Pp Flighty

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Flighty Airports — Printing Press CLI

Prerequisites: Install the CLI

This skill drives the flighty-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 flighty --cli-only

2. Verify: flighty-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/travel/flighty/cmd/flighty-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.

Live status, METAR weather, performance, and flight boards for 156 tracked airports — parsed straight from Flighty's SSR payload with no app install, no API key, and no login. Then it goes further than the site: network-wide rankings, airline footprints, route checks, healthy alternates, and change diffs, all from a local SQLite mirror that keeps working offline.

When to Use This CLI

When you or an agent need live airport disruption status, METAR weather, flight boards, or cross-airport disruption intelligence from the terminal. Ideal for pre-flight checks, travel-agent sweeps, meltdown-day writeups, and automation that needs JSON airport data without a private API.

Anti-triggers

Do not use this CLI for:

• Tracking a specific flight's live position — use Flighty's app or a flight-tracking API like AeroAPI; this CLI covers airport-level status only
• Flight booking or price search — use a booking API; Flighty Airports has no pricing data
• Personal flight history — the community Flighty MCP servers read your app database; this CLI is public-airport-data only
• Airports not on the meltdown map — the site tracks 156 airports; unlisted airports are unavailable

Unique Capabilities

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

Cross-airport intelligence

• airports worst — Rank the network's airports right now by cumulative delay and cancellations — the web map color-codes but never ranks.

  When an agent needs 'which airports are worst right now', this answers in one command instead of scraping the map.

  flighty-pp-cli airports worst --region Europe --limit 5 --json

• airports airline — One airline's delay/cancel/divert footprint aggregated across every synced airport — impossible per-airport on the site.

  Travel agents and ops folks ask 'which airline is the worst offender across my hubs today' — this answers it network-wide.

  flighty-pp-cli airports airline UA --json

• airports compare — Side-by-side status, delays, warnings, and flight rules for two airports.

  Choosing between alternates (SFO vs OAK) is a two-entity question the site cannot express.

  flighty-pp-cli airports compare sfo oak --json

• airports route — Both directions of one origin-destination pair's delay/cancel/divert stats, joined from each side's disrupted routes.

  Route data exists only from one airport's perspective; this shows both ends of the same route.

  flighty-pp-cli airports route sfo den --json

• airports nearby — Distance-ranked nearby airports, flagging which have normal operations right now.

  When your airport is melting down, 'where else can I fly from' is the immediate follow-up.

  flighty-pp-cli airports nearby sfo --healthy-only --limit 3 --json

Flight board intelligence

• airports find-flight — Find one flight by number across arrivals and departures boards — status, original vs actual time, gate, belt, terminal.

  The 'when is my flight actually leaving' question answered in one deterministic command.

  flighty-pp-cli airports find-flight den UA5072 --json

Local state that compounds

• airports diff — What changed since the last sync: status transitions, new/cleared warnings, delay deltas.

  Monitors and writers need 'what changed since this morning' — the numbers reset upstream, local history persists.

  flighty-pp-cli airports diff --json

Discovery Signals

This CLI was generated with browser-observed traffic context.

• Capture coverage: 0 API entries from 6 total network entries
• Protocols: html_scrape (55% confidence)
• Auth signals: none

Command Reference

airports — Airport status, delays, weather, and flight boards

• flighty-pp-cli airports arrivals — Show the live arrivals board for an airport
• flighty-pp-cli airports departures — Show the live departures board for an airport
• flighty-pp-cli airports list — List the tracked airports with live delay status (meltdown map catalog)
• flighty-pp-cli airports show — Show live status, weather, and performance for one airport
• flighty-pp-cli airports tv — List disrupted airports from the TV dashboard (same catalog, status-sorted)

Finding the right command

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

flighty-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

Morning meltdown sweep

flighty-pp-cli airports list --status MAJOR_ISSUES --json --select iata,city,status,warnings

One line of JSON naming every airport in serious trouble right now.

Pre-flight check

flighty-pp-cli airports show den --json --select airportWeather.flightRulesTitle,today.departurePerformance.onTime.percentage,warnings

Flight rules, on-time rate, and active warnings for your departure airport before you leave.

Find your flight's real departure

flighty-pp-cli airports find-flight den UA5072 --json

Status, original vs actual time, and gate for one flight number across both boards.

Rank Europe's worst hubs

flighty-pp-cli airports worst --region Europe --limit 5 --json

Magnitude-ranked by cumulative delay and cancellations — the web map never ranks.

Where else can I fly from

flighty-pp-cli airports nearby sfo --healthy-only --limit 3 --json

Nearest airports with normal operations when your home airport is melting down.

Auth Setup

No authentication required.

Run flighty-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:

  flighty-pp-cli airports list --agent --select id,slug,name

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

• Use per-kind env vars only when a specific kind must diverge: FLIGHTY_CONFIG_DIR, FLIGHTY_DATA_DIR, FLIGHTY_STATE_DIR, FLIGHTY_CACHE_DIR.

• Resolution order is per-kind env var, --home, FLIGHTY_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 flighty-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": {
      "flighty": {
        "command": "flighty-pp-mcp",
        "env": {
          "FLIGHTY_HOME": "/srv/flighty"
        }
      }
    }
  }

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

flighty-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>", "flighty-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 `flighty-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; flighty-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 flighty-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.