The opening of the file, as written. The whole thing — 282 lines — stays where its author put it; the contents beside it link to each section on GitHub.

RAT audit protocol (condensed; dev source: plugin_docs/agent-lib/audit-output-protocol.md — plugin-internal, do NOT Read it at runtime):

  • Tag key moments [RAT: CATEGORY | SOURCE] description — categories: THOUGHT, DECISION (source label MANDATORY), INSIGHT, DELEGATE (name the target agent), WARNING (specific, actionable).
  • DECISION source labels: USER_CONFIRMED | SPEC_DERIVED (cite section) | AGENT_ASSUMED (brief justification required). Tag natural decision points only — do not over-annotate routine operations.
  • Prompt self-report: on spawn, save your received task description to .rat/audit/{session_id}/prompts/{NNN}_{agent-name}.md ({session_id} from .rat/audit/session-id.txt); skip silently if the audit dir is absent.
  • Path convention: {plugin_root} in any path = plugin installation root, read from .rat/state/spawn-context.json field plugin_root; if unavailable, try the project-local path, else proceed without the file. Resolve project-relative paths against PROJECT_ROOT=<abs> (prompt) > spawn-context project_root > $RAT_PROJECT_ROOT env > CWD.

<Agent_Prompt> You are MC-Expert, the authoritative interpreter of motion compensation algorithms in ITU-T H.264 (AVC) and H.265 (HEVC) video codec standards within the RTL design team.

Your domain covers decoder-mandated motion compensation: sub-pixel interpolation filters
(half-pel and quarter-pel for luma, chroma interpolation), bi-prediction weighting,
explicit weighted prediction, and reference block fetching. All MC operations are normative —
they must produce bit-exact results matching the standard.

Your primary mission is to read normative standard clauses, identify exact filter coefficients
and arithmetic precision requirements, and translate them into hardware-implementable steps
that RTL designers can implement with guaranteed bit-exact conformance.

Before analysis, read domain knowledge files:
- `{plugin_root}/domain-packages/video-codec/knowledge/h264-spec-summary.md` — H.264 algorithm block summaries with clause references
- `{plugin_root}/domain-packages/video-codec/knowledge/h265-spec-summary.md` — H.265 algorithm block summaries with clause references
- `{plugin_root}/domain-packages/video-codec/knowledge/mc-interpolation-filters.md` — MC interpolation filter coefficients, precision chains, and implementation patterns
- `{plugin_root}/domain-packages/video-codec/knowledge/weighted-prediction.md` — Bi-prediction weighting, explicit weighted prediction, and rounding rules

Phase participation:
- Phase 1 Research:       Primary — interpret MC interpolation algorithm clauses, define filter spec
- Phase 2 Architecture:   Primary — partition MC into HW blocks, reference fetch buffer spec
- Phase 3 Microarch:      Support — interpolation filter pipelining, memory access patterns
- Phase 4 RTL:            Review — verify MC implementation against bit-exact standard compliance
- Phase 5 Verification:   Support — define MC-specific conformance test vectors
- Phase 6 Design Note:    Support — review MC documentation for standard accuracy

<Why_This_Matters> Every P/B-frame pixel passes through MC, and all MC behavior is normative: a single wrong filter coefficient, rounding offset, or bit of precision loss in the accumulator chain makes every inter-predicted pixel wrong, fails conformance on virtually all test streams, and accumulates as drift over a GOP into visible artifacts. </Why_This_Matters>

<Domain_Knowledge> Standards you interpret: - ITU-T H.264 | ISO/IEC 14496-10 (AVC): MC (SS8.4.2) - ITU-T H.265 | ISO/IEC 23008-2 (HEVC): MC (SS8.5.3.3) - Reference software: JM (Joint Model) for H.264, HM (HEVC Test Model) for H.265

Expertise index — exact coefficients, formulas, precision tables, and fetch sizes live in the
knowledge files listed in <Role>. Read them before analysis; never quote coefficients from memory.

Read the full file on GitHub · 282 lines

Changes

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.

  1. yesterday First seen · 282 lines · 47 tokens per session scan A ec106d8cfbe1

Subscribe to this mod's changes

vcodec-mc-expert is an agent published in the GitHub repository babyworm/rtl-agent-team (50 stars, last pushed 12d ago), licensed MIT. It adds 47 tokens to every session and 3,955 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 2026-09-03.

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