From agentic-skills
An open-ended pattern where agents autonomously conduct research, generate hypotheses, and explore solution spaces without a predefined path. Use when user asks to "add exploration to my agent", "balance exploration and exploitation", or mentions curiosity-driven, search strategies, or novelty seeking.
npx claudepluginhub lauraflorentin/skills-marketplace --plugin agentic-skillsThis skill uses the workspace's default tool permissions.
In the Exploration pattern, the goal is not to execute a known task, but to *find* new information or solutions. Agents act as scientists or researchers: they formulate a hypothesis, test it (by searching, coding, or simulating), analyze the results, and iterate. This allows for genuine novelty and discovery.
Guides Next.js Cache Components and Partial Prerendering (PPR) with cacheComponents enabled. Implements 'use cache', cacheLife(), cacheTag(), revalidateTag(), static/dynamic optimization, and cache debugging.
Migrates code, prompts, and API calls from Claude Sonnet 4.0/4.5 or Opus 4.1 to Opus 4.5, updating model strings on Anthropic, AWS, GCP, Azure platforms.
Generates or updates index.md listing all files and subdirectories in a target folder with 3-10 word descriptions from file contents. Use for indexing documentation directories.
In the Exploration pattern, the goal is not to execute a known task, but to find new information or solutions. Agents act as scientists or researchers: they formulate a hypothesis, test it (by searching, coding, or simulating), analyze the results, and iterate. This allows for genuine novelty and discovery.
def exploration_loop(topic):
knowledge_base = []
# Phase 1: Hypothesis Generation
hypotheses = brainstorming_agent.run(f"Generate ideas about {topic}")
for hypothesis in hypotheses:
# Phase 2: Experiment / Research
# Agent autonomously decides search queries or code to run
evidence = researcher_agent.run(f"Test this hypothesis: {hypothesis}")
# Phase 3: Analysis
conclusion = analyst_agent.run(
prompt="Does the evidence support the hypothesis?",
input={"hypothesis": hypothesis, "evidence": evidence}
)
knowledge_base.append(conclusion)
# Phase 4: Synthesis
return writer_agent.run("Write a report based on these conclusions", input=knowledge_base)
Input: "Survey the latest research on LLM memory architectures and identify the top 3 open problems."
What the agent does:
Output: A 2-page research brief with identified gaps and recommended next steps.
Input: "Explore competitor pricing pages and build a comparison matrix."
Output: A structured table comparing plans, features, and pricing across 8 competitors, with a gap analysis highlighting differentiation opportunities.
| Problem | Cause | Fix |
|---|---|---|
| Agent explores indefinitely | Missing stopping criteria | Set explicit iteration limit (max_iterations=10) and convergence condition |
| All hypotheses converge to same answer | Insufficient diversity in brainstorming | Add temperature variation or inject contrarian perspectives |
| Research results are outdated | Search tool returning cached content | Specify date filter (after:2024) in search queries |
| Agent loses track of prior findings | Context window exceeded | Use an external knowledge store; summarize findings at each iteration |
| Exploration produces no actionable output | No synthesis step | Always include a final writer_agent pass to consolidate findings |