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Tencent SkillHub · Data Analysis

London public transport journey planner and disruptions

Plan TfL journeys from start/end/time, resolve locations (prefer postcodes), and warn about disruptions; suggest alternatives when disrupted.

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High Signal

Plan TfL journeys from start/end/time, resolve locations (prefer postcodes), and warn about disruptions; suggest alternatives when disrupted.

⬇ 0 downloads ★ 0 stars Unverified but indexed

Install for OpenClaw

Quick setup
  1. Download the package from Yavira.
  2. Extract the archive and review SKILL.md first.
  3. Import or place the package into your OpenClaw setup.

Requirements

Target platform
OpenClaw
Install method
Manual import
Extraction
Extract archive
Prerequisites
OpenClaw
Primary doc
SKILL.md

Package facts

Download mode
Yavira redirect
Package format
ZIP package
Source platform
Tencent SkillHub
What's included
scripts/tfl_journey_disruptions.py, SKILL.md

Validation

  • Use the Yavira download entry.
  • Review SKILL.md after the package is downloaded.
  • Confirm the extracted package contains the expected setup assets.

Install with your agent

Agent handoff

Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.

  1. Download the package from Yavira.
  2. Extract it into a folder your agent can access.
  3. Paste one of the prompts below and point your agent at the extracted folder.
New install

I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.

Upgrade existing

I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.

Trust & source

Release facts

Source
Tencent SkillHub
Verification
Indexed source record
Version
1.0.1

Documentation

ClawHub primary doc Primary doc: SKILL.md 8 sections Open source page

TfL Journey Planner + Disruption Checks

Use this skill when the user wants a TfL journey plan and needs disruption awareness. Reference: https://tfl.gov.uk/info-for/open-data-users/api-documentation

Script helper

Use scripts/tfl_journey_disruptions.py for a quick journey + disruption check. Examples: python3 scripts/tfl_journey_disruptions.py \"940GZZLUSTD\" \"W1F 9LD\" --depart-at 0900 python3 scripts/tfl_journey_disruptions.py --from \"Stratford\" --to \"W1F 9LD\" --arrive-by 1800 Notes: If the API returns disambiguation options, pick one and retry with its parameterValue. If you have TfL API keys, set TFL_APP_ID and TFL_APP_KEY in the environment.

Inputs to collect

From: postcode, stop/station name, place name, or lat,lon To: postcode, stop/station name, place name, or lat,lon Time + intent: depart at or arrive by (and date if not explicit) Optional: mode or accessibility constraints if the user mentions them If any of these are missing or ambiguous, ask the user for clarification.

Resolve locations

Prefer postcodes when available. Otherwise, resolve place names and stations: If input looks like a UK postcode, use it directly as {from} or {to}. If input is lat,lon, use as-is. If input is a stop or station name, try StopPoint/Search/{query} and choose a hub or the relevant NaPTAN ID. If the search or journey result returns disambiguation, show the top options (common name + parameterValue) and ask the user to pick. When unsure, ask a clarifying question rather than guessing.

Plan journeys

Call: /Journey/JourneyResults/{from}/to/{to}?date=YYYYMMDD&time=HHMM&timeIs=Depart|Arrive Guidelines: If the user says "arrive by" use timeIs=Arrive; otherwise default to Depart. If the date is not provided, ask. If the user implies "now", you can omit date/time.

Extract candidate routes

From the response, take the first 1-3 journeys. For each, capture: Duration and arrival time Public transport legs (mode, line name, direction) Line IDs for disruption checks Line IDs usually appear in leg.routeOptions[].lineIdentifier.id or leg.line.id. Ignore walking legs.

Disruption checks

For each journey, collect unique line IDs and call: /Line/{ids}/Status Treat a route as disrupted if any line status is not "Good Service" or includes a reason. Summarize the severity and reason. Optionally, check station-specific issues with /StopPoint/{id}/Disruption when relevant.

Response strategy

If the top route has no disruptions, recommend it and say no active disruptions were found. If the top route is disrupted, warn first, then propose 1-2 alternative routes from other journeys. If all routes are disrupted, still recommend the best option but list the disruption warnings and alternatives. If the journey is for a future time (later today or another day), note that disruption statuses are current and may change by the travel time (for example: "Minor Delays now; this may change by morning"). Always invite the user to confirm a route or provide clarifications.

Category context

Data access, storage, extraction, analysis, reporting, and insight generation.

Source: Tencent SkillHub

Largest current source with strong distribution and engagement signals.

Package contents

Included in package
1 Docs1 Scripts
  • SKILL.md Primary doc
  • scripts/tfl_journey_disruptions.py Scripts