Skill: fuzzy-tool-retrieval

[Case 2]

• Initial Environment: A benchmark environment contains web-search and fetch tools plus a broad set of unrelated productivity tools. The prompt asks a quantitative question that cannot be answered from a single search result, but it also does not explicitly say 'use search'.
• Real Question: Assuming all research articles in Nature 2020 relied on statistical significance with an average p-value of 0.04, calculate how many papers would incorrectly claim significance, rounding up.
• Real Trajectory: The agent identifies that it first needs a retrieval tool to obtain the relevant count context and then a calculation step to produce the final number, rather than choosing unrelated file, email, or terminal tools.
• Real Answer: 41
• Why this demonstrates the capability: This case demonstrates fuzzy tool retrieval because the orchestration challenge lies in mapping an ordinary-language question to the correct retrieval-and-computation tool family.

[Case 3]

• Initial Environment: A large-scale federation of agents or microservices is active. Each worker maintains a capability profile tracking policy compliance (e.g., HIPAA labels), speed latency, and cost per execution alongside functional descriptions.
• Real Question: Process this confidential clinical record and extract demographic stats. Because it's a huge batch processing job that runs all night, prioritize extreme cost efficiency over latency, but absolute security is non-negotiable.
• Real Trajectory: The orchestrator evaluates the task against worker capability spaces. It finds three agents semantic matching 'clinical data extraction'. It checks the capability parameters: an ultra-fast high-tier model is dropped due to cost metrics; an insecure cheap node lacks the HIPAA flag. The payload is successfully routed to the specific secure, delayed-processing worker.
• Real Answer: Task assigned to the High-Security, High-Latency regional worker resulting in safe, cost-effective extraction.
• Why this demonstrates the capability: This captures multi-dimensional fuzzy retrieval and abstract SLO calibration. The orchestrator maps qualitative requests ('all night batch', 'secure') into semantic filtering parameters (Latency > 50ms, HIPAA=True) to discard technically capable but non-compliant tool choices.

Pipeline Execution Instructions

To synthesize data for this capability, you must strictly follow a 3-phase pipeline. Do not hallucinate steps. Read the corresponding reference file for each phase sequentially:

1. Phase 1: Environment Exploration Read the exploration guidelines to discover raw knowledge seeds: references/EXPLORATION.md

2. Phase 2: Trajectory Selection Once Phase 1 is complete, read the selection criteria to evaluate the trajectory: references/SELECTION.md

3. Phase 3: Data Synthesis Once a trajectory passes Phase 2, read the synthesis instructions to generate the final data: references/SYNTHESIS.md