pp-flight-goat

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Flight Goat — Printing Press CLI

Prerequisites: Install the CLI

This skill drives the flight-goat-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 flight-goat --cli-only
   

2. Verify: flight-goat-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 1.26.5 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/flight-goat/cmd/flight-goat-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.

Introduction

AeroAPI is a simple, query-based API that gives software developers access to a variety of FlightAware's flight data. Users can obtain current or historical data. AeroAPI is a RESTful API delivering accurate and actionable aviation data. With the introduction of Foresight™, customers have access to the data that powers over half of the predictive airline ETAs in the US.

Categories

AeroAPI is divided into several categories to make things easier to discover.

• Flights: Summary information, planned routes, positions and more
• Foresight: Flight positions enhanced with FlightAware Foresight™
• Airports: Airport information and FIDS style resources
• Operators: Operator information and fleet activity resources
• Alerts: Configure flight alerts and delivery destinations
• History: Historical flight access for various endpoints
• Miscellaneous: Flight disruption, future schedule information, and aircraft owner information

Development Tools

AeroAPI is defined using the OpenAPI Spec 3.0, which means it can be easily imported into tools like Postman. To get started try importing the API specification using Postman's instructions. Once imported as a collection only the "Value" field under the collection's Authorization tab needs to be populated and saved before making calls.

The AeroAPI OpenAPI specification is located at:
https://flightaware.com/commercial/aeroapi/resources/aeroapi-openapi.yml

Our open source AeroApps project provides a small collection of services and sample applications to help you get started.

The Flight Information Display System (FIDS) AeroApp is an example of a multi-tier application using multiple languages and Docker containers. It demonstrates connectivity, data caching, flight presentation, and leveraging flight maps.

The Alerts AeroApp demonstrates the use of AeroAPI to set, edit, and receive alerts in a sample application with a Dockerized Python backend and a React frontend.

Our AeroAPI push notification testing interface provides a quick and easy way to test the delivery of customized alerts via AeroAPI push.

Command Reference

aircraft — Manage aircraft

• flight-goat-pp-cli aircraft <type> — Returns information about an aircraft type, given an ICAO aircraft type designator string.

airports — Manage airports

• flight-goat-pp-cli airports get — Returns information about an airport given an ICAO or LID airport code such as KLAX, KIAH, O07, etc.
• flight-goat-pp-cli airports get-all — Returns the ICAO identifiers of all known airports.
• flight-goat-pp-cli airports get-delays-for-all — Returns a list of airports with delays.
• flight-goat-pp-cli airports get-nearby — Returns a list of airports located within a given distance from the given location.

alerts — AeroAPI alerting can be used to configure and receive real-time alerts on key flight events. With customizable alerting offered by our alert endpoints, AeroAPI empowers users to selectively pick various types of events/filters to alert on. By doing so, you can receive specially tailored alerts delivered to you for events such as flight plan filed, flight departure (out and off), flight arrival (on and in), and more!

To get started with alerting, the PUT /alerts/endpoint endpoint must first be used to set up the account-wide default URL that alerts will be delivered to. This step must be done before any alerts can be configured and will serve as the fallback URL that all alerts will be sent to for the account if a specific delivery URL is not designated on a particular alert. If this is not performed before configuring alerts, then you will receive a 400 error with an error message reminding you of this step when trying to interact with the POST /alerts endpoint. Once a URL is set via the PUT /alerts/endpoint endpoint, then alerts can be configured using the POST /alerts endpoint. The GET /alerts endpoint can also be used to retrieve all currently configured alerts associated with your AeroAPI key. The GET /alerts endpoint will allow you to easily retrieve the id of any specific alerts of interest configured for the account which can let you use the GET PUT and DELETE /alerts/{id} endpoints to retrieve, update, and delete specific alerts.

When configuring an individual alert, the target_url field can be set to a URL that’s different than the account-wide target endpoint set via the PUT /alerts/endpoint. If the target_url field is set on an alert, then that specific alert will be delivered to the specified target_url rather than the default account-wide one. If this field is not configured for the alert, then the alert will be delivered to the default account-wide endpoint. By setting this field, one can easily target different alerts to be received by different endpoints which can be useful for configuring per-application alerts or sending alerts to an alternate development environment without having to adjust a production alert configuration.

For each alert configured, one-to-many ‘events’ can be set for alert delivery. While most events will result in one alert delivery, both the arrival and the departure events can result in multiple alerts delivered (referred to as bundled). The departure event bundles the departure (actual OFF the ground) alert, along with the flight plan filed alert and up to 5 per-departure changes which can include alerts for significant departure delays of over 30 minutes, gate changes, and airport delays. FlightAware Global customers will also receive Power on and Ready to taxi alerts as part of the departure bundle. The arrival event bundles the arrival (actual ON the ground) alert, along with up to 5 en-route changes (including delays of over 30 minutes and excluding diversions) identified. FlightAware Global customers will also receive taxi stop times as part of the arrival bundle. Setting a bundled type and unbundled type for an On/Off will only result in a single alert in the case where events may overlap.

If there is a need to change the alert configurations, updating an alert using the PUT /alerts/{id} endpoint and a unique alert identifier (id) is preferred rather than creating an additional alert. By doing so, you can avoid duplicate alerts being delivered which could create unnecessary noise if they are not of interest anymore.

If at any point there is a need to delete an alert, the DELETE alerts/{id} endpoint can be leveraged to delete an alert so that it won’t be delivered anymore. As a reminder, specific alert IDs can be retrieved from the GET /alerts endpoint.

• flight-goat-pp-cli alerts create — Create a new AeroAPI flight alert.
• flight-goat-pp-cli alerts delete — Deletes specific alert with given ID
• flight-goat-pp-cli alerts delete-endpoint — Remove the default account-wide URL that will be POSTed to for alerts that are not configured with a specific URL.
• flight-goat-pp-cli alerts get — Returns the configuration data for an alert with the specified ID.
• flight-goat-pp-cli alerts get-all — Returns all configured alerts for the FlightAware account (this includes alerts configured through other means by the
• flight-goat-pp-cli alerts get-endpoint — Returns URL that will be POSTed to for alerts that are delivered via AeroAPI.
• flight-goat-pp-cli alerts set-endpoint — Updates the default URL that will be POSTed to for alerts that are delivered via AeroAPI.
• flight-goat-pp-cli alerts update — Modifies the configuration for an alert with the specified ID.

disruption-counts — Manage disruption counts

• flight-goat-pp-cli disruption-counts get — Returns flight cancellation/delay counts in the specified time period for a particular airline or airport.
• flight-goat-pp-cli disruption-counts get-all — Returns overall flight cancellation/delay counts in the specified time period for either all airlines or all airports.

flights — Manage flights

• flight-goat-pp-cli flights get — Returns the flight info status summary for a registration, ident, or fa_flight_id.
• flight-goat-pp-cli flights get-by-advanced-search — Returns currently or recently airborne flights based on geospatial search parameters.
• flight-goat-pp-cli flights get-by-position-search — Returns flight positions based on geospatial search parameters.
• flight-goat-pp-cli flights get-by-search — Search for airborne flights by matching against various parameters including geospatial data.
• flight-goat-pp-cli flights get-count-by-search — Full search query documentation is available at the /flights/search endpoint.

foresight — Foresight endpoints provide access to FlightAware's Foresight predictive models and predictions for key events. Our advanced machine learning (ML) models identify key influencing factors for a flight to forecast future events in real-time, providing unprecedented insight to improve operational efficiencies and facilitate better decision-making in the air and on the ground. To learn more about the power of Foresight, visit https://www.flightaware.com/commercial/foresight/

These endpoints each mirror a non-Foresight equivalent endpoint of similar functionality, with the addition of all the ML 'predicted' values included in the Foresight response. The respective non-Foresight endpoint response includes a flag, 'foresight_predictions_available', which can optionally be used as a trigger to obtain and leverage Foresight predictions on an as-needed basis and manage cost. Foresight is only available to Premium tier customers. Contact integrationsales@flightaware.com for more information, pricing details, and to have your account enabled for Foresight.

• flight-goat-pp-cli foresight get-flight-position-with — Get flight's current position, including Foresight data
• flight-goat-pp-cli foresight get-flight-with — Returns the flight info status summary for a registration, ident, or fa_flight_id
• flight-goat-pp-cli foresight get-flights-by-advanced-search-with — Returns currently or recently airborne flights based on geospatial search parameters.

history — Manage history

• flight-goat-pp-cli history get-aircraft-last-flight — Returns flight info status summary for an aircraft's last known flight given its registration.
• flight-goat-pp-cli history get-flight — Returns historical flight info status summary for a registration, ident, or fa_flight_id.
• flight-goat-pp-cli history get-flight-map — Returns a historical flight's track as a base64-encoded image.
• flight-goat-pp-cli history get-flight-route — Returns information about a historical flight's filed route including coordinates, names
• flight-goat-pp-cli history get-flight-track — Returns the track for a historical flight as an array of positions.

operators — Manage operators

• flight-goat-pp-cli operators get — Returns information for an operator such as their name, ICAO/IATA codes, headquarter location, etc.
• flight-goat-pp-cli operators get-all — Returns list of operator references (ICAO/IATA codes and URLs to access more information).

schedules — Manage schedules

• flight-goat-pp-cli schedules — Returns scheduled flights that have been published by airlines.

Finding the right command

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

flight-goat-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

Run flight-goat-pp-cli auth setup to print the URL and steps for getting a key (add --launch to open the URL). Then set:

export FLIGHT_GOAT_API_KEY="<your-key>"

To persist credentials, use flight-goat-pp-cli auth set-token <token>. Stored secrets live in credentials.toml under the data dir, not in config.toml.

Run flight-goat-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:

  flight-goat-pp-cli airports get 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

• Explicit retries — use --idempotent only when an already-existing create should count as success, and use --ignore-missing only when a missing delete target should count as success

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

• Use per-kind env vars only when a specific kind must diverge: FLIGHT_GOAT_CONFIG_DIR, FLIGHT_GOAT_DATA_DIR, FLIGHT_GOAT_STATE_DIR, FLIGHT_GOAT_CACHE_DIR.

• Resolution order is per-kind env var, --home, FLIGHT_GOAT_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 flight-goat-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": {
      "flight-goat": {
        "command": "flight-goat-pp-mcp",
        "env": {
          "FLIGHT_GOAT_HOME": "/srv/flight-goat"
        }
      }
    }
  }
  

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

flight-goat-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>", "flight-goat-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 `flight-goat-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; flight-goat-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 flight-goat-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:

flight-goat-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.
flight-goat-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).
flight-goat-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.

flight-goat-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

flight-goat-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.
• FLIGHT_GOAT_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:

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

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

Sink	Effect
stdout	Default; write to stdout only
file:<path>	Atomically write output to <path> (tmp + rename)
webhook:<url>	POST the output body to the URL (application/json or application/x-ndjson when --compact)

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.

flight-goat-pp-cli profile save briefing --json
flight-goat-pp-cli --profile briefing airports get mock-value
flight-goat-pp-cli profile list --json
flight-goat-pp-cli profile show briefing
flight-goat-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

Code	Meaning
0	Success
2	Usage error (wrong arguments)
3	Resource not found
4	Authentication required
5	API error (upstream issue)
7	Rate limited (wait and retry)
10	Config error

Argument Parsing

Parse $ARGUMENTS:

1. Empty, help, or --help → show flight-goat-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/travel/flight-goat/cmd/flight-goat-pp-mcp@latest
   

2. Register with Claude Code:

   claude mcp add flight-goat-pp-mcp -- flight-goat-pp-mcp
   

3. Verify: claude mcp list

Direct Use

1. Check if installed: which flight-goat-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:

   flight-goat-pp-cli <command> [subcommand] [args] --agent
   

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