Pp Airport Limousine

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Airport Limousine — Printing Press CLI

Prerequisites: Install the CLI

This skill drives the airport-limousine-pp-cli binary. You must verify the CLI is installed before invoking any command from this skill. If it is missing, install it first:

1. Install via the Printing Press installer. It defaults binaries to $HOME/.local/bin on macOS/Linux and %LOCALAPPDATA%\Programs\PrintingPress\bin on Windows:

   npx -y @mvanhorn/printing-press-library install airport-limousine --cli-only

2. Verify: airport-limousine-pp-cli --version
3. Ensure the reported install directory is on $PATH for the agent/runtime that will invoke this skill.

If the npx install fails (no Node, offline, etc.), fall back to a direct Go install (requires Go 1.26.6 or newer). This installs into $GOPATH/bin (default $HOME/go/bin), so add that directory to $PATH instead:

go install github.com/mvanhorn/printing-press-library/library/travel/airport-limousine/cmd/airport-limousine-pp-cli@latest

If --version reports "command not found" after install, the runtime cannot see the binary directory on $PATH. Do not proceed with skill commands until verification succeeds.

Find routes and stops, inspect a JST service date, compare current duration evidence and plan party fares. Every result retains the provider source and observation time.

Airport bus source contract

routes, stops, timetable, travel-times, transfers, fare, conditions and handoff use fresh public provider reads. Use --data-source auto or --data-source live; these commands reject local mode before making requests. Each invocation permits at most eight provider requests, reads at most 2 MiB per response, uses adaptive pacing, refuses redirects, and follows the configured --timeout for the whole command. Rate limiting is a typed error, rather than an empty result.

Select exact stop IDs: Haneda and Narita terminals, Shinjuku Station and Shinjuku Expressway Bus Terminal remain distinct. Dates default to today in Asia/Tokyo. Timetable output retains raw provider times and absolute +09:00 times; day_offset and rollover_inferred expose midnight handling. Route context and station columns appear under meta.context and meta.stations.

Current travel times retain the source JST clock, which has no verified date, and explicitly preserve adjusting, retrieving and unknown values. They are route estimates and do not identify a terminal or guarantee arrival. Schedules and published fares do not establish seat availability. fare uses actual published adult/child units for a served stop pair; if units differ across trips, select --trip-id from timetable. A missing unit remains unknown. Passenger category eligibility and operator-specific rules remain the user's responsibility.

conditions retains guide content update dates and links to current baggage notices. Its structured bag values and units survive agent output. handoff validates the dated route and prints its canonical timetable page; the user follows reservation links on that page. No command books, pays or changes an account.

When to Use This CLI

Use for Airport Limousine public route/stop discovery, dated Tokyo airport-bus schedules, published fares and current duration evidence. Choose exact terminal IDs before interpreting times or party fares.

Anti-triggers

Do not use this CLI for:

• Seat reservation, payments or account changes.
• Arrival guarantees, flight-connection guarantees or seat-inventory claims.
• Other bus operators or multimodal route pricing.

Unique Capabilities

These capabilities aren't available in any other tool for this API.

Airport bus planning

• timetable — Plan a service date at exact stops with JST times and midnight rollover.

  Inspect exact terminal rows and omit seat inventory.

  airport-limousine-pp-cli timetable --agent

• travel-times — Compare current and standard duration estimates with explicit unavailable states.

  Preserve source clock and estimates without guaranteeing arrival.

  airport-limousine-pp-cli travel-times --agent

• transfers — Read both airport transfer directions for the same service date.

  Keep terminal identity and failed fetches distinct.

  airport-limousine-pp-cli transfers --agent

• fare — Calculate an adult/child total from the operator fare for a served pair.

  State passenger-category assumptions and unknown fares.

  airport-limousine-pp-cli fare --agent

• conditions — Read concise current provider conditions with source timestamps and notices.

  Check luggage and child seating before booking.

  airport-limousine-pp-cli conditions --agent

HTTP Transport

This CLI uses Chrome-compatible HTTP transport for browser-facing endpoints. It does not require a resident browser process for normal API calls.

Discovery Signals

This CLI was generated with browser-observed traffic context.

• Capture coverage: 1 API entries from 5 total network entries
• Protocols: rest_json (75% confidence), html_scrape (55% confidence)
• Auth signals: none
• Candidate command ideas: list___data.json — Derived from observed GET /en/timetable/detail/Haneda-Narita/__data.json traffic.

Command Reference

pages — Read canonical operator page links

• airport-limousine-pp-cli pages guide — Read the provider baggage and boarding guide links
• airport-limousine-pp-cli pages routes — Read canonical route and timetable links
• airport-limousine-pp-cli pages stop — Read a known operator stop page and links
• airport-limousine-pp-cli pages timetable — Read canonical date timetable handoff links

Finding the right command

When you know what you want to do but not which command does it, ask the CLI directly:

airport-limousine-pp-cli which "<capability in your own words>"

which resolves a natural-language capability query to the best matching command from this CLI's curated feature index. Exit code 0 means at least one match; exit code 2 means no confident match — fall back to --help or use a narrower query. --json (and other machine formats) keep that exit-2 contract and write {"matches":[]} on stdout so agents can inspect the envelope without treating a miss as success.

Recipes

Find a stop

airport-limousine-pp-cli stops find --query Shinjuku --agent --select results.id,results.name,meta.observed_at

Retain exact stop IDs and source freshness.

Inspect a terminal

airport-limousine-pp-cli stops get HanedaAirportTerminal3 --agent

Read boarding map and provider route handoff.

Compare transfers

airport-limousine-pp-cli transfers --agent

Read both directions for today in JST.

Read luggage conditions

airport-limousine-pp-cli conditions --topic baggage --agent

Check current operator limits and linked notices.

Auth Setup

Public read-only sources; no login, key or reservation action. Chrome-compatible HTTP runs without a resident browser.

Run airport-limousine-pp-cli doctor to verify setup.

Agent Mode

Add --agent to any command. Expands to: --json --compact --no-input --no-color.

Global format flags share one contract on promoted, novel, sync, and --deliver paths:

• --json — one JSON document on stdout (sync progress events go to stderr)

• --compact — keep identity/status/timestamp fields; does not change the document vs stream shape

• --csv / --plain — tabular rows (collection envelopes unwrap to the row array)

• --quiet — one identity value per row, no envelope

• Pipeable — JSON on stdout, errors on stderr

• Filterable — --select keeps a subset of fields. Dotted paths descend into nested structures; arrays traverse element-wise. Critical for keeping context small on verbose APIs:

  airport-limousine-pp-cli pages guide --agent

• Previewable — --dry-run shows the request without sending

• Offline-friendly — sync/search commands can use the local SQLite store when available

• Non-interactive — never prompts, every input is a flag

• Explicit confirmation — --agent does not imply --yes; pass --yes separately only after the target, arguments, and side effects are clear

Response envelope

Commands that read from the local store or the API wrap output in a provenance envelope:

{
  "meta": {"source": "live" | "local", "synced_at": "...", "reason": "..."},
  "results": <data>
}

Parse .results for data and .meta.source to know whether it's live or local. A human-readable N results (live) summary is printed to stderr only when stdout is a terminal AND no machine-format flag (--json, --csv, --compact, --quiet, --plain, --select) is set — piped/agent consumers and explicit-format runs get pure JSON on stdout.

Paths and state

Agents should treat the CLI's path resolver as part of the runtime contract:

• Use --home <dir> for one invocation, or set AIRPORT_LIMOUSINE_HOME=<dir> to relocate all four path kinds under one root.

• Use per-kind env vars only when a specific kind must diverge: AIRPORT_LIMOUSINE_CONFIG_DIR, AIRPORT_LIMOUSINE_DATA_DIR, AIRPORT_LIMOUSINE_STATE_DIR, AIRPORT_LIMOUSINE_CACHE_DIR.

• Resolution order is per-kind env var, --home, AIRPORT_LIMOUSINE_HOME, XDG (XDG_CONFIG_HOME, XDG_DATA_HOME, XDG_STATE_HOME, XDG_CACHE_HOME), then platform defaults.

• config contains settings like config.toml and profiles. data contains credentials.toml, data.db, cookies, and auth sidecars. state contains persisted queries, jobs, and teach.log. cache contains regenerable HTTP/cache files.

• Stored secrets live in credentials.toml under the data dir. Existing legacy config.toml secrets are read for compatibility and leave config.toml on the first auth write.

• Run airport-limousine-pp-cli doctor --fail-on warn to surface path warnings. agent-context exposes a schema v4 paths block for agents that need the resolved dirs.

• For MCP, pass relocation through the MCP host config. The MCP binary does not inherit CLI flags:

  {
    "mcpServers": {
      "airport-limousine": {
        "command": "airport-limousine-pp-mcp",
        "env": {
          "AIRPORT_LIMOUSINE_HOME": "/srv/airport-limousine"
        }
      }
    }
  }

Fleet precedence: an inherited per-kind env var overrides an explicit --home for that kind. Use AIRPORT_LIMOUSINE_HOME or per-kind vars as durable fleet levers, and use --home only for a single invocation. Relocation is not reversible by unsetting env vars; move files manually before clearing AIRPORT_LIMOUSINE_HOME, or doctor will not find credentials left under the former root.

Automatic learning

This CLI ships a self-capturing learning loop. The CLI does its own bookkeeping: every invocation is journaled locally, a failed flag followed by a corrected retry auto-derives a flag_alias candidate, and a teach on a query family without a playbook auto-synthesizes a playbook_candidate from the session's journal. Your job is judgment only: recall first, act on surfaced candidates, teach the final answer, playbook amend when you observe a correction. You never record failures by hand.

Step 1: recall before any discovery

Before list/search/drill commands on a new user question, pass the question as an argv or MCP tool argument to recall --agent. Do not interpolate user-controlled text into a shell command line.

Quoted recall "<question>" breaks on an apostrophe, which is ordinary English. A quoted heredoc breaks when a body line equals the delimiter, and that delimiter is published in these docs. Write the question with a non-shell file-writing tool, then read it back as data:

# Write the question verbatim with your file-writing tool (no shell involved).
# Command substitution on a file only ever yields data — the shell never
# parses the file's bytes as syntax.
QUERY=$(cat /path/to/question.txt)
airport-limousine-pp-cli recall "$QUERY" --agent

Prefer MCP: pass the question as the tool's query argument. "$QUERY" after a file read is argv-safe; putting the question itself in the command text is not.

The response envelope:

{
  "query": "...",
  "normalized": "<normalized form>",
  "query_entities": ["..."],
  "found": true | false,
  "match_score": 0.0,
  "results": [
    { "resource_id": "...", "resource_type": "...", "venue": "...",
      "confidence": 2, "entity_match": "exact|partial|unknown",
      "source": "taught|preseed|pattern", "warnings": ["..."] }
  ],
  "mismatches": [ /* only when --debug-mismatches */ ],
  "warnings": [ /* top-level */ ],
  "candidates": [
    { "id": 12, "class": "flag_alias | playbook_candidate",
      "summary": "...", "sightings": 3, "last_seen": "...",
      "rationale": "...",
      "next_action": ["<trial command>", "airport-limousine-pp-cli learnings confirm 12"] }
  ],
  "playbook": {
    "query_family": "...",
    "playbook": {
      "steps": [ { "cmd": "<command with {slot} substitution>", "purpose": "..." } ],
      "entity_slots": ["$ENTITY"],
      "expected_tool_calls": 3
    },
    "slots_resolved": { "$ENTITY": { "token": "<live token>", "canonical": "<canonical>" } },
    "notes": "<workarounds + gotchas for this query family>"
  },
  "notes": "<duplicate surface for non-playbook callers>"
}

Empty-store short-circuit: if the store has no learnings, playbooks, or candidates yet (recall finds nothing and learnings list and learnings candidates are both empty), skip recall for the rest of this session instead of taxing every query; resume recall-first once something has been taught.

Step 2: decision tree

Read candidates, playbook, notes, results[0], and warnings in that order:

if Candidates present (warnings include "candidates_present"):
    -> candidates are try-then-confirm, never facts. Follow each candidate's
       two-step next_action verbatim: run the trial command first, then run
       `learnings confirm <id>` only after the trial verified the behavior.
       Reject a wrong candidate with `learnings reject <id>`.
    -> NEVER re-teach something recall surfaced as a candidate; confirm or
       reject that candidate instead of teaching a duplicate.
    -> candidates ride alongside playbooks and resource hits, not instead of
       them; continue with the branches below after acting on them.

if Playbook present:
    -> READ Playbook.notes verbatim FIRST (workarounds + gotchas the CLI surface doesn't expose)
    -> replay Playbook.steps in order, substituting Playbook.slots_resolved entries
       for the entity slot tokens. If a step's slot is unresolved, fall back to
       discovery for that step only.
    -> the Playbook's expected_tool_calls is a budget; if you find yourself running
       materially more, record the divergence via `airport-limousine-pp-cli playbook amend`
       at end-of-session.

elif Notes present (no Playbook):
    -> read Notes verbatim before any discovery step; they carry known gotchas
       for this query family even when no structured choreography exists yet.

elif Found AND Results[0].EntityMatch == "exact" AND Results[0].Confidence >= 2:
    -> skip discovery; fetch live data for Results[*].ResourceID in parallel

elif Found AND Results[0].EntityMatch == "partial":
    -> candidate hint, NOT a hit; read the resource title to validate before trusting

elif (any row in Mismatches[] when --debug-mismatches was passed):
    -> treat as cold start; the stored learning is for a different entity
       (different canonical resolved from query_entities)

else:  // Found == false, no playbook, no notes
    -> cold start; run discovery normally; teach the answer afterward (Step 4).
       If the family has no playbook yet, that teach auto-synthesizes a
       playbook candidate from this session's journal - you do not need to
       record one by hand.

Playbook and Notes are orthogonal to the per-resource path. A recall response can carry both a Playbook AND a Results[] hit - use both: the Playbook tells you which choreography to run; the resource hits short-circuit specific steps. Default to skipping mismatches; pass --debug-mismatches only when investigating cold-start surprises.

Candidate judgment details: learnings confirm <id> prints the candidate's full payload before materializing it - check that the printed payload matches the behavior you verified. learnings reject <id> tombstones the derivation signature so the same candidate does not resurface. The envelope carries only the few candidates worth acting on now; airport-limousine-pp-cli learnings candidates lists the full open set.

Graceful degradation: if learnings confirm is an unknown command, you are driving an older binary - ignore the candidates guidance and follow the rest of the protocol.

Step 3: always read warnings

• low_confidence: row exists at confidence<2. Treat as a hint, not a skip-discovery hit.
• resource_not_in_store: the local store doesn't have the resource the learning points at. The match validator couldn't classify entities — direct-fetch and re-evaluate.
• cross_alias_match (per-result): the row was taught under a different alias and matched the live query's canonical via entity_lookups (e.g., a "USA" teach satisfying a "United States" recall). Trust the resource_id.
• similar_shape_different_entity:<canonical> (top-level): a structurally matching row exists but its canonical entity differs from the live query's. Treated as cold start; the warning carries the conflicting canonical as a hint, but the row is NOT promoted into Results.
• ambiguous_alias (top-level): a single query entity resolved to multiple canonicals (e.g., "Cards" → Arizona Cardinals + St. Louis Cardinals). Surface the ambiguity from context before committing to a resource.
• candidates_present (top-level): the envelope carries a candidates section. Handle it via the candidates branch in Step 2 before anything else.
• Top-level no_learnings_for_query_family: the table had no rows above the Jaccard floor. Pure cold start.