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/agent-research-patterns-synthesis.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/agent-research-patterns-synthesis)<a href="https://agentmods.dev/commands/glassbead-tc/widescreen-research/agent-research-patterns-synthesis"><img src="https://agentmods.dev/badge/commands/glassbead-tc/widescreen-research/agent-research-patterns-synthesis/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/agent-research-patterns-synthesis"><img src="https://agentmods.dev/badge/commands/glassbead-tc/widescreen-research/agent-research-patterns-synthesis.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 | $0.04462 |
| Opus 5 | $0.00000 | $0.02231 |
| Sonnet 5 | $0.00000 | $0.00892 |
| Haiku 4.5 | $0.00000 | $0.00446 |
Grade A, and why
agent-research-patterns-synthesis 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 — 676 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Research Patterns: Qualitative Advantages Beyond Throughput
Synthesis Date: September 29, 2025 Based on: Academic literature analysis (Aug-Sep 2025) Sources: 8 papers from arXiv + industry implementations
Why Agents? (Beyond "It's Faster")
Qualitative Advantages from Literature
From recent research, agents provide fundamentally different research capabilities, not just efficiency gains:
1. Novel Connection Discovery (SciAgents, 2024)
Finding: Multi-agent graph reasoning discovered "previously unseen connections" in scientific domains that humans considered unrelated.
Mechanism:
- Agents traverse ontological knowledge graphs in non-human patterns
- Find interdisciplinary bridges by following semantic similarity across domains
- No preconception about what fields "should" be related
Example from paper:
- Discovered bio-inspired material properties by connecting biology → chemistry → physics
- Humans focused within disciplines; agents crossed boundaries naturally
Why agents excel: No disciplinary tunnel vision
2. Iterative Collaborative Improvement (AgentRxiv, 2025)
Finding: Agents sharing research through a preprint server achieved 11.4% better results than isolated agents, and 13.7% improvement with multiple collaborating labs.
Mechanism:
- Agent A publishes finding to shared server
- Agent B reads A's work, builds on it, publishes refinement
- Agent C synthesizes A+B into novel approach
- Compounding knowledge gain
Key insight: "Progress in scientific discovery is rarely the result of a single 'Eureka' moment, but is rather the product of hundreds of scientists incrementally working together"
Why agents excel:
- No ego barrier to building on others' work
- Can read and synthesize hundreds of prior works instantly
- Natural compounding of incremental improvements
3. Hypothesis Generation & Refinement (Agentic Science Survey, 2025)
Finding: Agentic systems show capabilities in "hypothesis generation, experimental design, execution, analysis, and iterative refinement -- behaviors once regarded as uniquely human"
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 · 676 lines · 0 tokens per session scan A dee011f76947
agent-research-patterns-synthesis 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 4,462 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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