pp-faa-registry

name pp-faa-registry description Look up any US aircraft by tail number from the terminal — live FAA registry inquiries plus a daily-synced offline copy of the full registry for fleet queries, Mode S hex decoding, and expiration alerts no other tool has. author Omar Shahine license Apache-2.0 argument-hint <command> [args] | install cli|mcp allowed-tools Read Bash metadata {"openclaw":{"requires":{"bins":"[Truncated]"},"install":["[Truncated]"]}} Faa Registry — Printing Press CLI Prerequisites: Install the CLI This skill drives the faa-registry-pp-cli binary. You must verify the CLI is installed before invoking any command from this skill. If it is missing, install it first: 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 faa-registry --cli-only Verify: faa-registry-pp-cli --version 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 1.26.5 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/faa-registry/cmd/faa-registry-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. CLI for the FAA Civil Aviation Registry. Look up any US-registered aircraft by N-number, serial number, owner name, make/model, engine, dealer, or state/county via the live registry.faa.gov inquiry app — and sync the FAA's daily Releasable Aircraft Database (all ~315K active registrations, ~383K deregistered records, ~126K reserved N-numbers, plus aircraft-model and engine reference data) into a local SQLite store for instant offline search, fleet reports, Mode S hex decoding, and expiring-registration alerts. 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. Command Reference aircraft — Live FAA registry lookups for individual aircraft (registration detail pages). faa-registry-pp-cli aircraft by-serial — Find aircraft by manufacturer serial number. faa-registry-pp-cli aircraft lookup — Look up an aircraft's full registration record by N-number (tail number, with or without the leading N). dealers — Live dealer-certificate searches. faa-registry-pp-cli dealers — Search FAA dealer certificates by dealer name. documents — Live document-index searches (recorded documents for collateral like airframes and engines). faa-registry-pp-cli documents — Search the FAA document index by collateral identifier. engines — Live engine-reference searches. faa-registry-pp-cli engines — Search the engine reference table by engine manufacturer and model. models — Live registry searches by aircraft make/model and reference data. faa-registry-pp-cli models — Search the aircraft model reference by manufacturer and model name owners — Live registry searches by registered owner name. faa-registry-pp-cli owners — List all aircraft registered to an owner name (paginated). regions — Live registry searches by geography. faa-registry-pp-cli regions by-country — List US-registered aircraft whose owners are located in a given country. faa-registry-pp-cli regions by-state — List aircraft registered in a state and county (paginated). Finding the right command When you know what you want to do but not which command does it, ask the CLI directly: faa-registry-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. Auth Setup No authentication required. Run faa-registry-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: faa-registry-pp-cli dealers --name example-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 FAA_REGISTRY_HOME=<dir> to relocate all four path kinds under one root. Use per-kind env vars only when a specific kind must diverge: FAA_REGISTRY_CONFIG_DIR , FAA_REGISTRY_DATA_DIR , FAA_REGISTRY_STATE_DIR , FAA_REGISTRY_CACHE_DIR . Resolution order is per-kind env var, --home , FAA_REGISTRY_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 faa-registry-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" : { "faa-registry" : { "command" : "faa-registry-pp-mcp" , "env" : { "FAA_REGISTRY_HOME" : "/srv/faa-registry" } } } } Fleet precedence: an inherited per-kind env var overrides an explicit --home for that kind. Use FAA_REGISTRY_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 FAA_REGISTRY_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: faa-registry-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>" , "faa-registry-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 `faa-registry-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; faa-registry-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 faa-registry-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: faa-registry-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. faa-registry-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)