Borrowing it
Nothing to install: this file belongs to glassBead-tc/widescreen-research. 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/glassBead-tc/widescreen-research/main/.claude/commands/research/advanced-retrieval-orchestrator.mdgit clone --depth 1 https://github.com/glassBead-tc/widescreen-researchWrote 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/glassbead-tc/widescreen-research/advanced-retrieval-orchestrator)<a href="https://agentmods.dev/commands/glassbead-tc/widescreen-research/advanced-retrieval-orchestrator"><img src="https://agentmods.dev/badge/commands/glassbead-tc/widescreen-research/advanced-retrieval-orchestrator/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/glassbead-tc/widescreen-research/advanced-retrieval-orchestrator"><img src="https://agentmods.dev/badge/commands/glassbead-tc/widescreen-research/advanced-retrieval-orchestrator.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 | $0.00000 | $0.03399 |
| Opus 5 | $0.00000 | $0.01699 |
| Sonnet 5 | $0.00000 | $0.00680 |
| Haiku 4.5 | $0.00000 | $0.00340 |
Grade A, and why
advanced-retrieval-orchestrator 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 10d 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 — 444 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Advanced Retrieval Orchestrator
Next-Generation Information Retrieval for Complex Development Workflows
Vision
Transform information retrieval from static search to dynamic intelligence orchestration - where multiple specialized retrieval agents collaborate to solve complex information needs that no single tool could handle alone.
Variables
RETRIEVAL_OBJECTIVE: $ARGUMENTS COMPLEXITY_LEVEL: "simple" | "multi_faceted" | "research_grade" | "discovery_mission" COORDINATION_STRATEGY: "parallel_retrieval" | "sequential_refinement" | "adaptive_exploration" VALIDATION_MODE: "speed_optimized" | "accuracy_focused" | "comprehensiveness_priority"
Specialized Retrieval Agents
📊 Structured Data Retrieval Agent
Specialization: Library documentation, APIs, technical references Advanced Capabilities:
- Schema-aware information extraction
- Library-specific documentation retrieval
- Version-specific code examples
- API reference optimization
MCP Arsenal:
context7__resolve-library-id- Resolve package names to library IDscontext7__get-library-docs- Get up-to-date library documentation with topic filtering
🌐 Semantic Web Retrieval Agent
Specialization: Deep web content analysis and intelligent search Advanced Capabilities:
- Content quality assessment and ranking
- Semantic similarity analysis across sources
- Real-time content freshness validation
- Code context retrieval
MCP Arsenal:
exa__web_search_exa- Web search with configurable result countsexa__get_code_context_exa- Code-specific context retrieval (libraries, SDKs, APIs)firecrawl__firecrawl_scrape- Deep content extraction from URLsfirecrawl__firecrawl_map- Site structure discoveryfirecrawl__firecrawl_crawl- Recursive site crawling
🔬 Academic Research Agent
Specialization: Scientific papers, academic research, technical publications Advanced Capabilities:
- ArXiv paper search and retrieval
- Recent category paper discovery
- Paper metadata and citation extraction
- Research trend analysis
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.
- 10d ago First seen · 444 lines · 0 tokens per session scan A b296b5d0c8d5
advanced-retrieval-orchestrator is a command published in the GitHub repository glassBead-tc/widescreen-research (6 stars, last pushed 10mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,399 tokens. 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 2026-08-31.
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checklist
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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.