nimbleway/agent-skills/local-places

Local Places

Location intelligence powered by Nimble Web Search Agents and web data APIs.

User request: $ARGUMENTS

Before running any commands, read references/nimble-playbook.md for Claude Code constraints (no shell state, no &/wait, sub-agent permissions, communication style).

Instructions

Step 0: Preflight

Follow the transport selection + standard preflight from references/nimble-playbook.md — pick CLI or MCP at session start, then run the standard preflight calls (date calc, today, profile, memory index) in parallel.

Also simultaneously:

• mkdir -p ~/.nimble/memory/{reports,local-places/checkpoints}
• Check for existing checkpoints: ls ~/.nimble/memory/local-places/checkpoints/ 2>/dev/null

From the results:

• CLI missing or API key unset -> references/profile-and-onboarding.md, stop
• Tag all nimble CLI calls: nimble --client-source nimble-agent-skills <subcommand>. MCP requests are attributed at the transport level — see references/nimble-playbook.md.
• Profile exists -> note the user's location preferences if any. Determine mode

using smart date windowing from references/nimble-playbook.md:

• Full mode: first run OR last run > 14 days ago
• Quick refresh: last run < 14 days ago (skip social enrichment, reviews only

for new places)

• Same-day repeat: if last_runs.local-places is today, check if a report

already exists at ~/.nimble/memory/reports/local-places-*[today].md. If so, ask: "Already ran today for this area. Run again for fresh data?" Don't silently re-run.

• Skip to Step 1
• No profile -> that's fine. Local places doesn't require onboarding. Proceed to Step 1.

Step 1: Parse Request & Starting Questions

Parse $ARGUMENTS for place type and location. Extract:

If both place type and location are clear from $ARGUMENTS, confirm briefly and proceed: "Searching for coffee shops in Williamsburg, Brooklyn..."

If partial or ambiguous, ask one combined question in plain text:

"What type of places are you looking for, and where? (e.g., 'coffee shops in

Williamsburg' or 'gyms near downtown Austin')"

If the user provided both but you want to scope further, use AskUserQuestion (counts as 1 of max 2 prompts):

How thorough should this search be?

- Quick scan -- top results from Google Maps + Yelp (~20 places)

- Comprehensive -- full discovery + social enrichment + reviews (~50+ places)

- Deep dive with map -- everything above + interactive neighborhood map

Step 2: Location Disambiguation

Before any API calls, resolve the location to avoid wasted searches.

Disambiguation triggers:

• Location name exists in multiple cities/states (e.g., "Williamsburg" = Brooklyn NY

vs. Williamsburg VA)

• Location is a broad area (e.g., "downtown Austin" = multiple neighborhoods)
• Location is informal (e.g., "Soho" = NYC vs. London)

If ambiguous, ask the user (counts toward 2-prompt max):

"There are a few places called Williamsburg. Which one?"

- Williamsburg, Brooklyn, NY

- Williamsburg, VA

- Other -- I'll specify

If unambiguous, infer the full location (city + state/country) and confirm inline: "Searching Williamsburg, Brooklyn, NY..."

After disambiguation, derive the slug for checkpointing and file paths: lowercase, hyphenated, includes city + state/country (e.g., williamsburg-brooklyn-ny).

Step 3: Check for Existing Checkpoint

Follow the Checkpointing & Resume pattern from references/memory-and-distribution.md.

Check: cat ~/.nimble/memory/local-places/checkpoints/{slug}/discovery.json 2>/dev/null

• Checkpoint found -> offer: "Found previous run ({N} places from {date}).

Resume and fill gaps, or start fresh?"

• No checkpoint -> proceed to Step 4

Step 4: WSA Discovery

Discover available WSAs for all phases before execution. Run these searches simultaneously:

nimble extract:templates list --limit 100  # then filter items for "maps"

nimble extract:templates list --limit 100  # then filter items for "reviews"

nimble extract:templates list --limit 100  # then filter items for "social"

nimble extract:templates list --limit 100  # then filter items for "{place-type}"

From the combined results:

1. Filter by entity_type: SERP for discovery, PDP/Profile for enrichment/detail
2. Prefer managed_by: "nimble" over managed_by: "community"
3. Classify into phases -- see references/wsa-pipeline.md for classification strategy
4. Validate each with nimble extract:templates get --extract-template-name {name} to confirm params
5. Cache all discovered WSA names + validated params for the rest of the run

If no WSAs found for a phase, that phase falls back to nimble search. Log which phases had WSA coverage and which are using fallback.

Step 5: Primary Search (Phase 1)

Read references/wsa-pipeline.md for category detection logic.

Run discovered maps/location WSAs simultaneously, using the validated params from Step 4:

nimble extract:templates run --template {discovered_maps_wsa} --params '{...validated params...}'

nimble extract:templates run --template {discovered_review_site_wsa} --params '{...validated params...}'

Tertiary (conditional): Run discovered credibility WSAs only if primary + secondary return < 10 combined unique results, or if the user asked for credibility/trust data.

If any WSA fails or returns empty, fall back to: nimble search --query "[place-type] in [location]" --max-results 20 --search-depth lite

After discovery:

1. Parse all results into a unified entity list
2. Deduplicate following the Entity Deduplication pattern from

references/nimble-playbook.md: place_id exact match -> domain normalization -> fuzzy name + city

1. Save checkpoint:

~/.nimble/memory/local-places/checkpoints/{slug}/discovery.json

Step 6: Social Enrichment (Phase 2)

For each discovered place that has a Facebook page or Instagram handle, run the social WSAs discovered in Step 4. Batch max 4 concurrent Bash calls.

nimble extract:templates run --template {discovered_social_wsa} --params '{...validated params...}'

Run each discovered social WSA for places with matching handles. Skip social platforms for which no WSA was discovered. If no social WSAs were found in Step 4, skip this phase entirely.

Save checkpoint: ~/.nimble/memory/local-places/checkpoints/{slug}/social.json

Step 7: Reviews (Phase 3)

For the top places (by source count and data completeness), run the review WSAs discovered in Step 4:

nimble extract:templates run --template {discovered_reviews_wsa} --params '{...validated params...}'

Batch max 4 concurrent calls. Focus on places that have a place_id or equivalent identifier from Phase 1 discovery. If no review WSAs were found in Step 4, fall back to: nimble search --query "[place-name] reviews" --max-results 5 --search-depth lite

Save checkpoint: ~/.nimble/memory/local-places/checkpoints/{slug}/reviews.json

Step 8: Food/Drink Bonus (Phase 4)

Auto-trigger when the place type category matches food/drink keywords. See references/wsa-pipeline.md for the category detection logic.

If triggered, run the delivery/food WSAs discovered in Step 4. Discovery first, then detail:

nimble extract:templates run --template {discovered_delivery_serp_wsa} --params '{...validated params...}'

For places found on delivery platforms, fetch full details using discovered detail WSAs:

nimble extract:templates run --template {discovered_delivery_detail_wsa} --params '{...validated params...}'

If no delivery WSAs were found in Step 4, fall back to: nimble search --query "[place-name] [location] delivery" --max-results 3 --search-depth lite

Only run for food/drink categories. Skip if category doesn't match.

Step 9: Deduplication & Confidence Scoring

Deduplication: Run a final dedup pass across all phases following the Entity Deduplication pattern from references/nimble-playbook.md. Merge fields from multiple sources into a single enriched record per place.

Confidence scoring: Follow the Entity Confidence Scoring pattern from references/nimble-playbook.md. Skill-specific target fields (N=8):

Scoring criteria:

• High (8/8 fields + 2+ sources + 10+ reviews) -> display as *** High
• Medium (5-7/8 fields OR 2+ sources with partial data) -> ** Medium
• Low (<=4/8 fields, single source, few/no reviews) -> * Low

Step 10: Output

Present results as a numbered table sorted by confidence (High first), then by rating within each tier.

# Local Places: [Place Type] in [Location]
*Found [N] places | [Date] | Confidence: [H] High, [M] Medium, [L] Low*
 
## Results
 
| # | Name | Rating | Reviews | Confidence | Address | Sources |
|---|------|--------|---------|------------|---------|---------|
| 1 | Place A | 4.8 (312) | *** High | 123 Main St | [Maps][Yelp] |
| 2 | Place B | 4.6 (89)  | ** Medium | 456 Oak Ave | [Maps] |
...
 
## Top Picks (High Confidence)
 
### 1. Place A
- **Address:** 123 Main St, Williamsburg, Brooklyn, NY
- **Phone:** (555) 123-4567 | **Website:** [placea.com](https://placea.com)
- **Rating:** 4.8/5 (312 reviews on Google Maps, 289 on Yelp)
- **Social:** Instagram @placea (2.1K followers) | Facebook (1.8K likes)
- **Hours:** Mon-Fri 7am-7pm, Sat-Sun 8am-6pm
- **Delivery:** Available on DoorDash, Uber Eats
- **Why it stands out:** [1-2 sentences from review highlights]
- **Sources:** [Google Maps](link) | [Yelp](link) | [Facebook](link)
 
[Repeat for each High confidence place]
 
## Other Results (Medium + Low Confidence)
[Briefer format -- name, rating, address, missing data noted]
 
## What's Missing
[Note any data gaps: "3 places had no website or social presence",
 "Reviews unavailable for BBB-only listings"]

Source links are mandatory. Every place must have at least one clickable source URL (Google Maps link, Yelp listing, website, or social profile). Places without any source link should be noted in "What's Missing" but still included if they have sufficient data from WSA results.

Drill-down: After presenting, tell the user:

"Want details on any place? Say 'tell me more about #3' or ask for the

interactive map."

Step 11: Interactive Map (on request or "Deep dive" mode)

Generate an HTML file with Leaflet.js + OpenStreetMap tiles. See references/wsa-pipeline.md for the full map generation pattern and color scheme.

Save to: ~/.nimble/memory/local-places/{slug}-map-{date}.html

Open in browser: open ~/.nimble/memory/local-places/{slug}-map-{date}.html

Only generate automatically if the user chose "Deep dive with map" in Step 1. For map generation details, see references/wsa-pipeline.md. Otherwise, offer it as a follow-up action.

Step 12: Save to Memory

Make all Write calls simultaneously: