Pp Bestfoodtrucks

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.

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:

bestfoodtrucks-pp-cli teach --query "<user's question>" --resource-type <type> --resource <id1> --resource <id2>
# (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.

Step 5: playbooks - optional flags, automatic synthesis

You do not need to decide whether a session "deserves" a playbook: a teach on a family without one auto-synthesizes a playbook_candidate from the session's journal, and the next session judges it via confirm/reject. Attach explicit playbook flags only when you already hold choreography worth recording verbatim - workarounds the CLI didn't surface (silently-dropped flags, undocumented params, pagination tricks, payload gotchas). Prefer the integrated one-call form - record the resource learning and the playbook in the same teach invocation:

# Common case: record both the resource learning AND the playbook in one call.
bestfoodtrucks-pp-cli teach \
  --query "<user's question>" \
  --resource <id> \
  --playbook-file ~/playbooks/<shape>.json \
  --playbook-notes-file ~/playbooks/<shape>-notes.md
# (append shell `&` to background it)

# Alternate: playbook-only (no resource to record alongside).
bestfoodtrucks-pp-cli teach-playbook \
  --query "<user's question>" \
  --playbook-file ~/playbooks/<shape>.json \
  --notes-file ~/playbooks/<shape>-notes.md

Playbook files are JSON with steps, entity_slots, expected_tool_calls. Notes files are markdown carrying the gotchas verbatim. File-free callers (MCP-only agents) pass the same content inline: --playbook-json and --playbook-notes on the integrated teach form, --playbook-json and --notes on teach-playbook. On the integrated teach form, the playbook flags are optional - omit them entirely for a resource-only teach. On the standalone teach-playbook form, at least one of the playbook and notes flags must be set; both empty is rejected. Playbooks are keyed on the structural query family (entities stripped) so a recipe taught from one entity-shaped query applies to every other query of the same shape, with slots_resolved binding the live query's canonical at recall time.

When you DO find a playbook on a future recall, treat it as ground truth: replay the steps with slots_resolved substitutions, skip the discovery that the choreography already documents, and read notes before any step.

Step 6: playbook amend & when your debug response identifies a correction

If your debug-protocol response identifies a concrete correction the notes or playbook should know — a workaround, an undocumented endpoint shape, a stale field name, observed schema drift, an empty-payload fallback — fire playbook amend BEFORE emitting your user-facing response. Same fire-and-forget posture as teach.

bestfoodtrucks-pp-cli playbook amend \
  --query "<exact recall query string>" \
  --add-note "<your concrete correction>"
# (append shell `&` to background it)

What counts as worth amending: a behavior you OBSERVED this session that future-you would benefit from knowing. Examples worth amending:

• A workaround for a CLI surface that silently drops or misorders a flag.
• An undocumented endpoint shape (response wrapped in {meta, results}, payload nested two levels deeper than the docs claim).
• Observed schema drift (a field renamed, an index that shifted between seasons, a category label that the API now returns lower-cased).

What does NOT belong in notes:

• The year-specific or entity-specific answer to the user's question. That's the response, not a learning.
• Per-team / per-athlete / per-row data the playbook already retrieves at runtime.
• Statements that paraphrase what the existing notes already say.

The amend command appends to the family's existing notes with a timestamped marker ([amend YYYY-MM-DDTHH:MMZ]: <text>). Multiple amends accumulate; the audit trail is visible. If no playbook exists yet for the family, amend creates a notes-only one (so cold-start corrections still land).

PII discipline for amend notes

playbook amend notes are designed to potentially flow upstream as shared knowledge in future versions of the Printing Press. Keep them clean of user-identifying content so the upstream-contribution path stays open without retroactive scrubbing:

• Do NOT embed paths to user filesystems, personal API keys or tokens, user email addresses, user GitHub handles, or specific query histories tied to a single user.
• Acceptable: endpoint shapes, undocumented field names, API gotchas, observed schema drift, workarounds for CLI surfaces, generalizable pagination or retry tactics.

If a correction is only meaningful with user-specific context, it belongs in a personal note, not in the playbook amend.

Measuring the loop

bestfoodtrucks-pp-cli learnings stats reports recall hit rate, teach-to-reuse, playbook resolution rate, and candidate confirm/reject counts from the local learn_events table. Rates are null until they have a denominator; everything stays on this machine. Use it to check whether the loop is earning its keep for this CLI.

Disabling learning

• --no-learn on a single command short-circuits both recall and the teach write path. Use for deterministic agent flows or tests that must not be affected by accumulated learnings.
• BESTFOODTRUCKS_NO_LEARN=true in the environment globally disables the pipeline.

Agent Feedback

When you (or the agent) notice something off about this CLI, record it:

bestfoodtrucks-pp-cli feedback "the --since flag is inclusive but docs say exclusive"
bestfoodtrucks-pp-cli feedback --stdin < notes.txt
bestfoodtrucks-pp-cli feedback list --json --limit 10

Entries are stored locally as feedback.jsonl under the resolved data dir. They are never POSTed unless BESTFOODTRUCKS_FEEDBACK_ENDPOINT is set AND either --send is passed or BESTFOODTRUCKS_FEEDBACK_AUTO_SEND=true. Default behavior is local-only.

Write what surprised you, not a bug report. Short, specific, one line: that is the part that compounds.

Output Delivery

Every command accepts --deliver <sink>. The output goes to the named sink in addition to (or instead of) stdout, so agents can route command results without hand-piping. Three sinks are supported:

Unknown schemes are refused with a structured error naming the supported set. Webhook failures return non-zero and log the URL + HTTP status on stderr.

Named Profiles

A profile is a saved set of flag values, reused across invocations. Use it when a scheduled or recurring agent reuses the same saved flags while providing different input each run.

bestfoodtrucks-pp-cli profile save briefing --json
bestfoodtrucks-pp-cli --profile briefing graphql --query example-value
bestfoodtrucks-pp-cli profile list --json
bestfoodtrucks-pp-cli profile show briefing
bestfoodtrucks-pp-cli profile delete briefing --yes

Explicit flags always win over profile values; profile values win over defaults. agent-context lists all available profiles under available_profiles so introspecting agents discover them at runtime.

Exit Codes

Argument Parsing

Parse $ARGUMENTS:

1. Empty, help, or --help → show bestfoodtrucks-pp-cli --help output
2. Starts with install → ends with mcp → MCP installation; otherwise → see Prerequisites above
3. Anything else → Direct Use (execute as CLI command with --agent)

MCP Server Installation

1. Install the MCP server:

   go install github.com/mvanhorn/printing-press-library/library/food-and-dining/bestfoodtrucks/cmd/bestfoodtrucks-pp-mcp@latest
   

2. Register with Claude Code:

   claude mcp add bestfoodtrucks-pp-mcp -- bestfoodtrucks-pp-mcp
   

3. Verify: claude mcp list

Direct Use

1. Check if installed: which bestfoodtrucks-pp-cli If not found, offer to install (see Prerequisites at the top of this skill).
2. Match the user query to the best command from the Unique Capabilities and Command Reference above.
3. Execute with the --agent flag:

   bestfoodtrucks-pp-cli <command> [subcommand] [args] --agent
   

4. If ambiguous, drill into subcommand help: bestfoodtrucks-pp-cli <command> --help.