Pp Foodpanda

foodpanda — Printing Press CLI

Prerequisites: Install the CLI

This skill drives the foodpanda-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 foodpanda --cli-only
   

2. Verify: foodpanda-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 before this CLI has a public-library category, install Node or use the category-specific Go fallback after publish.

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.

foodpanda's API hands you one restaurant at a time and forgets everything the moment you close the tab. This CLI mirrors an entire area into SQLite, so you can ask questions foodpanda structurally cannot answer: which restaurant near me sells the cheapest biryani, what changed in this menu since last week, and how delivery fees really compare once service fee and minimum order are counted. Works across every market foodpanda runs, and needs no API key for catalog data.

When to Use This CLI

Reach for this CLI whenever a question spans more than one restaurant, or spans time. It is the right tool for comparing prices, fees or ratings across many vendors at once, for finding which nearby restaurant sells a specific dish, for tracking how a menu changes between syncs, and for pulling structured menu data for analysis. It covers every foodpanda market, not just one country.

Anti-triggers

Do not use this CLI for:

• Do not use this CLI to place an order, build a cart, or pay — it is read-only by design and has no checkout.
• Do not use it to fetch merchant commission rates; those are contract terms that appear in no consumer-facing foodpanda response.
• Do not use it for pandamart or grocery product catalogs; darkstores are listed but their item catalog is a separate surface this CLI does not cover.
• Do not use it to read your past orders; foodpanda exposes no web order-history route.
• Do not use it for a competitor delivery platform such as Uber Eats, Deliveroo or Careem — it only speaks to foodpanda.

Unique Capabilities

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

Local state that compounds

• home — Rank every restaurant near your saved home address by what delivery actually costs you.

  Reach for this when the question is 'what is cheapest to get delivered to me', not 'what is this one restaurant's fee'.

  foodpanda-pp-cli home --sort fee --limit 25 --agent
  

• dish — Find which nearby restaurant sells a specific dish cheapest, searching every synced menu at once.

  Use this for item-level price hunting; use search when you want restaurants by name instead.

  foodpanda-pp-cli dish --query 'chicken biryani' --max-price 600 --agent
  

• menu-diff — Show what changed in a restaurant's menu and prices between two syncs.

  Use this to catch price rises, removed items, or newly added deals over time.

  foodpanda-pp-cli menu-diff --vendor-code pk2v --since 7d --agent
  

Competitive intelligence

• posture — Rank vendors by advertising and placement signals: CPC ad participation, promoted and premium status, and ranking score.

  Use this for competitive analysis of who buys placement. It does not report merchant commission rates, which are not exposed in any consumer surface.

  foodpanda-pp-cli posture --latitude 31.5204 --longitude 74.3587 --ads-only --agent
  

• coverage — Determine which vendors actually deliver to an arbitrary point using each vendor's published delivery radius.

  Use this before assuming a restaurant is orderable from an address you have not tried.

  foodpanda-pp-cli coverage --latitude 31.4820 --longitude 74.3430 --agent
  

• fees — Compare the full cost structure across an area: delivery fee, minimum order, service fee and VAT together.

  Use this when headline delivery fee is misleading because service fee or minimum order dominates.

  foodpanda-pp-cli fees --latitude 24.8607 --longitude 67.0011 --sort total --agent
  

Signal the site throws away

• digest — Split a restaurant's blended star rating into per-topic scores so food quality and delivery quality are separated.

  Use this to tell 'the food is bad' apart from 'the delivery is bad' before trusting a rating.

  foodpanda-pp-cli digest --vendor-code pk2v --agent
  

• market-compare — Run the same query across every foodpanda market and compare vendor counts, ratings and fees side by side.

  Use this for regional benchmarking; use vendors list when you only care about one city.

  foodpanda-pp-cli market-compare --query pizza --markets pk,sg,my --agent
  

• find — Search vendors live upstream and label how strongly each result actually matched the query.

  Use this for live upstream search where match quality matters; use 'search' instead for offline full-text search over already-synced data.

  foodpanda-pp-cli find --query sushi --latitude 31.5204 --longitude 74.3587 --explain --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.

Command Reference

menu — Full vendor detail including nested menus, products and prices

• foodpanda-pp-cli menu <vendor_code> — Fetch one vendor with full menu, deals and delivery conditions

reviews — Customer reviews with per-topic rating breakdown

• foodpanda-pp-cli reviews <vendor_code> — List reviews for a vendor, newest first

vendors — Browse and search foodpanda vendors near a location

• foodpanda-pp-cli vendors list — List vendors near a coordinate, with filters and sorting
• foodpanda-pp-cli vendors search — Full-text search vendors and dishes near a coordinate

Finding the right command

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

foodpanda-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

Cheapest delivery near home

foodpanda-pp-cli home --sort fee --limit 20 --agent

Ranks every restaurant reaching your saved home address by real delivery cost, which the app never lets you sort by.

Narrow a huge listing payload for an agent

foodpanda-pp-cli vendors list --latitude 31.5204 --longitude 74.3587 --limit 40 --agent --select code,name,rating,minimum_delivery_fee,minimum_order_amount

Vendor rows carry 80+ fields each; selecting five keeps the response small enough to reason over without burning context.

Find the cheapest biryani in town

foodpanda-pp-cli dish --query biryani --max-price 700 --sort price --agent

Searches every synced menu at once and returns item-level matches with the restaurant that sells them.

Track a menu for price rises

foodpanda-pp-cli menu-diff --vendor-code pk2v --since 14d --agent

Diffs two local snapshots to surface added, removed and repriced items over the last two weeks.

See who is buying placement

foodpanda-pp-cli posture --latitude 24.8607 --longitude 67.0011 --ads-only --sort points --agent

Ranks vendors by CPC ad participation and premium placement signals for competitive analysis.

Auth Setup

Catalog browsing needs no credentials at all — vendor search, menus, prices, deals and reviews all work anonymously. Only your own account data (saved addresses, and the home command that depends on them) needs a session. Run foodpanda-pp-cli auth login --chrome to import the token cookie from a browser where you are already signed in; the CLI composes it into an Authorization header. There is no API key to request and nothing to paste.

Run foodpanda-pp-cli doctor to verify setup.

Agent Mode

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

• 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:

  foodpanda-pp-cli menu mock-value --agent --select id,name,status
  

• 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

• 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": "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 FOODPANDA_HOME=<dir> to relocate all four path kinds under one root.

• Use per-kind env vars only when a specific kind must diverge: FOODPANDA_CONFIG_DIR, FOODPANDA_DATA_DIR, FOODPANDA_STATE_DIR, FOODPANDA_CACHE_DIR.

• Resolution order is per-kind env var, --home, FOODPANDA_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 foodpanda-pp-cli doctor --fail-on warn to surface path and credential-location 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": {
      "foodpanda": {
        "command": "foodpanda-pp-mcp",
        "env": {
          "FOODPANDA_HOME": "/srv/foodpanda"
        }
      }
    }
  }
  

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

foodpanda-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>", "foodpanda-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 `foodpanda-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; foodpanda-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.
• lookup_refresh_available (top-level): an entity in the query has no lookup row yet, but synced data could provide one. Run foodpanda-pp-cli sync to refresh entity lookups.
• 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:

foodpanda-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.
foodpanda-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).
foodpanda-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.

foodpanda-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

foodpanda-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.
• FOODPANDA_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:

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

Entries are stored locally as feedback.jsonl under the resolved data dir. They are never POSTed unless FOODPANDA_FEEDBACK_ENDPOINT is set AND either --send is passed or FOODPANDA_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.

foodpanda-pp-cli profile save briefing --json
foodpanda-pp-cli --profile briefing menu mock-value
foodpanda-pp-cli profile list --json
foodpanda-pp-cli profile show briefing
foodpanda-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 foodpanda-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/foodpanda/cmd/foodpanda-pp-mcp@latest
   

2. Register with Claude Code:

   claude mcp add foodpanda-pp-mcp -- foodpanda-pp-mcp
   

3. Verify: claude mcp list

Direct Use

1. Check if installed: which foodpanda-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:

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

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