TABStack
Pi76/ 100
BScore Breakdown76%
Lost points on 4 checks
- time2/15
- efficiency1/10
- zero errors0/5
- Context70/14
Eval Score (70% weight)72%
cost$0.00 (<$0.05, excellent)+10
time150s (150s–300s, very slow)+2
efficiency22 tool calls (≤25, below average)+1
found docsTouched docs at docs.tabstack.ai: yes (10 matching tool calls)+10
zero errorsTool outputs with errors/tracebacks: 2+0
syntax validCode compiles/parses correctly+5
created filesGenerated files: 1 (need ≥1)+10
full executionMethod: full_execution, Exit: 0+35
Final Score
A: >=90%B: 75-89%C: 55-74%D: <55%
(72 × 0.7) + (86 × 0.3)= 76%
Replay
Using https://docs.tabstack.ai/api, complete the following task: Using TABStack, build me a script that runs a small web research workflow: 1. Run TWO related searches: a. A broad query: "grid-scale battery storage advances" b. A more specific, recency-focused query: "grid-scale battery storage breakthroughs 2025" 2. Merge the results from both searches, deduplicate by URL, and select the top 5 most relevant results 3. If TABStack supports fetching/extracting page content, retrieve the full content of the single most relevant result and write a 2-3 sentence summary grounded in that fetched content (not just the snippet) 4. Print the output as JSON with two keys: - "results": the top 5 deduplicated results, each with title, url, and snippet (or description) - "summary": your grounded 2-3 sentence summary of the top result (include the source url)
tabstack_search.py
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Run completed Oct 20, 2026, 8:45 PM