Pp Seats Aero

Seats.aero — Printing Press CLI

Prerequisites: Install the CLI

This skill drives the seats-aero-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 seats-aero --cli-only
   

2. Verify: seats-aero-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/seats-aero/cmd/seats-aero-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.

seats-aero-pp-cli wraps all seven Partner API endpoints (cached search, bulk availability, trips, routes, destinations, refresh, live) with agent-native output, then syncs routes and availability into typed SQLite tables. That local store powers new-since, calendar, direct-scan, reach and a quota-guarded recheck, none of which a thin wrapper or the web app can do.

When to Use This CLI

Use seats-aero-pp-cli whenever an agent needs award-flight availability by mileage program: cheapest awards on a route, a program's calendar, nonstop reach from an airport, or verifying a cached seat before booking. It is the right tool for miles-and-points redemption research and for building a local award-availability corpus that can be queried offline.

Anti-triggers

Do not use this CLI for:

• Do not use it to book or ticket a flight; it reports availability only and never books.
• Do not use it for cash fares, hotel awards, points valuations, or transfer-partner math; the Partner API carries none of that.
• Do not use it to set push alerts; Seats.aero alerts are a web-app feature, use new-since on a schedule instead.
• Do not expect live search with a Pro key; that endpoint is commercial-agreement only.

Unique Capabilities

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

Local state that compounds

• new-since — See which award seats appeared on a route since you last looked, from your synced local data.

  Reach for this when the user asks what changed or what is new on a route, instead of re-running a full search and diffing by eye.

  seats-aero-pp-cli new-since --origin JFK --destination NRT --cabin business --since 24h --agent
  

• direct-scan — Find direct-flight award seats under a mileage ceiling across every synced program at once.

  Pick this over the awards search when the user wants nonstop-only results across programs from already-synced data with zero API calls.

  seats-aero-pp-cli direct-scan --origin JFK --destination NRT --cabin business --max-mileage 90000 --sources united,virginatlantic,aeroplan --agent
  

• calendar — Turn one route's synced availability into a date-by-cabin matrix you can scan at a glance.

  Use this when the user asks which dates have business or first availability on a route; it answers from the local store in one shot.

  seats-aero-pp-cli calendar --origin JFK --destination NRT --source united --start 2026-10-01 --end 2026-12-31 --agent
  

Quota-aware plumbing

• recheck — Re-verify aging award rows are still live right before booking. It performs one live quota probe per run (1 daily call), and --apply is refused when quota is unknown unless --ignore-quota is passed.

  Print-only by default: lists the aging rows it would refresh, their age, and the remaining daily quota (one probe call). Add --apply to spend refresh credits; --apply is refused when the quota is unknown unless --ignore-quota is passed.

  seats-aero-pp-cli recheck --origin JFK --destination NRT --cabin business --max-mileage 90000 --agent
  

• reach — Discover where your miles can take you nonstop from one airport, ranked by cost and cross-checked against real dated seats.

  Reach for this when the user has miles but no fixed destination. It has no local mode, and results is an object {origin, cabin, max_mileage, source, destinations[]} rather than a bare array. --confirm-live makes up to 10 extra live /search calls (opt-in; never under the test harness).

  seats-aero-pp-cli reach --origin JFK --cabin business --max-mileage 90000 --top 10 --agent
  

Command Reference

Upgrading from 2026.8.1

seats-aero-partner-search was renamed to awards; the old command remains a hidden, deprecated alias for one release. Its --cabin flag is now --cabins and accepts comma-separated values. Existing stores are migrated by the first read-write command, such as sync; doctor is read-only and reports migration_pending without changing the store.

The credential environment variable is now SEATS_AERO_API_KEY. SEATS_AERO_PARTNER_PARTNER_AUTHORIZATION and the TOML key aero_partner_partner_authorization are still honoured; doctor reports the selected credentials_location.

The store is migrated in place on the first read-write command (sync or doctor), while the legacy seats_aero_partner_search table is left untouched. sync --concurrency now defaults to 1 (was 4), and --timeout now defaults to 1m (was 30s). The MCP tool is now awards_cached-search.

availability — Manage availability

• seats-aero-pp-cli availability — Retrieve a large amount of availability objects from one specific mileage program.

awards — Manage awards

• seats-aero-pp-cli awards — Search Seats.aero cached award availability between one or more origin and destination airports, across one or more mileage programs.

destinations — Manage destinations

• seats-aero-pp-cli destinations — Returns the airports reachable from (or to) a single airport

live — Manage live

• seats-aero-pp-cli live — Commercial-agreement API keys only; Pro keys receive 403 -- do not retry. 5-15 s latency.

refresh — Manage refresh

• seats-aero-pp-cli refresh — Use this endpoint to refresh old cached data; credit-metered; the response quota block shows remaining refreshes; Pro keys only.

reach — Discover nonstop destinations within a mileage ceiling

• seats-aero-pp-cli reach — Has no local mode, and results is an object {origin, cabin, max_mileage, source, destinations[]} rather than a bare array. --confirm-live makes up to 10 extra live /search calls (opt-in; never under the test harness).

recheck — Re-verify aging award rows before booking

• seats-aero-pp-cli recheck — Print-only by default; performs one live quota probe per run (1 daily call). --apply is refused when the quota is unknown unless --ignore-quota is passed.

routes — Manage routes

• seats-aero-pp-cli routes — Get all origin-destination routes tracked for a mileage program.

trips — Manage trips

• seats-aero-pp-cli trips <id> — Retrieve flight-level information from an Availability object by its revalidation/trip ID from search or availability results.

Freshness Contract

This printed CLI owns bounded freshness only for registered store-backed read command paths. In --data-source auto mode, those paths check sync_state and may run a bounded refresh before reading local data. --data-source local never refreshes. --data-source live reads the API and does not mutate the local store. Set SEATS_AERO_NO_AUTO_REFRESH=1 to skip the freshness hook without changing source selection.

sync and sync --resources availability cannot populate availability because the endpoint requires source. Use seats-aero-pp-cli sync --resources availability --resource-param availability:source=<program> --since 7d once per program. The routes resource needs no parameter.

Covered paths:

• seats-aero-pp-cli availability
• seats-aero-pp-cli availability get
• seats-aero-pp-cli availability list
• seats-aero-pp-cli availability search
• seats-aero-pp-cli awards
• seats-aero-pp-cli awards get
• seats-aero-pp-cli awards list
• seats-aero-pp-cli awards search
• seats-aero-pp-cli destinations
• seats-aero-pp-cli destinations get
• seats-aero-pp-cli destinations list
• seats-aero-pp-cli destinations search
• seats-aero-pp-cli routes
• seats-aero-pp-cli routes get
• seats-aero-pp-cli routes list
• seats-aero-pp-cli routes search

When JSON output uses the generated provenance envelope, freshness metadata appears at meta.freshness. Treat it as current-cache freshness for the covered command path, not a guarantee of complete historical backfill or API-specific enrichment.

Finding the right command

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

seats-aero-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. --json (and other machine formats) keep that exit-2 contract and write {"matches":[]} on stdout so agents can inspect the envelope without treating a miss as success.

Recipes

Cheapest business awards across all programs, narrowed for an agent

seats-aero-pp-cli awards --origin-airport SFO --destination-airport NRT --cabins business --order-by lowest_mileage --take 10 --agent --select data.Date,data.Route.Source,data.JMileageCost,data.JDirect

One cached search across every program, with --select trimming the wide availability rows to the four fields that matter.

What appeared since yesterday

seats-aero-pp-cli new-since --origin JFK --destination NRT --cabin business --since 24h --agent

Diffs the local availability table on first_seen_at, so only genuinely new rows come back.

Verify before you book, without spending credits

seats-aero-pp-cli recheck --origin JFK --destination NRT --cabin business --max-mileage 90000 --agent

Print-only by default: lists the aging rows it would refresh, their age, and the remaining daily quota (one probe call). Add --apply to spend refresh credits.

Direct-only under 90k miles across three programs

seats-aero-pp-cli direct-scan --origin JFK --destination NRT --cabin business --max-mileage 90000 --sources united,virginatlantic,aeroplan --agent

A cross-program join over synced data that the one-program-per-call bulk endpoint cannot answer.

Where can 90k miles take me nonstop

seats-aero-pp-cli reach --origin JFK --cabin business --max-mileage 90000 --top 10 --agent

Fans out via destinations then confirms each candidate against dated seats. reach has no local mode, and results is an object {origin, cabin, max_mileage, source, destinations[]} rather than a bare array. --confirm-live makes up to 10 extra live /search calls (opt-in; never under the test harness).

Auth Setup

Seats.aero Pro subscribers create a Partner API key under Settings, then export it as SEATS_AERO_API_KEY. Keys are Pro-tier (1,000 calls per day, resets at midnight UTC; the X-RateLimit-Remaining header tracks it) or commercial-agreement keys. Pro keys cannot call live search and commercial keys cannot call refresh; doctor reports the remaining daily calls and which tier your key appears to be.

Run seats-aero-pp-cli doctor to verify setup.

Agent Mode

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

Global format flags share one contract on promoted, novel, sync, and --deliver paths:

• --json — one JSON document on stdout (sync progress events go to stderr)

• --compact — keep identity/status/timestamp fields; does not change the document vs stream shape

• --csv / --plain — tabular rows (collection envelopes unwrap to the row array)

• --quiet — one identity value per row, no envelope

• 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:

  seats-aero-pp-cli destinations --agent --select airport,business,economy
  

• 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 confirmation — --agent does not imply --yes; pass --yes separately only after the target, arguments, and side effects are clear

• Explicit retries — use --idempotent only when an already-existing create 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 SEATS_AERO_HOME=<dir> to relocate all four path kinds under one root.

• Use per-kind env vars only when a specific kind must diverge: SEATS_AERO_CONFIG_DIR, SEATS_AERO_DATA_DIR, SEATS_AERO_STATE_DIR, SEATS_AERO_CACHE_DIR.

• Resolution order is per-kind env var, --home, SEATS_AERO_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 seats-aero-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": {
      "seats-aero": {
        "command": "seats-aero-pp-mcp",
        "env": {
          "SEATS_AERO_HOME": "/srv/seats-aero"
        }
      }
    }
  }
  

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

seats-aero-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>", "seats-aero-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 `seats-aero-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; seats-aero-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 seats-aero-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:

seats-aero-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.
seats-aero-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).
seats-aero-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.

seats-aero-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

seats-aero-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.
• SEATS_AERO_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:

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

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

Outputs from doctor, agent-context, learnings, and feedback can contain local paths or free text and should not be delivered to third-party webhooks.

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.

seats-aero-pp-cli profile save briefing --json
seats-aero-pp-cli --profile briefing destinations
seats-aero-pp-cli profile list --json
seats-aero-pp-cli profile show briefing
seats-aero-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 seats-aero-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/seats-aero/cmd/seats-aero-pp-mcp@latest
   

2. Register with Claude Code:

   claude mcp add seats-aero-pp-mcp -- seats-aero-pp-mcp
   

3. Verify: claude mcp list

Direct Use

1. Check if installed: which seats-aero-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:

   seats-aero-pp-cli <command> [subcommand] [args] --agent
   

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