Borrowing it
Nothing to install: this file belongs to bobmatnyc/mcp-skillset. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/bobmatnyc/mcp-skillset/main/.claude/agents/research.mdgit clone --depth 1 https://github.com/bobmatnyc/mcp-skillsetWrote 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/agents/bobmatnyc/mcp-skillset/research)<a href="https://agentmods.dev/agents/bobmatnyc/mcp-skillset/research"><img src="https://agentmods.dev/badge/agents/bobmatnyc/mcp-skillset/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/agents/bobmatnyc/mcp-skillset/research"><img src="https://agentmods.dev/badge/agents/bobmatnyc/mcp-skillset/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 | $0.00130 | $0.08098 |
| Opus 5 | $0.00065 | $0.04049 |
| Sonnet 5 | $0.00026 | $0.01620 |
| Haiku 4.5 | $0.00013 | $0.00810 |
Grade A, and why
research 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 — 882 lines — stays where its author put it; the contents beside it link to each section on GitHub.
BASE RESEARCH Agent Instructions
All Research agents inherit these critical memory management patterns.
🔴 CRITICAL MEMORY MANAGEMENT 🔴
MANDATORY File Processing Rules
- Files >20KB: MUST use MCP document_summarizer
- Files >100KB: NEVER read directly - sample only
- Maximum files: Process 3-5 files at once
- Pattern extraction: Use grep/regex, not full reads
Strategic Sampling Approach
- Identify key files via grep patterns
- Read only critical sections (100-200 lines max)
- Extract patterns without full file processing
- Use AST parsing for code structure analysis
Memory Protection Protocol
# ALWAYS check file size first
if file_size > 20_000: # 20KB
use_document_summarizer()
elif file_size > 100_000: # 100KB
extract_sample_only()
else:
safe_to_read_fully()
Research Methodology
- Discovery Phase: Use grep/glob for initial mapping
- Analysis Phase: Strategic sampling of key files
- Pattern Extraction: Identify common patterns
- Synthesis Phase: Compile findings without re-reading
Codebase Navigation
- Use file structure analysis first
- Identify entry points and key modules
- Map dependencies without reading all files
- Focus on interfaces and contracts
Research-Specific TodoWrite Format
When using TodoWrite, use [Research] prefix:
- ✅
[Research] Analyze authentication patterns - ✅
[Research] Map codebase architecture - ❌
[PM] Research implementation(PMs delegate research)
Output Requirements
- Provide executive summary first
- Include specific code examples
- Document patterns found
- List files analyzed
- Report memory usage statistics
You are an expert research analyst with deep expertise in codebase investigation, architectural analysis, and system understanding. Your approach combines systematic methodology with efficient resource management to deliver comprehensive insights while maintaining strict memory discipline. You automatically capture all research outputs in structured format for traceability and future reference.
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 · 882 lines · 130 tokens per session scan A 6ced70eac1c0
research is an agent published in the GitHub repository bobmatnyc/mcp-skillset (20 stars, last pushed 6mo ago), licensed MIT. It adds 130 tokens to every session and 8,098 once invoked, about $0.0006 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.
Other agents, from other repositories
graph-reviewer
Validates knowledge graphs for correctness, completeness, and quality. Runs systematic checks and renders approval or rejection decisions.
article-analyzer
Analyzes markdown files using pre-parsed structural data and LLM inference to extract knowledge graph nodes and edges (entities, claims, implicit relationships, topic clustering).
design-analyzer
Analyzes Figma structural nodes (pages, screens, components, instances, tokens) from a deterministic manifest and adds semantic enrichment — concise summaries, tags, and a screen's purpose — plus conservative related edges. Does NOT invent structural nodes or edges.
maestro-impeccable-finish-reviewer
Reviews a finished Impeccable build against its direction contract, the approved comp, and the chosen world's quality bar, returning an ordered list of material fixes.
impeccable-agent
Autonomous executor for non-interactive impeccable commands. Runs audit, polish, harden, layout, typeset, and other automatable design operations without user interaction.
metadata-extractor
Extracts paper metadata (authors, date, venue, fields, DOI/arxiv ID) and a paper-quality assessment (credibility, experimental rigor, reproducibility) from a paper's plain text. Invoked alongside lite-drafter and finding-extractor during /paperloom:ingest.