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.
git clone --depth 1 https://github.com/Galbaz1/video-research-mcpWrote 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/commands/galbaz1/video-research-mcp/research-doc)<a href="https://agentmods.dev/commands/galbaz1/video-research-mcp/research-doc"><img src="https://agentmods.dev/badge/commands/galbaz1/video-research-mcp/research-doc/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/commands/galbaz1/video-research-mcp/research-doc"><img src="https://agentmods.dev/badge/commands/galbaz1/video-research-mcp/research-doc.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00010 | $0.01101 |
| Opus 5.5 | $0.00004 | $0.00440 |
| Sonnet 5.5 | $0.00002 | $0.00220 |
| Haiku 4.5 | $0.00001 | $0.00110 |
Grade A, and why
research-doc 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 18d 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 — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Document Research: $ARGUMENTS
Run multi-phase document analysis with progressive memory saving. Retain document/page pointers and check decisive claims against the originals; generated citations and tiers are proposals.
Phase 1: Identify Documents
-
Determine the input from "$ARGUMENTS":
- If it's a single file path: use
research_documentwithfile_paths=["<path>"] - If it's multiple paths (comma or space separated): split and pass as
file_pathslist - If it's a directory: use
Globto find all PDFs/text files, then pass asfile_paths - If it starts with
http://orhttps://: pass asurlslist - If arguments include both local files and URLs: pass both
file_pathsandurls
- If it's a single file path: use
-
Determine scope from context:
- Single document, quick question →
scope="quick" - 1-2 documents, standard analysis →
scope="moderate"(default) - 2+ documents, comparative analysis →
scope="deep" - Comprehensive review with methodology critique →
scope="comprehensive"
- Single document, quick question →
-
Call
research_documentwith:instruction: Use $ARGUMENTS context or default to "Analyze these documents comprehensively — extract key findings, assess methodology, identify agreements and contradictions."scope: As determined abovethinking_level: "high"
Phase 2: Present & Save Results
-
Present findings organized by evidence tier:
- CONFIRMED — Checked against the cited original passage/data; distinguish what the document states from independently established truth
- STRONG INDICATOR — Strongly implied by document evidence
- INFERENCE — Reasonable conclusion from document context
- SPECULATION — Extrapolation beyond what documents support
- UNKNOWN — Documents do not address this
-
For multi-document results, highlight:
- Cross-references: Where documents agree or contradict
- Evidence chains: How findings flow across documents
- Methodology critique: Per-document reliability assessment
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.
- 18d ago Changed bdf265ac4a60
- 24d ago Changed · -1 lines 665f578b2fc1
- 1mo ago First seen · 123 lines · 10 tokens per session scan A d7fefc996be0
research-doc is a command published in the GitHub repository Galbaz1/video-research-mcp (23 stars, last pushed yesterday), licensed MIT. It adds 10 tokens to every session and 1,101 once invoked, about $0.0000 per session on Opus 5.5. 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.
Other commands, from other repositories
setup-video-vision
Interactive setup wizard for claude-video-vision — configure backend, whisper, frames, and verify dependencies.
brief-compliance-check
Check a LaTeX coursework submission against the requirements in a supplied PDF assessment brief. Use when verifying format, required sections, word limits, or deliverables before submission. Not for general prose proofreading; use $proofread.
latex-scaffold
Convert an existing Markdown manuscript draft into a buildable LaTeX project with canonical structure and build configuration. Use when prose already exists but no LaTeX source tree does. Not for checking template drift in an existing project; use $latex-template.
noirdoc-redact
Redact a local file via noirdoc, placing the clean copy in .noirdoc/cache/.
eg-fix-bug
Fix a bug using the elephant/goldfish workflow — problem doc, goldfish diagnosis check, failing test, fix, review, validate.
ocr
Extract text from images using AI-powered OCR.