Pp Japan47go

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JAPAN47GO — Printing Press CLI

Prerequisites: Install the CLI

This skill drives the japan47go-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 japan47go --cli-only

2. Verify: japan47go-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/japan47go/cmd/japan47go-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.

Discover a bounded Japanese shortlist, inspect local association conditions, and compare notice, party and fee rules before choosing a date. Saved observations retain their source and retrieval times; availability remains unknown.

When to Use This CLI

Use for Japanese local guide-association request conditions and selected experiences where notice, party size, qualified fees or route durations matter. Inspect details before making a recommendation and cite the source with observation time.

Anti-triggers

Do not use this CLI for:

• Bookings, payments or messages
• Live inventory, opening now or guaranteed operation
• Broad complete event recommendations or personal suitability

Unique Capabilities

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

Local service decisions

• services discover — Find Japanese guide and experience candidates with honest page coverage.

  Find Japanese guide and experience candidates with honest page coverage.

  japan47go-pp-cli services discover --query 妻籠 --kind guides --max-pages 1 --limit 3 --agent

• services inspect — Inspect bounded Japanese request, duration, fee and schedule facts.

  Inspect bounded Japanese request, duration, fee and schedule facts.

  japan47go-pp-cli services inspect 2980022e-ef99-4115-95e5-be5227cdc74e --agent

• services compare — Check a requested date and party against explicit notice and participant rules.

  Check a requested date and party against explicit notice and participant rules.

  japan47go-pp-cli services compare --ids 2980022e-ef99-4115-95e5-be5227cdc74e,0ad62a4e-2987-4e83-af63-7a6dd69e0d98 --on 2026-11-01 --as-of 2026-10-25 --party 1 --agent

• services compare — Compare fees without turning expense-based volunteer services into free tours.

  Compare fees without turning expense-based volunteer services into free tours.

  japan47go-pp-cli services compare --ids 0ad62a4e-2987-4e83-af63-7a6dd69e0d98,c98eaa8d-a854-4494-88db-a04b6de17461 --require-free --agent

Saved evidence

• services saved — Revisit saved normalized facts offline with original observation times.

  Revisit saved normalized facts offline with original observation times.

  japan47go-pp-cli services saved --query 妻籠 --limit 3 --agent

Command Reference

services — Local guide and experience evidence

• japan47go-pp-cli services discover — Discover bounded Japanese local service candidates

• japan47go-pp-cli services inspect — Inspect published local guide or experience facts

• japan47go-pp-cli services compare — Compare explicit notice, party and qualified fee requirements across 2..5 records

• japan47go-pp-cli services saved — Read normalized observations offline with fetch times

Finding the right command

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

japan47go-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

Guide candidates

japan47go-pp-cli services discover --query 妻籠 --kind guides --max-pages 1 --limit 3 --agent

Carry source query and page coverage into the answer.

Compact source conditions

japan47go-pp-cli services inspect 2980022e-ef99-4115-95e5-be5227cdc74e --agent --select id,name_ja,source_url,request,price,durations_minutes,observed_at

Keep decision facts and provenance visible.

Short notice and single traveler

japan47go-pp-cli services compare --ids 2980022e-ef99-4115-95e5-be5227cdc74e,0ad62a4e-2987-4e83-af63-7a6dd69e0d98 --on 2026-11-01 --as-of 2026-10-25 --party 1 --agent

Published-rule compatibility does not imply an available guide.

Saved evidence

japan47go-pp-cli services saved --query 妻籠 --limit 3 --agent

Use recorded fetch times rather than implying current facts.

Auth Setup

Public anonymous JAPAN47GO SSR pages; no API key or browser required. Provider reads only.

Run japan47go-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 the service workflows and generated learning commands:

• --json — one JSON document on stdout (warnings 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:

  japan47go-pp-cli services discover --agent --select id,name_ja,source_url

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

• Offline evidence — services saved and services inspect/compare --data-source local read normalized recorded facts

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

• Read-only — do not use this CLI for create, update, delete, publish, comment, upvote, invite, order, send, or other mutating requests

Response envelope

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

{
  "meta": {"source": "local"},
  "results": {"service": [], "returned": 0, "note": "No matching saved observations."}
}

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 JAPAN47GO_HOME=<dir> to relocate all four path kinds under one root.

• Use per-kind env vars only when a specific kind must diverge: JAPAN47GO_CONFIG_DIR, JAPAN47GO_DATA_DIR, JAPAN47GO_STATE_DIR, JAPAN47GO_CACHE_DIR.

• Resolution order is per-kind env var, --home, JAPAN47GO_HOME, XDG (XDG_CONFIG_HOME, XDG_DATA_HOME, XDG_STATE_HOME, XDG_CACHE_HOME), then platform defaults.

• config contains runtime config.json settings and profiles. data holds the separate generated learning database. state contains persisted queries/jobs and teach.log. cache contains the bounded japan47go-observations-v1.sqlite service observations.

• Public source reads require no credentials. No secret file or TOML migration is part of the service workflow.

• Run japan47go-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": {
      "japan47go": {
        "command": "japan47go-pp-mcp",
        "env": {
          "JAPAN47GO_HOME": "/srv/japan47go"
        }
      }
    }
  }

Fleet precedence: an inherited per-kind env var overrides an explicit --home for that kind. Use JAPAN47GO_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 JAPAN47GO_HOME, or saved observations will remain 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=$(cat /tmp/japan47go-question.txt)
japan47go-pp-cli recall "$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>", "japan47go-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. Candidate confirmation/rejection uses the numeric candidates[].id, which is separate from a service UUID. The example below assumes the returned candidate ID is 1; substitute the actual returned numeric ID:

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 1` only after the trial verified the behavior.
       Reject a wrong candidate with `learnings reject 1`.
    -> 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 `japan47go-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 1 prints the candidate's full payload before materializing it - check that the printed payload matches the behavior you verified. learnings reject 1 tombstones the derivation signature so the same candidate does not resurface. The envelope carries only the few candidates worth acting on now; japan47go-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.

Step 4: teach & after finalizing your response - always

Teaching is unconditional. After resolving a query the store could not answer, background-teach the final resource mapping - no call-count threshold, no judging whether it was "worth" learning. The teach is the anchor of the loop: it triggers playbook synthesis for a family without a playbook, and same-referent phrasings fold into one family so near-duplicate teaches do not fragment the store. Fire it after assembling your user-facing response but BEFORE emitting it, with a shell & so the call returns immediately. Pass the query the same way as recall — argv/MCP, or file-then-$QUERY. Do not splice the question into the command text:

QUERY=$(cat /tmp/japan47go-question.txt)
japan47go-pp-cli teach --query "$QUERY" --resource-type services --resource 2980022e-ef99-4115-95e5-be5227cdc74e --resource 0ad62a4e-2987-4e83-af63-7a6dd69e0d98
# (append shell `&` to background it)

Silent on success. Errors only land in teach.log under the resolved state dir. Teach the most specific resource - if the user asked a broad question and you walked through parent records to find the specific answer, teach the leaf id, not the parent. The CLI uses seeded entity_lookups for cross-alias resolution at recall time, so a teach under one alias (e.g., "Niners") satisfies future queries under another alias (e.g., "49ers", "San Francisco") automatically.

PII rule: teach the structural question with identifiers stripped - never include names, emails, phone numbers, account ids, or other personal identifiers in taught queries or notes. The CLI scans teach queries for obvious email/phone shapes and warns, but does not block; strip before teaching rather than relying on the warning.