nimble agent — reference

Pre-built agents for specific sites. Always faster and more reliable than manual extraction — use them whenever a matching agent exists (see Step 0 in SKILL.md).

Table of Contents

• 1. List agents
• 2. Get agent details (schema)
• 3. Run agent (sync)
• 4. Run agent (async)
• Response shapes
• Known agents — baked-in table
• Agent gallery
• Agent memory — save new agents

---

1. List agents

Parameters:

Parameter	CLI flag	Type	Default	Description
limit	--limit	int	—	Results per page
offset	--offset	int	—	Pagination offset
search	--search	string	—	Search agents by domain, vertical, or name keyword
managed_by	--managed-by	string	—	Filter by attribution (e.g. nimble)
privacy	--privacy	string	—	Filter by privacy level

CLI:

# All agents (broad lookup)
nimble agent list --limit 100

# Targeted search by domain or vertical (preferred when domain is known)
nimble agent list --limit 100 --search "amazon"
nimble agent list --limit 100 --search "ecommerce"
nimble agent list --limit 100 --search "jobs"

Python SDK:

from nimble_python import Nimble
nimble = Nimble(api_key=os.environ["NIMBLE_API_KEY"])

agents = nimble.agent.list()

Response fields per agent: name, display_name, description, vertical, entity_type, domain, is_public, managed_by

---

2. Get agent details (schema)

Parameters:

Parameter	CLI flag	Type	Description
template_name	--template-name	string	Agent name (required)

CLI:

nimble agent get --template-name amazon_pdp

Python SDK:

agent = nimble.agent.get(template_name="amazon_pdp")

Response: same as list item + output_schema — JSON schema mapping field names to {type, description}.

---

3. Run agent (sync)

Parameters:

Parameter	CLI flag	Type	Default	Description
agent	--agent	string	required	Agent name
params	--params	JSON	required	Agent input parameters
localization	--localization	bool	false	Enable zip_code/store_id localization (agent-dependent)

CLI:

nimble agent run --agent amazon_pdp --params '{"asin": "B0CHWRXH8B"}'

Python SDK:

resp = nimble.agent.run(agent="amazon_pdp", params={"asin": "B0CHWRXH8B"})
parsing = resp.data.parsing  # dict for PDP agents, list for SERP agents

Response fields: task_id, status (success/failed), status_code, data.parsing, data.html, metadata.query_duration, metadata.agent

SERP parsing items are typed Pydantic objects — call .model_dump() before json.dumps() or ** spread. PDP parsing is a plain dict.

---

4. Run agent (async)

Parameters:

Parameter	CLI flag	Type	Default	Description
agent	--agent	string	required	Agent name
params	--params	JSON	required	Agent input parameters
localization	--localization	bool	false	Enable localization
callback_url	--callback-url	string	—	POST callback when task completes
storage_type	--storage-type	string	—	s3 or gs
storage_url	--storage-url	string	—	Destination: s3://bucket/prefix/
compress	--storage-compress	bool	false	Gzip the stored output
custom_name	--storage-object-name	string	—	Custom filename instead of task_id

CLI:

nimble agent run-async --agent amazon_pdp --params '{"asin": "B0CHWRXH8B"}' \
  --callback-url "https://your.server/callback"

Python SDK:

resp = await nimble.agent.run_async(agent="amazon_pdp", params={"asin": "B0CHWRXH8B"})
task_id = resp.task["id"]   # resp.task is a plain dict

Task states: pending → success or error — poll and fetch results via nimble-tasks reference.

---

Response shapes

Agent type	data.parsing shape	Notes
PDP (product/profile/detail)	flat dict	Access with .get("field")
SERP / list	array of objects	Iterate items; call .model_dump() in SDK
Google Search	{"entities": {"OrganicResult": [...], ...}}	Nested entities dict

---

Known agents — baked-in table

Note: Agent names containing date and random suffixes (e.g. indeed_search_2026_02_23_vlgtrsgu) are Nimble-managed and may be updated. Always run nimble agent list --limit 100 to confirm current names before use.

E-commerce — US

Site	Agent	Key param
Amazon product page	amazon_pdp	asin
Amazon search	amazon_serp	keyword
Amazon best sellers	amazon_best_sellers	—
Amazon category	amazon_plp	(see schema)
Walmart product	walmart_pdp	product_id
Walmart search	walmart_serp	keyword
Target product	target_pdp	tcin
Target search	target_serp	query
Best Buy product	best_buy_pdp	product_id
Home Depot product	homedepot_pdp	(see schema)
Home Depot search	homedepot_serp	(see schema)
Sam's Club product	sams_club_pdp	(see schema)
Sam's Club search	sams_club_plp	(see schema)
eBay search	ebay_search_2026_02_23_pbgj8oft	(see schema)
ASOS product	asos_pdp	(see schema)
ASOS search	asos_serp	(see schema)
Kroger product	kroger_pdp	(see schema)
Kroger search	kroger_serp	(see schema)
Foot Locker product	footlocker_pdp	(see schema)
Staples product	staples_pdp	(see schema)
Staples search	staples_serp	(see schema)
Office Depot product	office_depot_pdp	(see schema)
B&H search	b_and_h_serp	(see schema)
Slickdeals	slickdeals_pdp	(see schema)

Food delivery

Site	Agent
DoorDash restaurant	doordash_pdp
DoorDash search	doordash_serp
Uber Eats restaurant	uber_eats_pdp
Uber Eats search	uber_eats_serp

Real estate

Site	Agent	Key params
Zillow listings	zillow_plp	zip_code, listing_type (sales/rentals/sold)
Zillow property	zillow_pdp	(see schema)
Rightmove search	rightmove_search_2026_02_23_pxo1ccrm	(see schema)

Jobs

Site	Agent	Key params
Indeed search	indeed_search_2026_02_23_vlgtrsgu	location, search_term
ZipRecruiter	ziprecruiter_search_2026_02_23_8rtda7lg	(see schema)

Search & maps

Site	Agent	Key param
Google search	google_search	query
Google Maps search	google_maps_search	query
Google Maps reviews	google_maps_reviews	place_id
Yelp search	yelp_serp	search_query, location (optional)

News & finance

Site	Agent
BBC article	bbc_info_2026_02_23_wexv71ke
BBC search	bbc_search_2026_02_23_t0gj94t2
The Guardian search	guardian_search_2026_02_23_nair7e5i
NYTimes search	nytimes_search_2026_02_23_4zleml8l
Bloomberg search	bloomberg_search_2026_02_23_a9u4p1tv
Yahoo Finance	yahoo_finance_info_2026_02_23_fl3ij8ps
MarketWatch	marketwatch_info_2026_02_23_zpwkys0h
Morningstar	morningstar_search_2026_02_23_zicq0zdj
Polymarket	polymarket_prediction_data_2026_02_24_9zhwkle8

Social media

Site	Agent
Instagram post	instagram_post
Instagram profile	instagram_profile_by_account
Instagram reel	instagram_reel
TikTok account	tiktok_account
TikTok video	tiktok_video_page
TikTok Shop product	tiktok_shop_pdp
Facebook page	facebook_page
Facebook profile	facebook_profile_about_section
YouTube Shorts	youtube_shorts
Pinterest search	pinterest_search_2026_02_23_kxzd5awh
Quora topic	quora_info_2026_02_23_99baxvhr

Travel & LLM platforms

Site	Agent
Skyscanner flights	skyscanner_flights
ChatGPT	chatgpt
Gemini	gemini
Grok	grok
Perplexity	perplexity

---

Agent gallery

open -a "Google Chrome" "https://online.nimbleway.com/pipeline-gallery"
open -a "Google Chrome" "https://online.nimbleway.com/pipeline-gallery/amazon_pdp/overview"

---

Agent memory — save new agents

After a successful run, save to learned/examples.json so Step 0 matches faster next time:

python3 -c "
import json, pathlib, datetime
p = pathlib.Path.home() / '.claude/skills/nimble-web-expert/learned/examples.json'
data = json.loads(p.read_text()) if p.exists() else {'good': [], 'bad': [], 'agents': []}
data.setdefault('agents', [])
new_entry = {
    'agent_name': 'amazon_pdp',
    'tags': ['amazon', 'product', 'ecommerce', 'price', 'asin'],
    'params_example': {'asin': 'B0CHWRXH8B'},
    'last_used': str(datetime.date.today()),
    'notes': 'Returns price, title, rating, availability.'
}
if not any(a['agent_name'] == new_entry['agent_name'] for a in data['agents']):
    data['agents'].append(new_entry)
    p.write_text(json.dumps(data, indent=2))
    print('Saved.')
"