Pp Bestfoodtrucks

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Best Food Trucks — Printing Press CLI

Prerequisites: Install the CLI

This skill drives the bestfoodtrucks-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 bestfoodtrucks --cli-only

2. Verify: bestfoodtrucks-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/food-and-dining/bestfoodtrucks/cmd/bestfoodtrucks-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.

Best Food Trucks has no public API, no SDK, and no way to ask 'where else does this truck park' or 'summarize this week for my team.' This CLI talks directly to the same GraphQL backend the website and mobile apps use, then adds schedule digests, cuisine search, truck-centric reverse lookup, and cross-lot views the live site was never built to answer.

When to Use This CLI

Use this CLI for anonymous, read-only lookups of Best Food Trucks lot schedules, truck rotations, and menu data — checking what's at a specific office campus or lot, tracking a favorite truck's schedule across lots, or summarizing a week's schedule for a team. It is the right tool whenever a task needs structured or human-readable schedule/menu data faster than clicking through the bot-protected website by hand.

Anti-triggers

Do not use this CLI for:

• Do not use this CLI to place real food orders or complete checkout — order-ahead requires a customer login and payment method, which this CLI does not automate.
• Do not use this CLI to manage a customer's 'subscribe to lot schedule' notification preferences — that also requires a logged-in customer session not exercised by this build.
• Do not use this CLI as a general nationwide food-truck search engine beyond lots/trucks/markets already known to Best Food Trucks — it only knows what Best Food Trucks itself tracks.

Unique Capabilities

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

Cross-schedule synthesis

• lot digest — Turns a lot's upcoming schedule into ready-to-paste announcement text instead of raw structured data.

  Reach for this when the task is 'summarize this week's schedule for humans,' not 'give me structured data.'

  bestfoodtrucks-pp-cli lot digest playa-district

• trucks find — Finds every upcoming shift at a lot matching a cuisine, without opening each shift page one at a time.

  Use this when a user names a cuisine and a lot rather than a specific date — it walks the whole visible schedule window for you.

  bestfoodtrucks-pp-cli trucks find --cuisine Thai --lot playa-district --json

• lots digest — Combines multiple lots' schedules into one view in a single command instead of visiting each lot's page separately.

  Use this when a user tracks more than one regular lot (e.g., office campus plus a nearby favorite) and wants one combined answer.

  bestfoodtrucks-pp-cli lots digest --lots playa-district,at-t-los-angeles --json

Truck-centric views the website never built

• truck schedule — Shows every lot a specific truck visits, past and future — a view the Best Food Trucks website itself never built.

  Use this to answer 'when does my favorite truck come back' or 'what other lots does this truck serve' — impossible from the live site's own navigation.

  bestfoodtrucks-pp-cli truck schedule 11869 --json --select locations.records.startTime,locations.records.lot.name

• market hotlist — Ranks trucks operating in a city by review signal, a cross-truck aggregate the site never computes.

  Use this for 'what's the best-rated truck in this city' rather than checking trucks one at a time.

  bestfoodtrucks-pp-cli market hotlist los-angeles --limit 10

Discovery Signals

This CLI was generated with browser-observed traffic context.

• Capture coverage: 25 API entries from 196 total network entries
• Protocols: graphql (92% confidence), graphql_persisted_query (90% confidence), rest_json (75% confidence)
• Generation hints: browser_http_transport, graphql_persisted_query, requires_protected_client, weak_schema_confidence
• Candidate command ideas: create_graphql — Derived from observed POST /graphql traffic.; create_track_referrer.json — Derived from observed POST /track-referrer.json traffic.; create_track_request — Derived from observed POST /api/v1/intent_pixel/track_request traffic.; head_schedule.json — Derived from observed HEAD /_next/data/-dh7Qe2PYMUxWzoOQbyo3/lots/playa-district/schedule.json traffic.; list_179609_playa_district_on_2026_08_26.json — Derived from observed GET /_next/data/-dh7Qe2PYMUxWzoOQbyo3/shifts/179609-playa-district-on-2026-08-26.json traffic.; list_attribution_trigger — Derived from observed GET /attribution_trigger traffic.; list_can_track_visitor — Derived from observed GET /api/v1/intent_pixel/can_track_visitor traffic.; list_j — Derived from observed GET /j traffic.
• Caveats: empty_payload: API-looking request returned an empty or null payload; schema confidence is weak.; empty_payload: API-looking request returned an empty or null payload; schema confidence is weak.; empty_payload: API-looking request returned an empty or null payload; schema confidence is weak.; empty_payload: API-looking request returned an empty or null payload; schema confidence is weak.; empty_payload: API-looking request returned an empty or null payload; schema confidence is weak.; empty_payload: API-looking request returned an empty or null payload; schema confidence is weak.; empty_payload: API-looking request returned an empty or null payload; schema confidence is weak.; empty_payload: API-looking request returned an empty or null payload; schema confidence is weak.; empty_payload: API-looking request returned an empty or null payload; schema confidence is weak.; empty_payload: API-looking request returned an empty or null payload; schema confidence is weak.

Command Reference

graphql — Raw GraphQL passthrough against the Best Food Trucks API. This is a generic escape-hatch endpoint; Phase 3 hand-writes typed, well-named commands (lot get/schedule/digest, shift get, truck schedule, market list/hotlist, trucks find, lots digest) on top of a hand-authored GraphQL client rather than relying on this endpoint's auto-emitted command surface. The CLI always sends full query text (no extensions.persistedQuery hash) to avoid depending on the server's Apollo Automatic Persisted Query cache.

• bestfoodtrucks-pp-cli graphql — Execute a raw, read-only GraphQL query against the Best Food Trucks API (advanced escape hatch; mutations are out of scope)

Finding the right command

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

bestfoodtrucks-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.

Recipes

What's at my office today

bestfoodtrucks-pp-cli lot schedule playa-district --json --select locationSchedule.dateAlias,locationSchedule.locations.truck.name

Pulls just today/tomorrow's truck name from the full schedule payload, instead of parsing the whole nested response by hand.

Paste this week's schedule into Slack

bestfoodtrucks-pp-cli lot digest playa-district

Produces the announcement text directly — no manual copy-paste from the website required.

Find every Thai truck coming to my lot

bestfoodtrucks-pp-cli trucks find --cuisine Thai --lot playa-district --json

Walks the visible schedule window and filters by cuisine tag, something no single API call does server-side.

Track a favorite truck across every lot it visits

bestfoodtrucks-pp-cli truck schedule 11869 --agent --select locations.records.startTime,locations.records.lot.name

Reverse lookup with --select narrows a potentially large history-plus-future list down to just the two fields an agent needs to answer 'when and where.'

Combine two lots you care about into one view

bestfoodtrucks-pp-cli lots digest --lots playa-district,at-t-los-angeles

One command instead of visiting two separate lot pages and manually merging the results.

Auth Setup

No authentication required.

Run bestfoodtrucks-pp-cli doctor to verify setup.

Agent Mode

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

• 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:

  bestfoodtrucks-pp-cli graphql --query example-value --agent

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

• 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

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

• Use per-kind env vars only when a specific kind must diverge: BESTFOODTRUCKS_CONFIG_DIR, BESTFOODTRUCKS_DATA_DIR, BESTFOODTRUCKS_STATE_DIR, BESTFOODTRUCKS_CACHE_DIR.

• Resolution order is per-kind env var, --home, BESTFOODTRUCKS_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 data.db and the local learning store. state contains persisted queries, jobs, and teach.log. cache contains regenerable HTTP/cache files.

• This CLI stores no Best Food Trucks API credentials — the read surface is fully anonymous, so there is no credentials.toml or auth sidecar to manage.

• Run bestfoodtrucks-pp-cli doctor --fail-on warn to surface path and connectivity 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": {
      "bestfoodtrucks": {
        "command": "bestfoodtrucks-pp-mcp",
        "env": {
          "BESTFOODTRUCKS_HOME": "/srv/bestfoodtrucks"
        }
      }
    }
  }

Fleet precedence: an inherited per-kind env var overrides an explicit --home for that kind. Use BESTFOODTRUCKS_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 BESTFOODTRUCKS_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, run:

bestfoodtrucks-pp-cli recall "<user's question>" --agent

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>", "bestfoodtrucks-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 `bestfoodtrucks-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; bestfoodtrucks-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.