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)<a href="https://agentmods.dev/commands/galbaz1/video-research-mcp/research"><img src="https://agentmods.dev/badge/commands/galbaz1/video-research-mcp/research/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"><img src="https://agentmods.dev/badge/commands/galbaz1/video-research-mcp/research.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 | $0.00009 | $0.01290 |
| Opus 5 | $0.00005 | $0.00645 |
| Sonnet 5 | $0.00002 | $0.00258 |
| Haiku 4.5 | $0.00001 | $0.00129 |
Grade B, and why
research scanned grade B with 1 finding 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.
Reads agent configuration directoriesmediumAgent snooping
.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.
a. Determine the memory directory: find the `.claude/` project memory path for the current working directory. Use `Glob` on `~/.claude/projects/*/memory/` to find the active project memory path if needed. How it starts
The opening of the file, as written. The whole thing — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research: $ARGUMENTS
For web-grounded deep research with the Gemini Deep Research Agent ($2-5/task, 10-20 min), use
/gr:research-deepinstead. This command uses offline analysis (free, instant).
Run a multi-phase deep research analysis with progressive memory saving and automatic evidence-network visualization.
Phase 1: Research (run BOTH in parallel)
These two calls are independent — they hit different models with separate quotas. Issue both tool calls in a single turn:
web_search(query="$ARGUMENTS")— uses Gemini Flash with Google Search groundingresearch_deep(topic="$ARGUMENTS", scope="moderate", thinking_level="high")— uses Gemini Pro
Do NOT wait for one to finish before starting the other.
Phase 2: Present & Save Initial Results
-
Present findings organized by evidence tier:
- CONFIRMED — Multiple independent sources agree
- STRONG INDICATOR — Credible evidence with minor gaps
- INFERENCE — Reasonable conclusion from indirect evidence
- SPECULATION — Plausible but unverified
- UNKNOWN — Insufficient evidence
-
Highlight open questions and methodology critique
-
Immediately save initial results: a. Determine the memory directory: find the
.claude/project memory path for the current working directory. UseGlobon~/.claude/projects/*/memory/to find the active project memory path if needed. b. Generate a slug from the topic: lowercase, hyphens, no special chars, max 50 chars (e.g., "impact of mcp on ai agents" →impact-of-mcp-on-ai-agents) c. UseWriteto save at<memory-dir>/gr/research/<slug>/analysis.md:
---
source: web research
topic: "$ARGUMENTS"
analyzed: <ISO 8601 timestamp>
updated: <ISO 8601 timestamp>
scope: moderate
findings_count: <number>
evidence_tiers:
confirmed: <count>
strong_indicator: <count>
inference: <count>
speculation: <count>
unknown: <count>
---
# $ARGUMENTS
> Researched on <YYYY-MM-DD HH:MM>
> Scope: moderate
## Executive Summary <!-- <YYYY-MM-DD HH:MM> -->
<2-3 sentence summary>
## Findings <!-- <YYYY-MM-DD HH:MM> -->
### CONFIRMED
1. **<Finding>** — <evidence summary>
### STRONG INDICATOR
2. **<Finding>** — <evidence summary>
### INFERENCE
3. **<Finding>** — <evidence summary>
### SPECULATION
4. **<Finding>** — <evidence summary>
### UNKNOWN
5. **<Finding>** — <evidence summary>
## Sources <!-- <YYYY-MM-DD HH:MM> -->
<Cited sources with URLs where available>
## Open Questions <!-- <YYYY-MM-DD HH:MM> -->
<Unresolved questions for future investigation>
## Methodology Critique <!-- <YYYY-MM-DD HH:MM> -->
<Assessment of research methodology limitations>
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 · 150 lines · 9 tokens per session scan B 6fab7f593056
research is a command published in the GitHub repository Galbaz1/video-research-mcp (23 stars, last pushed 1mo ago), licensed MIT. It adds 9 tokens to every session and 1,290 once invoked, about $0.0000 per session on Opus 5. A static security scan graded it B with 1 finding (reads agent configuration directories). 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.
eg-new-feature
Build a new feature using the elephant/goldfish workflow — design doc, goldfish design check, implement, review, validate.
eg-fix-bug
Fix a bug using the elephant/goldfish workflow — problem doc, goldfish diagnosis check, failing test, fix, review, validate.
dock-chat
Dock the full conversation to Telegram — drive Claude from your phone.
mem-last
Prints latest memory cards for the current project.
mem-prune
Removes old low-importance memories.