Pp Overpass

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Overpass — Printing Press CLI

Prerequisites: Install the CLI

This skill drives the overpass-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 overpass --cli-only

2. Verify: overpass-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.

Overpass is a query language wearing a REST costume, and its public mirrors fall over independently under load. This maps photographic vocabulary — water towers, lighthouses, brutalist buildings, piers, observatories — onto the right tag combinations, builds valid Overpass QL, retries across mirrors, caches results locally, and exports GeoJSON.

When to Use This CLI

Use this when the task is finding physical things in the world worth going to look at — structures, viewpoints, industrial or architectural subjects — near a place or along a route. It also exposes raw Overpass QL for queries the curated taxonomy does not cover.

When Not to Use This CLI

Do not activate this CLI for requests that require creating, updating, deleting, publishing, commenting, upvoting, inviting, ordering, sending messages, booking, purchasing, or changing remote state. This printed CLI exposes read-only commands for inspection, export, sync, and analysis.

Unique Capabilities

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

Subjects without the query language

• near — Finds things worth photographing near a place — water towers, lighthouses, brutalist buildings, piers, viewpoints — by name rather than by OpenStreetMap tag.

  Reach for this whenever someone wants to know what is worth shooting around a place, without needing to know OpenStreetMap tagging.

  overpass-pp-cli near --at "Los Angeles" --type water_tower --radius 25km

• types — Lists every subject type the CLI knows how to find, with the OpenStreetMap tags behind each one.

  Reach for this to see what can be searched before running near, or to check what tags a subject type actually resolves to.

  overpass-pp-cli types --group architecture

• route — Finds subjects inside a corridor between two places, so a drive can be planned around what is worth stopping for.

  Reach for this when planning a drive or day trip and the question is what to stop for on the way.

  overpass-pp-cli route --from "Los Angeles" --to "Salton Sea" --type water_tower --corridor 15km

• geojson — Writes results as GeoJSON for opening in a map, a Leaflet page, or any GIS tool.

  Reach for this when the result needs to end up on a map rather than in a terminal.

  overpass-pp-cli geojson --at "Los Angeles" --type viewpoint --radius 10km

Surviving a flaky public API

• mirrors — Shows which Overpass mirrors are currently answering and how many rate-limit slots each has free.

  Reach for this when queries are failing or slow, before assuming the query itself is wrong.

  overpass-pp-cli mirrors

• Check partial before you trust a count. Overpass answers a query that hit its own server-side timeout or memory ceiling with usable elements plus a remark — the list is real but it undercounts, and nothing about the results themselves says so. near --json and route --json set "partial": true and "partial_remark"; geojson puts the same two keys on the FeatureCollection, so a saved .geojson still carries the caveat when it is reopened later. When partial is true, narrow the radius or raise --query-timeout before reporting a number.

  The warning is never printed ahead of a JSON or GeoJSON document — it rides inside it, with a human-readable copy on stderr. Prose on stdout would make the response unparseable for exactly the queries most worth re-reading.

Command Reference

geocoding — Manage geocoding

• overpass-pp-cli geocoding — Keyless, but Nominatim requires a descriptive User-Agent and permits roughly one request per second.

overpass — Manage overpass

• overpass-pp-cli overpass query — The escape hatch. Accepts Overpass QL directly for queries the curated subject taxonomy does not cover.
• overpass-pp-cli overpass status — Returns plain text, not JSON. Useful for seeing whether a mirror has free slots before sending a large query.

Finding the right command

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

overpass-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 is worth shooting near here

overpass-pp-cli near --at "Los Angeles" --type water_tower --radius 25km

Maps the subject name to OSM tags, builds the query, and retries across mirrors.

See the tags behind a subject

overpass-pp-cli types --group industrial

Shows exactly which OpenStreetMap tags each subject type resolves to, so the search is inspectable.

Plan a drive around subjects

overpass-pp-cli route --from "Los Angeles" --to "Palm Springs" --type water_tower --corridor 12km

Builds a corridor between the two points and finds subjects inside it.

Narrow a verbose result payload

overpass-pp-cli near --at "Los Angeles" --type viewpoint --radius 20km --agent --select subjects.name,subjects.latitude,subjects.longitude

Overpass elements carry every OSM tag; --select with dotted paths returns only the fields needed.

Export for a map

overpass-pp-cli geojson --at "Los Angeles" --type viewpoint --radius 10km --out spots.geojson

Converts Overpass elements, whose coordinates live in different fields for nodes and ways, into standard GeoJSON.

Auth Setup

No credential at all. Both Overpass and Nominatim are keyless; they only require a descriptive User-Agent, which this CLI sends. Nominatim permits about one request per second and Overpass allocates a small number of concurrent slots per mirror, so the CLI paces itself and fails over rather than hammering one host.

Run overpass-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:

  overpass-pp-cli geocoding --q "San Pedro, CA" --agent --select id,name,status

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

• Offline-friendly — search --data-source local and export read the local SQLite store, which an explicit sync --resources ... populates. Bare sync is a no-op here: Overpass has no enumerable collections, and near/route/geojson do not persist their results.

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

• Use per-kind env vars only when a specific kind must diverge: OVERPASS_CONFIG_DIR, OVERPASS_DATA_DIR, OVERPASS_STATE_DIR, OVERPASS_CACHE_DIR.

• Resolution order is per-kind env var, --home, OVERPASS_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 overpass-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": {
      "overpass": {
        "command": "overpass-pp-mcp",
        "env": {
          "OVERPASS_HOME": "/srv/overpass"
        }
      }
    }
  }

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

overpass-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>", "overpass-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 `overpass-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; overpass-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 overpass-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.