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/spencermarx/open-code-reviewWrote 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/spencermarx/open-code-review/researcher)<a href="https://agentmods.dev/commands/spencermarx/open-code-review/researcher"><img src="https://agentmods.dev/badge/commands/spencermarx/open-code-review/researcher.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 | $0.00293 |
| Opus 5 | $0.00000 | $0.00147 |
| Sonnet 5 | $0.00000 | $0.00059 |
| Haiku 4.5 | $0.00000 | $0.00029 |
Grade A, and why
researcher 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 3d 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 researcher — 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.
What it actually says
SPARC Researcher Mode
Purpose
Deep research with parallel WebSearch/WebFetch and Memory coordination.
Activation
Option 1: Using MCP Tools (Preferred in Claude Code)
mcp__claude-flow__sparc_mode {
mode: "researcher",
task_description: "research AI trends 2024",
options: {
depth: "comprehensive",
sources: ["academic", "industry", "news"]
}
}
Option 2: Using NPX CLI (Fallback when MCP not available)
# Use when running from terminal or MCP tools unavailable
npx claude-flow sparc run researcher "research AI trends 2024"
# For alpha features
npx claude-flow@alpha sparc run researcher "research AI trends 2024"
Option 3: Local Installation
# If claude-flow is installed locally
./claude-flow sparc run researcher "research AI trends 2024"
Core Capabilities
- Information gathering
- Source evaluation
- Trend analysis
- Competitive research
- Technology assessment
Research Methods
- Parallel web searches
- Academic paper analysis
- Industry report synthesis
- Expert opinion gathering
- Data compilation
Memory Integration
- Store research findings
- Build knowledge graphs
- Track information sources
- Cross-reference insights
- Maintain research history
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.
- 3d ago First seen · 55 lines · 0 tokens per session scan A ec6c5c30269d
researcher is a command published in the GitHub repository spencermarx/open-code-review (355 stars, last pushed 1mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 293 tokens. A static security scan graded it A with 0 findings. It is 100% identical to researcher, differing in 0 lines, and is treated as a copy.
Other commands, from other repositories
xray
Multi-dimensional paper audit — spawns 5 parallel sub-agents to check numerical accuracy, terminology consistency, code-paper alignment, citation accuracy, and evaluation integrity.
research
Start or resume an academic research project — idea through literature, methodology, writing, feedback, and publishing.
openehr-explain
One-stop router that explains or looks up any openEHR thing — auto-detects an archetype, a template, an RM/AM/BASE type, an RM structural concept, an ADL idiom, an AQL query or keyword, or a terminology code (replaces /archetype-explain, /template-explain, /type-spec, /rm-structure, /adl-idiom, /terminology).
ckm-search
Search the openEHR Clinical Knowledge Manager (CKM) for archetypes or templates.
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.