# Pp Sensortower > Every free Sensor Tower ranking, plus the rank history the dashboard forgets the moment you close the tab. Trigger phrases: `what rank is this app`, `who is new in the app store charts`, `why did this app climb the charts`, `compare this app on ios and android`, `top free apps in this category`, `use sensortower`, `run sensortower`. - Skill: `mvanhorn-printing-press-libra/pp-sensortower` (Agent Skill) - Install (CLI): `npx skillmds add mvanhorn-printing-press-libra/pp-sensortower` - Raw SKILL.md: https://api.skillmd.com/api/skills/mvanhorn-printing-press-libra/pp-sensortower/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - License: Apache-2.0 - Author: mvanhorn (https://skillmd.com/u/mvanhorn-printing-press-libra) - Updated: 2026-09-09 - Page: https://skillmd.com/skills/mvanhorn-printing-press-libra/pp-sensortower --- # Sensor Tower — Printing Press CLI ## Prerequisites: Install the CLI This skill drives the `sensortower-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 `$HOME/.local/bin` on macOS/Linux and `%LOCALAPPDATA%\Programs\PrintingPress\bin` on Windows: ```bash npx -y @mvanhorn/printing-press-library install sensortower --cli-only ``` 2. Verify: `sensortower-pp-cli --version` 3. Ensure the reported install directory is on `$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 `$PATH` instead: ```bash go install github.com/mvanhorn/printing-press-library/library/marketing/sensortower/cmd/sensortower-pp-cli@latest ``` If `--version` reports "command not found" after install, the runtime cannot see the binary directory on `$PATH`. Do not proceed with skill commands until verification succeeds. Sensor Tower's dashboard shows you a rank right now and keeps no history you can query. This CLI mirrors app profiles, top charts, and publisher portfolios into local SQLite, then answers the questions the web UI structurally cannot: what moved since last week (movers), which release actually shifted the chart (teardown), and how the same product stands on iOS versus Android (compare). Ranks are exact and free; the revenue estimates are coarse buckets, so the commands lean on rank movement and never dress a bucket up as a precise figure. ## When to Use This CLI Use this CLI for mobile app market intelligence questions grounded in store rankings: where an app sits in a category chart, how that position moved over time, which release preceded a move, and how a product compares across iOS and Android. It is strongest when the question involves change over time, because it keeps a local history that Sensor Tower's dashboard does not. It works on a free Sensor Tower account, and most commands need no login at all. ## Anti-triggers Do not use this CLI for: - Do not use this CLI for precise revenue or download numbers; upstream returns 1-significant-figure buckets and app owners report they can be off by more than half - Do not use this CLI for Sensor Tower's paid API (api.sensortower.com) — it targets the free dashboard surface and cannot mint or use an enterprise auth_token - Do not use this CLI for Ad Intelligence, Pathmatics ad creatives, Usage Intelligence, or web analytics; those are entitled products that return 401 on the free surface - Do not use this CLI for keyword rank tracking or ASO keyword research; those endpoints are not part of the reachable free surface - Do not use this CLI for high-volume bulk export; the upstream rate limit is roughly a dozen requests before a four-minute lockout ## Unique Capabilities These capabilities aren't available in any other tool for this API. ### History the dashboard never kept - **`movers`** — See which apps climbed, fell, or newly appeared in a category chart since the previous 'movers' run. _Reach for this when the question is 'who is new or moving in this category', which the dashboard cannot answer because it keeps no history._ ```bash sensortower-pp-cli movers 6015 --country US --agent ``` - **`teardown`** — Align an app's release timeline against its rank history to see which shipped version moved the needle. _Reach for this when a competitor spikes and you need to know whether a release, not luck, explains it._ ```bash sensortower-pp-cli teardown 460177396 --agent ``` - **`watch digest`** — Print every tracked app's current rank per chart plus its delta since the previous 'movers' run. _Reach for this for the recurring Monday check across a whole tracked set, instead of re-reading ranks app by app._ ```bash sensortower-pp-cli watch digest --agent ``` ### Exact ranks over fuzzy money - **`divergence`** — Find monetization outliers in a category by comparing each app's free-chart rank against its grossing-chart rank. _Reach for this to spot install-rich but monetization-poor apps (or the inverse) without trusting Sensor Tower's coarse revenue estimates._ ```bash sensortower-pp-cli divergence 6015 --country US --agent ``` ### Cross-platform - **`compare`** — Compare one product's iOS and Android standing side by side after resolving cross-platform identity. _Reach for this when asked which platform is winning for the same app; requires a session cookie via auth login --chrome._ ```bash sensortower-pp-cli compare 460177396 tv.twitch.android.app --agent ``` ## Command Reference **apps** — Look up app metadata by store ID across iOS, Android, and unified IDs - `sensortower-pp-cli apps android` — Look up one or more Android apps by package name - `sensortower-pp-cli apps get` — Full iOS app profile: exact category ranks, 400+ version history entries, in-app purchases, rating breakdown - `sensortower-pp-cli apps ios` — Look up several iOS apps at once by numeric App Store ID (leaner than 'apps get'; accepts multiple IDs) - `sensortower-pp-cli apps unified` — Resolve an app to its unified cross-platform record, joining iOS and Android identities **categories** — Reference data for category IDs and slugs - `sensortower-pp-cli categories` — Every valid iOS category ID and Android category slug, as published by Sensor Tower **category** — An app's rank history and per-category ranking summary - `sensortower-pp-cli category history` — Daily rank history for apps across chart types and countries over a date range - `sensortower-pp-cli category summary` — Which categories and chart types an app currently ranks in **find** — Find apps and publishers by name in the live store catalog - `sensortower-pp-cli find ` — Search the store catalog by name; returns matching apps or publishers with download and revenue signals **publishers** — Publisher portfolios and unified publisher identity - `sensortower-pp-cli publishers apps` — Every iOS app in a publisher's portfolio - `sensortower-pp-cli publishers unified` — Resolve a publisher to its unified cross-platform record **rankings** — Store top-chart rankings by category and country - `sensortower-pp-cli rankings android` — Top Google Play charts for a category and country on a given date - `sensortower-pp-cli rankings ios` — Top iOS charts (free/paid/grossing) for a category and country on a given date ## 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 `SENSORTOWER_NO_AUTO_REFRESH=1` to skip the freshness hook without changing source selection. Covered paths: - `sensortower-pp-cli categories` - `sensortower-pp-cli categories get` - `sensortower-pp-cli categories list` - `sensortower-pp-cli categories 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: ```bash sensortower-pp-cli which "" ``` `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 ### Find an app, then read its exact ranks ```bash sensortower-pp-cli find spotify --os ios --agent --select app_id,name ``` Search returns 99 entities; narrowing with --select keeps an agent's context small before picking an ID. ### Pull only the rank block from a 39KB profile ```bash sensortower-pp-cli apps get 460177396 --agent --select name,category_rankings,worldwide_last_month_downloads ``` The app hub object has 51 top-level keys including a 400+ entry version array; --select avoids burning context on fields you did not ask for. ### See which release moved the chart ```bash sensortower-pp-cli teardown 460177396 --agent ``` Aligns the versions timeline against locally stored rank history so you can see whether a ship date precedes a climb. ### Catch new entrants in a category ```bash sensortower-pp-cli movers 6015 --country US --agent ``` Set-differences the latest chart snapshot against the previous one; requires two 'movers' runs on the same category to have a baseline. ### Compare the same product across platforms ```bash sensortower-pp-cli compare 460177396 tv.twitch.android.app --agent ``` Resolves cross-platform identity via the cookie-gated unified endpoint, then shows both rank ladders side by side. ### Track apps, then digest them without spending requests ```bash sensortower-pp-cli watch add 460177396 --os ios && sensortower-pp-cli watch digest --agent ``` 'watch digest' makes zero API calls: it reports ranks from snapshots that 'movers' already stored, which matters against a ~12-request budget. ## Auth Setup Most of this CLI needs no credentials at all: app lookups, catalog search, top charts, rank history, and publisher portfolios all work anonymously. A Sensor Tower session cookie unlocks exactly one thing — the cross-platform unified endpoints behind 'apps unified', 'publishers unified', and 'compare'. Run 'auth login --chrome' to import that session from your browser. The upstream API is rate limited at roughly a dozen requests before returning 429 with about a four-minute recovery and no Retry-After header, so this CLI is cache-first by design: 'movers' and 'divergence' each cost exactly one request, 'movers' stores a chart snapshot locally as it goes, and 'watch digest' reads that stored history without calling the API at all. Run `sensortower-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: ```bash sensortower-pp-cli apps 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 - **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: ```json { "meta": {"source": "live" | "local", "synced_at": "...", "reason": "..."}, "results": } ``` 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 ` for one invocation, or set `SENSORTOWER_HOME=` to relocate all four path kinds under one root. - Use per-kind env vars only when a specific kind must diverge: `SENSORTOWER_CONFIG_DIR`, `SENSORTOWER_DATA_DIR`, `SENSORTOWER_STATE_DIR`, `SENSORTOWER_CACHE_DIR`. - Resolution order is per-kind env var, `--home`, `SENSORTOWER_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 `sensortower-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: ```json { "mcpServers": { "sensortower": { "command": "sensortower-pp-mcp", "env": { "SENSORTOWER_HOME": "/srv/sensortower" } } } } ``` Fleet precedence: an inherited per-kind env var overrides an explicit `--home` for that kind. Use `SENSORTOWER_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 `SENSORTOWER_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: ```bash sensortower-pp-cli recall "" --agent ``` The response envelope: ```json { "query": "...", "normalized": "", "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": ["", "sensortower-pp-cli learnings confirm 12"] } ], "playbook": { "query_family": "...", "playbook": { "steps": [ { "cmd": "", "purpose": "..." } ], "entity_slots": ["$ENTITY"], "expected_tool_calls": 3 }, "slots_resolved": { "$ENTITY": { "token": "", "canonical": "" } }, "notes": "" }, "notes": "" } ``` 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 ` only after the trial verified the behavior. Reject a wrong candidate with `learnings reject `. -> 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 `sensortower-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 ` prints the candidate's full payload before materializing it - check that the printed payload matches the behavior you verified. `learnings reject ` tombstones the derivation signature so the same candidate does not resurface. The envelope carries only the few candidates worth acting on now; `sensortower-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:` (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 `sensortower-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: ```bash sensortower-pp-cli teach --query "" --resource-type --resource --resource # (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: ```bash # Common case: record both the resource learning AND the playbook in one call. sensortower-pp-cli teach \ --query "" \ --resource \ --playbook-file ~/playbooks/.json \ --playbook-notes-file ~/playbooks/-notes.md # (append shell `&` to background it) # Alternate: playbook-only (no resource to record alongside). sensortower-pp-cli teach-playbook \ --query "" \ --playbook-file ~/playbooks/.json \ --notes-file ~/playbooks/-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`. ```bash sensortower-pp-cli playbook amend \ --query "" \ --add-note "" # (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]: `). 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 `sensortower-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. - `SENSORTOWER_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: ``` sensortower-pp-cli feedback "the --since flag is inclusive but docs say exclusive" sensortower-pp-cli feedback --stdin < notes.txt sensortower-pp-cli feedback list --json --limit 10 ``` Entries are stored locally as `feedback.jsonl` under the resolved data dir. They are never POSTed unless `SENSORTOWER_FEEDBACK_ENDPOINT` is set AND either `--send` is passed or `SENSORTOWER_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 `. 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:` | Atomically write output to `` (tmp + rename) | | `webhook:` | 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. ``` sensortower-pp-cli profile save briefing --json sensortower-pp-cli --profile briefing apps get mock-value sensortower-pp-cli profile list --json sensortower-pp-cli profile show briefing sensortower-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 `sensortower-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: ```bash go install github.com/mvanhorn/printing-press-library/library/marketing/sensortower/cmd/sensortower-pp-mcp@latest ``` 2. Register with Claude Code: ```bash claude mcp add sensortower-pp-mcp -- sensortower-pp-mcp ``` 3. Verify: `claude mcp list` ## Direct Use 1. Check if installed: `which sensortower-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: ```bash sensortower-pp-cli [subcommand] [args] --agent ``` 4. If ambiguous, drill into subcommand help: `sensortower-pp-cli --help`.