# Pp Screener > Every Screener.in feature, plus offline search, screen intersections, and a local fundamentals mirror no other tool has. Trigger phrases: `check fundamentals for INFY`, `run the bull cartel screen`, `who's buying insider stock this month`, `compare TCS and Wipro`, `latest quarterly results`, `screener data`, `use screener.in`. - Skill: `mvanhorn-printing-press-libra/pp-screener` (Agent Skill) - Install (CLI): `npx skillmds add mvanhorn-printing-press-libra/pp-screener` - Raw SKILL.md: https://api.skillmd.com/api/skills/mvanhorn-printing-press-libra/pp-screener/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-screener --- # Screener.in — Printing Press CLI ## Prerequisites: Install the CLI This skill drives the `screener-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 `$HOME/.local/bin` on macOS/Linux and `%LOCALAPPDATA%\Programs\PrintingPress\bin` on Windows: ```bash npx -y @mvanhorn/printing-press-library install screener --cli-only ``` 2. Verify: `screener-pp-cli --version` 3. Ensure the reported install directory is on `$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 `$PATH` instead: ```bash go install github.com/mvanhorn/printing-press-library/library/other/screener/cmd/screener-pp-cli@latest ``` If `--version` reports "command not found" after install, the runtime cannot see the binary directory on `$PATH`. Do not proceed with skill commands until verification succeeds. Search any Indian company and pull its full fundamental profile — key metrics, machine-generated pros/cons, quarterly results, P&L, balance sheet, cash flow, ratios, shareholding, peers, and price charts — from the terminal. Sync company pages, screens, and insider trades into a local SQLite mirror, then run cross-company comparisons, quarterly trend flags, screen overlaps, and insider-flow rankings that no single page or API call provides. ## When to Use This CLI Use this CLI for Indian equity fundamental research: checking a company's full profile, screening the market by a strategy, comparing candidates, monitoring quarterly earnings trends, tracking insider activity, and building a local fundamentals mirror for offline or agent-driven analysis. ## Anti-triggers Do not use this CLI for: - Do not use for intraday price action or live quotes — Screener.in data updates at market close, not real time - Do not use for option chain or derivatives data — Screener.in covers equities only - Do not use for US or non-Indian markets — Screener.in covers NSE/BSE listed companies ## Unique Capabilities These capabilities aren't available in any other tool for this API. ### Local state that compounds - **`compare`** — See two to four companies' fundamentals side by side (valuation, margins, growth, insider activity) without tab-switching. _Pick this when an agent needs to decide between candidate stocks on comparable fundamentals instead of fetching pages one at a time._ ```bash screener-pp-cli compare TCS HDFCBANK --agent ``` - **`qtrend`** — Spot whether a company's quarterly profit/sales growth is accelerating or deteriorating, with YOY change and margin drift computed automatically. _Pick this when an agent needs the trend shape of a company's earnings, not just the latest quarter's raw numbers._ ```bash screener-pp-cli qtrend INFY --quarters 8 --agent ``` - **`insider-flow`** — Answer 'who is net buying the most this month?' with per-company insider buy/sell aggregation. _Pick this when an agent needs insider conviction ranked by net value, not a raw list of individual trades._ ```bash screener-pp-cli insider-flow --since 30d --top 10 --agent ``` ### Cross-entity local queries - **`overlap`** — Find companies that appear in two or more stock screens in one command, replacing spreadsheet dedup. _Pick this when an agent needs the intersection of multiple screening strategies to find high-conviction candidates._ ```bash screener-pp-cli overlap 1 the-bull-cartel 59 magic-formula --agent ``` - **`rank`** — Re-score a screen's companies with a composite of fundamentals (P/E, ROCE, growth), sorted by what matters to you. _Pick this when an agent should prioritize within a screen by fundamentals or insider conviction rather than the default table order._ ```bash screener-pp-cli rank 1 the-bull-cartel --by roce --agent ``` ## Command Reference **company** — Search companies and fetch fundamental profiles - `screener-pp-cli company by-id` — Fetch a company page by numeric ID (redirects to symbol page) - `screener-pp-cli company chart` — Fetch price/technical chart data for a company - `screener-pp-cli company peers` — Fetch peer comparison table for a company - `screener-pp-cli company profile` — Fetch the full company profile page (key metrics, analysis, quarterly results, P&L, balance sheet, cash flow, ratios - `screener-pp-cli company profile-standalone` — Fetch standalone (parent-only) company profile page - `screener-pp-cli company search` — Search for companies by name or ticker (live autocomplete API) **explore** — Browse popular screens and sector categories - `screener-pp-cli explore` — Browse popular themes, formulas, and sector categories **filings** — Market pulse filings hub (requires login) - `screener-pp-cli filings` — Market Pulse hub: bulk deals, block deals, SAST trades, insider trades **full_text_search** — Full-text search across companies and filings (requires login) - `screener-pp-cli full-text-search` — Full-text search for companies **ipo** — Upcoming and recent IPO listings - `screener-pp-cli ipo` — List upcoming IPOs with subscription status **market** — Browse market sectors and industry groups - `screener-pp-cli market ` — List companies in a market sector (e.g. IN08/IN0801/IN080101 = IT - Software) **results** — Latest quarterly results (Market Pulse, requires login) - `screener-pp-cli results` — Latest quarterly results with YOY growth (Sales, EBIDT, Net profit, EPS) and filters **screens** — Browse and run stock screening screens - `screener-pp-cli screens list` — List all stock screening screens - `screener-pp-cli screens run` — Run a stock screen and get ranked results (CMP, P/E, Mar Cap, Div Yld, NP Qtr, Qtr Profit Var, Sales Qtr, Qtr Sales Var **trades** — Market pulse trade activity (requires login) - `screener-pp-cli trades` — Insider trades (bought/sold/pledge/ESOP) with filters ## 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 `SCREENER_NO_AUTO_REFRESH=1` to skip the freshness hook without changing source selection. Covered paths: - `screener-pp-cli explore` - `screener-pp-cli explore get` - `screener-pp-cli explore list` - `screener-pp-cli explore search` - `screener-pp-cli filings` - `screener-pp-cli filings get` - `screener-pp-cli filings list` - `screener-pp-cli filings search` - `screener-pp-cli full_text_search` - `screener-pp-cli full_text_search get` - `screener-pp-cli full_text_search list` - `screener-pp-cli full_text_search search` - `screener-pp-cli ipo` - `screener-pp-cli ipo get` - `screener-pp-cli ipo list` - `screener-pp-cli ipo search` - `screener-pp-cli screens` - `screener-pp-cli screens get` - `screener-pp-cli screens list` - `screener-pp-cli screens 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 screener-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 ### Full company research brief ```bash screener-pp-cli company profile RELIANCE --agent --select top_ratios,analysis,quarterly_results ``` One command returns the key metrics, pros/cons, and quarterly results as agent-ready JSON ### High-conviction screen intersection ```bash screener-pp-cli overlap 1 the-bull-cartel 59 magic-formula --agent ``` Companies passing both a value formula and a growth screen ### Insider conviction ranking ```bash screener-pp-cli insider-flow --since 30d --top 10 --agent ``` Who's net buying the most this month, ranked by value ### Earnings momentum check ```bash screener-pp-cli qtrend INFY --quarters 8 --agent ``` Is profit growth accelerating or deteriorating over 8 quarters? ### Candidate shortlist comparison ```bash screener-pp-cli compare TCS WIPRO HCLTECH --agent --select name,pe,roce,profit_growth ``` Side-by-side fundamentals for a shortlist, narrowed with --select ## Auth Setup Screener.in's public pages (company profiles, screens, sectors, IPO) need no login. Market-pulse pages — latest quarterly results, insider trades, filings — require a free Screener.in account. Run 'screener-pp-cli auth login --chrome' to import your logged-in Chrome session cookie once; the CLI replays it for gated pages. Run `screener-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 screener-pp-cli company search --q example-value --agent --select id,name,url ``` - **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 `SCREENER_HOME=` to relocate all four path kinds under one root. - Use per-kind env vars only when a specific kind must diverge: `SCREENER_CONFIG_DIR`, `SCREENER_DATA_DIR`, `SCREENER_STATE_DIR`, `SCREENER_CACHE_DIR`. - Resolution order is per-kind env var, `--home`, `SCREENER_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 `screener-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": { "screener": { "command": "screener-pp-mcp", "env": { "SCREENER_HOME": "/srv/screener" } } } } ``` Fleet precedence: an inherited per-kind env var overrides an explicit `--home` for that kind. Use `SCREENER_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 `SCREENER_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 screener-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": ["", "screener-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 `screener-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; `screener-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 `screener-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 screener-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. screener-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). screener-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 screener-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 `screener-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. - `SCREENER_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: ``` screener-pp-cli feedback "the --since flag is inclusive but docs say exclusive" screener-pp-cli feedback --stdin < notes.txt screener-pp-cli feedback list --json --limit 10 ``` Entries are stored locally as `feedback.jsonl` under the resolved data dir. They are never POSTed unless `SCREENER_FEEDBACK_ENDPOINT` is set AND either `--send` is passed or `SCREENER_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. ``` screener-pp-cli profile save briefing --json screener-pp-cli --profile briefing company search --q example-value screener-pp-cli profile list --json screener-pp-cli profile show briefing screener-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 `screener-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/other/screener/cmd/screener-pp-mcp@latest ``` 2. Register with Claude Code: ```bash claude mcp add screener-pp-mcp -- screener-pp-mcp ``` 3. Verify: `claude mcp list` ## Direct Use 1. Check if installed: `which screener-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 screener-pp-cli [subcommand] [args] --agent ``` 4. If ambiguous, drill into subcommand help: `screener-pp-cli --help`.