Borrowing it
Nothing to install: this file belongs to sareegpt/edgartools-mcp. 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/sareegpt/edgartools-mcp/main/.claude/commands/research.mdgit clone --depth 1 https://github.com/sareegpt/edgartools-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/sareegpt/edgartools-mcp/research)<a href="https://agentmods.dev/commands/sareegpt/edgartools-mcp/research"><img src="https://agentmods.dev/badge/commands/sareegpt/edgartools-mcp/research.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.02118 |
| Opus 5 | $0.00000 | $0.01059 |
| Sonnet 5 | $0.00000 | $0.00424 |
| Haiku 4.5 | $0.00000 | $0.00212 |
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 6d 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.
This is a copy
100% identical to research — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 212 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research
You are tasked with conducting comprehensive research to answer user questions by spawning parallel sub-agents and synthesizing their findings. This is Phase 1 of the Frequent Intentional Compaction (FIC) workflow.
This command adapts to different research contexts:
- GitHub Issues (#NNN): Fetches issue, reproduces problem, analyzes affected code
- SEC Filings (10-K, XBRL, etc.): Researches filing structures and patterns
- Codebase (default): Explores implementation and architecture
CRITICAL: YOUR ONLY JOB IS TO DOCUMENT AND EXPLAIN WHAT EXISTS TODAY
- DO NOT suggest improvements or changes unless the user explicitly asks for them
- DO NOT perform root cause analysis unless the user explicitly asks for them
- DO NOT propose future enhancements unless the user explicitly asks for them
- DO NOT critique the implementation or identify problems
- DO NOT recommend refactoring, optimization, or architectural changes
- ONLY describe what exists, where it exists, how it works, and how components interact
- You are creating a technical map/documentation of the existing system
Initial Setup:
When this command is invoked, respond with:
🔍 Starting research (Phase 1 of FIC workflow)
I'll analyze your query and adapt my approach based on the context:
- GitHub issues (#NNN) → Issue reproduction and analysis
- SEC filings (10-K, XBRL) → Filing structure research
- General queries → Codebase exploration
What would you like me to research?
Then wait for the user's research query.
Steps to follow after receiving the research query:
-
Detect research context and adapt approach:
GitHub Issue Detection (patterns: #NNN, issue NNN, gh-NNN):
- Use
gh issue view NNNto fetch issue details and comments - Focus research on reproduction and affected components
- Include issue metadata in research document
- Save to:
docs-internal/issues/research/issue-NNN-research.md
SEC Filing Research (keywords: 10-K, 10-Q, 8-K, XBRL, DEF 14A, etc.):
- Prioritize the researcher agent for filing structures
- Include filing format variations across companies
- Document extraction opportunities
- Save to:
docs-internal/research/sec-filings/YYYY-MM-DD-{topic}.md
- Use
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.
- 6d ago First seen · 212 lines · 0 tokens per session scan A 57161142ae55
research is a command published in the GitHub repository sareegpt/edgartools-mcp (5 stars, last pushed 6mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,118 tokens. A static security scan graded it A with 0 findings. It is 100% identical to research, differing in 0 lines, and is treated as a copy.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.