Agent skill

Goplaces Togo

by LeoYeAI in LeoYeAI/openclaw-master-skills

Ask the user for their Google saved places list, look up each place with goplaces, and recommend the single best one to visit today based on their preferences and visit history.

MITAuto-check passedDocuments & Office

Install Goplaces Togo

skills CLI
$ npx skills add LeoYeAI/openclaw-master-skills --skill goplaces-togo -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install LeoYeAI/openclaw-master-skills goplaces-togo --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/goplaces-togo .claude/skills/goplaces-togo && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
goplaces-togo
GitHub stars
2.2k
Token cost
~4.4k tokens
SKILL.md length
2,166 words
Files
2
Skills in repo
1,235
Repo updated
First seen
Licence
MIT

At a glance

Ask the user for their Google saved places list, look up each place with goplaces, and recommend the single best one to visit today based on their preferences and visit history.

  • Works in 12 steps: Load or ask for the saved list → Parse the CSV and extract user comments → Classify each place by city → …
  • Tasks that involve CSV and tabular files
  • SKILL.md covers Goal, Prerequisites, Data file schema and Steps, plus 1 more section
  • Reaches google.com; needs GOOGLE_PLACES_API_KEY

What it does

Goplaces Togo is an agent skill from LeoYeAI/openclaw-master-skills. Ask the user for their Google saved places list, look up each place with goplaces, and recommend the single best one to visit today based on their preferences and visit history.

Its SKILL.md is about 4.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `_meta.json`).

It sits in Documents & Office, covering CSV and tabular files. The repository describes itself as: 🧠 Curated collection of 1209+ best OpenClaw skills — weekly updated by MyClaw.ai. The licence is MIT.

When your agent uses it

  • Tasks that involve CSV and tabular files

Example prompts

  • “/goplaces-togo”

Requirements

  • A credential in GOOGLE_PLACES_API_KEY

Workflow steps

12 steps, taken from the step headings in SKILL.md.

  1. Load or ask for the saved list
  2. Parse the CSV and extract user comments
  3. Classify each place by city
  4. Ask which city they are in today
  5. Ask for cuisine and location preferences
  6. Resolve any remaining unresolved place IDs
  7. Fetch details for each place ID
  8. Load visit history
  9. Score and rank
  10. Recommend exactly one place
  11. Confirm selection and record the visit
  12. Handle edge cases

What it can do on your machine

Read from SKILL.md and the folder at commit e5199b5. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are bash and json).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • google.com

    Also links to:

    • takeout.google.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • GOOGLE_PLACES_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Goplaces Togo loads about 4.4k tokens when it runs. Until then it costs about 48 tokens; SKILL.md has 2,166 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~48
When it runs · the whole SKILL.md, loaded when a task matches
~4.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 2,166 words, ~4,438 tokens.

Download SKILL.mdSave it as .claude/skills/goplaces-togo/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
goplaces-togo
description
Ask the user for their Google saved places list, look up each place with goplaces, and recommend the single best one to visit today based on their preferences and visit history.

goplaces-togo

Goal

Help the user pick one place to visit from their saved Google Places list by fetching live details (rating, hours, reviews) via the goplaces CLI, incorporating the user's own notes, their stated cuisine and location preferences, and their visit history — then making a clear, opinionated recommendation.

Prerequisites

  • goplaces binary is installed and on PATH.
  • GOOGLE_PLACES_API_KEY environment variable is set.
  • Google Takeout saved places CSV — export once from https://takeout.google.com:
    1. Click "Deselect all", then check only "Saved"
    2. Click "Next step" → "Export once" → "Create export"
    3. Download the zip and find the CSV at Takeout/Saved/Saved Places.csv
    4. Give the agent the file path — it will import and remember it automatically
  • A local data file at skills/goplaces-togo/goplaces-visits.json persists the imported list and visit history (created automatically on first use).

Data file schema

skills/goplaces-togo/goplaces-visits.json is the single source of truth for all persistent state:

json
{
  "savedList": [
    {
      "name": "string",
      "mapsUrl": "string | null",
      "placeId": "string | null",
      "city": "string | null",
      "userComment": "string | null",
      "addedAt": "YYYY-MM-DD"
    }
  ],
  "places": {
    "<place_id>": {
      "name": "string",
      "city": "string | null",
      "visits": [
        { "date": "YYYY-MM-DD", "time": "HH:MM", "note": "string | null" }
      ]
    }
  }
}
  • savedList — the user's saved places, persisted so they don't have to paste it again.
  • savedList[].city — normalised city name resolved via the Places API; used to filter by city before scoring.
  • places — keyed by place_id, holds visit history for recording and recency scoring.

Steps

1. Load or ask for the saved list

Read skills/goplaces-togo/goplaces-visits.json. Check whether savedList exists and has at least one entry.

If a saved list exists, show it to the user and ask:

I have your saved list from last time:

  1. <name> — <userComment or "(no note)">
  2. ...

Use this list, or provide a new CSV to replace it? (Press Enter to use the existing list.)

  • If the user presses Enter or says "use it" / "yes" / similar affirmation → keep savedList as-is and proceed.
  • If the user provides a new CSV file path or pastes CSV content → replace savedList with the newly parsed entries (see Step 2), write to disk, then proceed.
  • If the user says "add" or "update" followed by a new CSV → merge: add new entries, keep existing ones that are not duplicates (match on name case-insensitively or mapsUrl). Write merged list to disk.

If no saved list exists, say exactly:

I need your Google saved places list. Here's how to get it:

  1. Go to https://takeout.google.com
  2. Click "Deselect all", then scroll down and check only "Saved"
  3. Click "Next step" → "Export once" → "Create export"
  4. Download the zip, open it, and find the CSV file inside the Saved/ folder (e.g. Saved Places.csv)
  5. Share the file path or paste its contents here

Wait for the user to provide the CSV before proceeding to Step 2.

2. Parse the CSV and extract user comments

The CSV exported from Google Takeout has this header and format:

Title,Note,URL,Tags,Comment
Gochi Cupertino,,https://www.google.com/maps/place/Gochi+Cupertino/data=!4m2!3m1!1s0x808fb5c78e1841e7:0xf8efac3fb5ce0b40,,
Kunjip Tofu,Fine and good for date,https://www.google.com/maps/place/Kunjip+Tofu/data=!4m2!3m1!1s0x808fb10003a9a597:0xe41f61b5ba1b2f19,,

For each non-empty data row (skip the header and blank rows):

  • Title → name
  • Note → userComment (use null if empty)
  • URL → mapsUrl — store as-is for reference; do not attempt to extract a place ID from the URL path, as the hex values in data=!4m2!3m1!1s<hex> are Google's internal CID format, not Places API IDs
  • Tags, Comment → ignore for now
  • Set addedAt to today's date
  • Store { name, mapsUrl, userComment, addedAt, placeId: null }

After parsing, write the resulting array to savedList in skills/goplaces-togo/goplaces-visits.json immediately, before doing any API calls. This ensures the list is never lost even if the session ends early.

3. Classify each place by city

For every entry in savedList where city is null, resolve the place name to get its city:

bash
goplaces resolve "<place name>" --limit 1 --json

From the result, extract the city using this priority order:

  1. candidates[0].addressComponents — find the component with types containing "locality" → use its longText
  2. Fall back to the component with types containing "administrative_area_level_2" → use its longText
  3. Fall back to parsing the city token from candidates[0].formattedAddress (typically the second comma-separated segment, e.g. "Gochi, Cupertino, CA 95014" → "Cupertino")
  4. If all fail, set city to null and note the entry as unclassified

Normalise city names to title case (e.g. "cupertino" → "Cupertino"). Store placeId from candidates[0].place_id at the same time — no need to re-resolve in Step 4.

After classifying all entries, write the updated savedList (with city and placeId filled in) back to disk.

Show the user a summary grouped by city:

Found N places across X cities:

  • Cupertino (3): Gochi Cupertino, Eilleen's Kitchen, ...
  • Santa Clara (2): Pho to Chau 999, ...
  • Unclassified (1): Some Place Name
4. Ask which city they are in today

Say exactly:

Which city are you in today? (or press Enter to search all cities)

  • If the user names a city → filter savedList to entries where city matches (case-insensitive). Use only these for scoring. If the city has zero matches, say so and ask again or offer to search all.
  • If the user presses Enter or says "all" / "anywhere" → use the full savedList.
  • Store the chosen city as currentCity (or null for all) in memory for this session.
5. Ask for cuisine and location preferences

Say exactly:

What cuisine or type of food are you in the mood for today? And do you have a preferred neighbourhood or area? (Press Enter to skip either.)

Wait for the user's response. Parse two optional values:

  • cuisinePreference — e.g. "Japanese", "Italian", "anything spicy", null if skipped.
  • locationPreference — e.g. "Shibuya", "within 2km of Shinjuku Station", null if skipped.

If the user skips both, proceed without preference filtering.

6. Resolve any remaining unresolved place IDs

Step 3 already resolved and stored placeId for newly imported entries. Only re-run resolve for entries that still have placeId: null (e.g. manually added entries):

bash
goplaces resolve "<place name>" --limit 1 --json

Parse the JSON. Take candidates[0].place_id. Also backfill city if it is still null. If the result is empty, mark the entry as unresolvable and skip it (report at the end).

7. Fetch details for each place ID
bash
goplaces details <place_id> --reviews --json

Collect the following fields for each place:

  • displayName.text — human name
  • currentOpeningHours.openNow — is it open right now?
  • rating — overall rating (0–5)
  • userRatingCount — number of reviews
  • priceLevel — 0 (free) to 4 (very expensive)
  • primaryType or types[] — cuisine/category tags
  • location — { latitude, longitude } for distance scoring
  • reviews[0].text.text — top review snippet (first 150 chars)
  • editorialSummary.text — one-line editorial blurb if present
8. Load visit history

The places section of skills/goplaces-togo/goplaces-visits.json was already read in Step 1. For each place in the working list, look up its place_id in places and derive:

  • visitCount — length of the visits array (0 if the key is absent).
  • daysSinceLastVisit — days between today and visits[last].date (null if never visited).
9. Score and rank

Compute a score for each resolved place. All bonus terms are additive.

base  = rating * log10(max(userRatingCount, 1))
open  = openNow ? +1.0 : -2.0

# Cuisine preference bonus (apply if cuisinePreference is set)
# Check if place types or editorial summary contain the preference keyword (case-insensitive)
cuisine = cuisineMatch ? +2.0 : 0.0

# Location preference bonus (apply if locationPreference is set)
# Resolve locationPreference to lat/lng via: goplaces resolve "<locationPreference>" --limit 1 --json
# Compute haversine distance in km between place.location and preference location
# distance_km = haversine(place.lat, place.lng, pref.lat, pref.lng)
location = distance_km <= 1  ? +2.0
         : distance_km <= 3  ? +1.0
         : distance_km <= 10 ? +0.0
         :                     -1.0
# If locationPreference is null, location bonus = 0

# User comment sentiment bonus
# If userComment is not null, read it holistically:
#   - Positive signals (e.g. "great", "love", "best", "go often") → +1.5
#   - Negative signals (e.g. "meh", "overrated", "avoid", "disappointing") → -1.5
#   - Conditional signals (e.g. "only on weekdays", "good for lunch") →
#       evaluate against current day/time; match → +0.5, mismatch → -0.5
#   - No clear signal → 0
comment = <sentiment score from userComment>

# Recency penalty — discourage going to the same place too soon
recency = visitCount == 0              ? +0.5   # never visited bonus
        : daysSinceLastVisit <= 7      ? -2.0
        : daysSinceLastVisit <= 30     ? -0.5
        :                                 0.0

score = base + open + cuisine + location + comment + recency

Rank places by score descending. Exclude unresolvable entries from the ranking but list them at the end.

10. Recommend exactly one place

Present the top-ranked place as your recommendation using this format:


Recommended: <Place Name>

  • Open now: Yes / No
  • Rating: X.X / 5 (N reviews)
  • Price: $ / $$ / $$$ / $$$$ (omit if unavailable)
  • Your note: "<userComment>" (omit if null)
  • Visits: N times (last: YYYY-MM-DD) / Never visited
  • Why: <one or two sentences drawing on editorial summary, top review, user comment, and how it matches their preferences>
bash
goplaces details <place_id> --reviews

(Run the above to see full hours, phone, and website.)


Then list the remaining resolved places as a ranked table:

RankPlaceRatingOpenVisitsScore
2...

Finish with any unresolvable entries: "Could not look up: X, Y — please check the spelling or paste Google Maps URLs."

If only one place was provided, still confirm it looks good (or flag if it is closed, poorly rated, or visited very recently).

11. Confirm selection and record the visit

After showing the recommendation, ask:

Are you going to <Place Name>? Say "yes" to log this visit, or tell me which place from the list you picked instead.

Wait for the user's response.

  • If the user confirms a place (by saying yes or naming one), record the visit:

    1. Read skills/goplaces-togo/goplaces-visits.json (already loaded; use in-memory copy).
    2. Ensure places["<place_id>"] exists; create it with { name, visits: [] } if not.
    3. Append a new visit entry:
      json
      { "date": "YYYY-MM-DD", "time": "HH:MM", "note": null }
      Use today's date and the current local time (24-hour format). Do not touch savedList.
    4. Write the full updated object (both savedList and places) back to skills/goplaces-togo/goplaces-visits.json.
    5. Confirm: "Logged your visit to <Place Name> on <date> at <time>. Have a great time!"
  • If the user says no or skips, say "No worries — enjoy your day!" and do nothing.

Show full SKILL.md (822 more words)Show less
12. Handle edge cases
  • No places resolved: Tell the user none of the entries could be matched and ask them to double-check names or paste Google Maps URLs instead.
  • All places closed: State that all options appear closed right now, rank by score anyway, and caveat the recommendation.
  • Location resolution fails: Skip the location bonus for all places and note that the area could not be resolved.
  • API error: Surface the error message and ask the user to verify GOOGLE_PLACES_API_KEY.
  • History file corrupt: Warn the user, treat both savedList and places as empty, and do not overwrite until the user provides a list or confirms a visit.
  • User wants to clear the list: If the user says "clear my list" or "forget my places", set savedList to [] in skills/goplaces-togo/goplaces-visits.json (keep places history intact) and confirm: "Your saved list has been cleared. Paste a new list whenever you're ready."
  • User wants to remove one entry: If the user says "remove <name>", delete the matching entry from savedList by name (case-insensitive), write to disk, confirm removal. Leave places history for that place_id untouched.
  • City classification fails for some entries: Proceed normally; list unclassified entries under "Unclassified" in the city summary. If the user picks a city, exclude unclassified entries from that city's pool but offer: "I also have N unclassified places — include them?"
  • User's city has only one place: Recommend it directly (skip scoring), but flag if it is closed or poorly rated.

Capture behavior

These phrases can be said at any point — not just during the recommendation flow. Detect the intent and act immediately without requiring the user to be in a specific step.

Retroactive visit logging
User saysAction
"I went to Nobu last night"Resolve "Nobu" against savedList (or run goplaces resolve). Ask: "Got it — what time did you go? (or press Enter to skip)". Log visit with yesterday's date and provided time (or null).
"We ended up going to that ramen place on Saturday"Identify the place (clarify if ambiguous). Ask for time, then log with the Saturday date.
"Just got back from Trattoria Roma"Log visit with today's date and current time.
"I visited 3 places this week: X, Y, Z"Log all three sequentially. For each, ask "What day and time for X?" then record.
Post-visit feedback
User saysAction
"It was amazing" / "Loved it"Find the most recently logged place (today or last visit). Set note on that visit entry to a positive summary. Update userComment in savedList entry to reflect the positive sentiment.
"It was just okay" / "Nothing special"Same as above but neutral note.
"Disappointing, won't go back"Log negative note on that visit. Update userComment in savedList to "disappointing — avoid". This will feed a -1.5 comment penalty in future scoring.
"Great for lunch but too loud for dinner"Log as conditional note. Update userComment to "good for lunch, too loud for dinner". Future scoring will match against time-of-day.
"The omakase was worth it, go on weekdays"Update userComment in savedList to the verbatim advice. Confirm: "Updated your note for <Place>."
City browsing
User saysAction
"What cities do I have?"Group savedList by city and print each city with the count and names of places in it.
"Show me my Tokyo places"Filter savedList to city == "Tokyo" and list them with userComment and visit count.
"I'm in San Jose today"Set currentCity = "San Jose" for this session, filter the working list accordingly, and jump straight to Step 5.
"Show places near Cupertino"Resolve "Cupertino" to lat/lng, then re-score using location bonus for all places regardless of city.
List management
User saysAction
"Add Sukiyabashi Jiro to my list"Append { name: "Sukiyabashi Jiro", mapsUrl: null, placeId: null, userComment: null, addedAt: today } to savedList. Confirm: "Added Sukiyabashi Jiro to your list."
"Add Pizza Pilgrims — good for groups"Parse name + comment from the phrase. Append with userComment: "good for groups", mapsUrl: null.
"Here's my updated Takeout CSV: <path>"Re-parse the CSV and merge into savedList: add new entries, update userComment for existing names, preserve visit history.
"Remove Shake Shack from my list"Delete matching savedList entry (case-insensitive). Confirm removal.
"Show me my list"Print all savedList entries: index, name, userComment, visit count from places.
"How many times have I been to Nobu?"Look up the place in places, count visits. Reply: "You've been to Nobu 4 times. Last visit: 2025-11-03."
"What did I think of Trattoria Roma?"Find the entry's userComment and the note fields on its visits. Summarise them.
Preference shortcuts
User saysAction
"Surprise me"Skip Step 3 entirely (no preference). Run full scoring and recommend top result.
"Something near me"Ask "What's your current neighbourhood or landmark?" then use that as locationPreference.
"I want Japanese, anywhere is fine"Set cuisinePreference = "Japanese", locationPreference = null. Jump straight to scoring.
"Same as last time"Reuse the cuisinePreference and locationPreference from the previous session if stored; otherwise ask again. Store last-used preferences under "lastPreferences": { cuisine, location } in skills/goplaces-togo/goplaces-visits.json.

© LeoYeAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in skills/goplaces-togo of LeoYeAI/openclaw-master-skills.

  • SKILL.md
  • _meta.json

Open the folder on GitHubat commit e5199b5

Compare with similar skills

Goplaces Togo next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Goplaces Togo compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Goplaces Togo this skillLeoYeAI/openclaw-master-skills2.2k—~4.4kAutomated safety check: PassMIT
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Instrument Data To Allotropeaws-samples/amazon-bedrock-agents-healthcare-lifesciences2742 repos~2.7kAutomated safety check: PassApache-2.0
Abuse Hunternexu-io/harness-engineering-guide664—~1.9kAutomated safety check: PassMIT
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Questions about Goplaces Togo

What does Goplaces Togo do?

Ask the user for their Google saved places list, look up each place with goplaces, and recommend the single best one to visit today based on their preferences and visit history. Goplaces Togo is an agent skill from LeoYeAI/openclaw-master-skills. Ask the user for their Google saved places list, look up each place with goplaces, and recommend the single best one to visit today based on their preferences and visit history.

When should I use Goplaces Togo?

Goplaces Togo fits situations like: tasks that involve CSV and tabular files.

How do I install Goplaces Togo in Claude Code?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill goplaces-togo -a claude-code`. Or copy the skill folder (skills/goplaces-togo in LeoYeAI/openclaw-master-skills) into .claude/skills/goplaces-togo in your project. Claude Code loads it when a task matches its description.

How do I install Goplaces Togo in Codex?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill goplaces-togo -a codex`. Or copy the skill folder (skills/goplaces-togo in LeoYeAI/openclaw-master-skills) into .agents/skills/goplaces-togo in your project. Codex loads it when a task matches its description.

Can I use Goplaces Togo in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add LeoYeAI/openclaw-master-skills --skill goplaces-togo -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/goplaces-togo, .gemini/skills/goplaces-togo, .github/skills/goplaces-togo and .opencode/skills/goplaces-togo in your project.

What does Goplaces Togo need to run?

Going by SKILL.md and its folder, Goplaces Togo needs credentials named GOOGLE_PLACES_API_KEY. Our summary lists: A credential in GOOGLE_PLACES_API_KEY.

Does Goplaces Togo access the network?

SKILL.md names 2 domains. In commands or code: google.com; the agent is likely to contact it when it follows the instructions. As links in the text: takeout.google.com. This is read from the text; nothing was executed.

Is Goplaces Togo safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Goplaces Togo use?

Goplaces Togo is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Goplaces Togo use?

About 4.4k tokens (SKILL.md is roughly 18k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Goplaces Togo?

Skills that share tags, products or a category with Goplaces Togo: Data Table Manager (n8n-io/n8n, 207k stars), Instrument Data To Allotrope (aws-samples/amazon-bedrock-agents-healthcare-lifesciences, 274 stars), Abuse Hunter (nexu-io/harness-engineering-guide, 664 stars) and Intelligence Requirements Builder (TracecatHQ/tracecat, 3.8k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Goplaces Togo?

LeoYeAI (a GitHub user) maintains it in LeoYeAI/openclaw-master-skills, which has 2,161 GitHub stars. The repository holds 1,235 skills in this directory. The repository was last updated on July 20, 2026.

Source: LeoYeAI/openclaw-master-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.