Pp Openrouter

OpenRouter — Printing Press CLI

Prerequisites: Install the CLI

This skill drives the openrouter-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 openrouter --cli-only
   

2. Verify: openrouter-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 before this CLI has a public-library category, install Node or use the category-specific Go fallback after publish.

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.

Every other OpenRouter tool wraps chat; this one governs it. A synced local mirror of models, generations, and credits powers per-cron cost attribution (usage cost-by), pre-flight budget gates (budget check), degradation watches (providers degraded), and runway projections (credits runway) that no single API call can answer. The full endpoint surface — catalog, keys admin, activity, analytics — ships alongside as typed commands.

When to Use This CLI

Reach for this CLI when the task is operating LLM workloads over OpenRouter: attributing spend to crons or agents, gating scheduled runs on budgets, shortlisting models by capability and price, watching provider health before dispatch, or projecting credit and cap runway. It is the right tool whenever the question spans local history plus the API — anything a single raw HTTP call cannot answer.

Anti-triggers

Do not use this CLI for:

• Interactive chat sessions or a conversational playground — use a chat client; the chat command here is agent plumbing, not a UX
• Building application inference paths — use the official OpenRouter SDKs in your app, not shell-outs to a CLI
• Org member management, SCIM provisioning, or SSO setup — use the OpenRouter dashboard
• Real-time token streaming displays — the CLI returns complete responses, not stream renderers

Unique Capabilities

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

Catalog intelligence

• models query — Shortlist models by capability, price, and context window with a structured query over the synced catalog — offline and instant.

  Reach for this instead of fetching the full catalog: it answers a shortlist question in ~200 tokens instead of hundreds of KB.

  openrouter-pp-cli models query "tools=true cost.completion<5 ctx>=128k" --agent
  

• models churn — See what changed in the model catalog between syncs: additions, removals, and repricings with deltas.

  Use this to catch a pinned model repricing or vanishing before the invoice does.

  openrouter-pp-cli models churn --since 7d --agent
  

Cost governance for agent fleets

• usage cost-by — Attribute spend to the cron, agent, or lineage that incurred it, over any window.

  Use this when the question is 'which job burned the money', not 'which model'.

  openrouter-pp-cli usage cost-by --since 7d --agent
  

• usage anomaly — Flag per-model cost spikes against the trailing baseline before they compound.

  Run this in a daily cron to catch a misbehaving lineage the day it regresses, not at invoice time.

  openrouter-pp-cli usage anomaly --since 24h --agent
  

• budget — Set spend caps per cron or agent and enforce them pre-flight with typed exit codes.

  Gate a scheduled run on exit code 0/8 so an over-budget lineage never fires.

  openrouter-pp-cli budget check nightly-drafter
  

• generation explain — Break one generation into its cost anatomy: tokens, latency, and the delta versus the cheapest provider.

  The next command after an anomaly fires: it turns 'cost spiked' into 'this call, this provider, this delta'.

  openrouter-pp-cli generation explain gen-abc123 --agent
  

• usage reconcile — Verify the local usage mirror against upstream daily totals and flag days that disagree.

  Run this before trusting any local cost analysis — it is the trust root for the other usage commands.

  openrouter-pp-cli usage reconcile --since 7d --agent
  

Provider health and dispatch

• providers degraded — See which providers or models are currently degraded before dispatching work to them.

  Check this before dispatch to route around trouble instead of retrying into it.

  openrouter-pp-cli providers degraded --agent
  

• endpoints failover — Rank the providers serving one model by status, price, and observed latency for dispatch order.

  Feed this to a router to pre-empt degraded providers for a specific model.

  openrouter-pp-cli endpoints failover deepseek/deepseek-v4 --agent
  

Runway and rate-limit headroom

• key eta — Project the date your key's spend cap trips, from the observed burn rate.

  Use this to plan the week's runs against the cap instead of discovering it mid-run.

  openrouter-pp-cli key eta --agent
  

• limits status — One view of current headroom: key-cap remaining, free-tier daily quota, and today's free-model burn.

  Check before a free-tier dev loop or fleet dispatch to avoid opaque 429s.

  openrouter-pp-cli limits status --agent
  

• credits runway — Project days-to-zero for prepaid credits at the trailing burn rate — the 402 leading indicator.

  A daily cron on this answers 'when do we hit 402' while there is still time to top up.

  openrouter-pp-cli credits runway --agent
  

Command Reference

activity — Manage activity

• openrouter-pp-cli activity — Returns user activity data grouped by endpoint for the last 30 (completed) UTC days.

audio — Manage audio

• openrouter-pp-cli audio create-speech — Synthesizes audio from the input text. Returns a raw audio bytestream in the requested format (e.g. mp3, pcm, wav).
• openrouter-pp-cli audio create-transcriptions — Transcribes audio into text.

benchmarks — Benchmarks endpoints

• openrouter-pp-cli benchmarks — Unified benchmark endpoint that aggregates scores from multiple benchmark sources (Artificial Analysis, Design Arena

byok — BYOK endpoints

• openrouter-pp-cli byok create-byokkey — Create a new bring-your-own-key (BYOK) provider credential.
• openrouter-pp-cli byok delete-byokkey — Delete (soft-delete) a bring-your-own-key (BYOK) provider credential by its id.
• openrouter-pp-cli byok get-byokkey — Get a single bring-your-own-key (BYOK) provider credential by its id.
• openrouter-pp-cli byok list-byokkeys — List the bring-your-own-key (BYOK) provider credentials for the authenticated entity's default workspace.
• openrouter-pp-cli byok update-byokkey — Update an existing bring-your-own-key (BYOK) provider credential by its id.

chat — Chat completion endpoints

• openrouter-pp-cli chat — Sends a request for a model response for the given chat conversation. Supports both streaming and non-streaming modes.

classifications — Task classification market-share endpoints

• openrouter-pp-cli classifications — Returns the market-share breakdown of OpenRouter traffic by task classification (e.g.

containers — Containers endpoints

credits — Credit management endpoints

• openrouter-pp-cli credits create-coinbase-charge — Deprecated.
• openrouter-pp-cli credits get — Get total credits purchased and used for the authenticated user.

datasets — Public OpenRouter usage datasets. Data returned by these endpoints is licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/): reuse and republish it, including commercially, with attribution to OpenRouter.

• openrouter-pp-cli datasets get-app-rankings — Returns the top public apps on OpenRouter ranked by token usage inside the requested date window
• openrouter-pp-cli datasets get-rankings-daily — Returns the top 50 public models per day by total token usage on OpenRouter
• openrouter-pp-cli datasets get-session-cost — Returns weekly refreshed, aggregated cost-per-session cells for the published harnesses.

embeddings — Text embedding endpoints

• openrouter-pp-cli embeddings create — Submits an embedding request to the embeddings router
• openrouter-pp-cli embeddings list-models — Returns a list of all available embeddings models and their properties

endpoints — Endpoint information

• openrouter-pp-cli endpoints — Preview the impact of ZDR on the available endpoints

files — Files endpoints

• openrouter-pp-cli files delete — Deletes a file owned by the requesting workspace. Deletion is irreversible.
• openrouter-pp-cli files get-metadata — Retrieves metadata for a single file owned by the requesting workspace.
• openrouter-pp-cli files list — Lists files belonging to the workspace of the authenticating API key.
• openrouter-pp-cli files upload — Uploads a file to be referenced in future API calls.

generation — Generation history endpoints

• openrouter-pp-cli generation get — Get request & usage metadata for a generation
• openrouter-pp-cli generation list-content — Get stored prompt, completion, and error content for a generation
• openrouter-pp-cli generation submit-feedback — Submit structured feedback on a generation the authenticated user made.

guardrails — Guardrails endpoints

• openrouter-pp-cli guardrails create — Create a new guardrail for the authenticated user.
• openrouter-pp-cli guardrails delete — Delete an existing guardrail. Management key required.
• openrouter-pp-cli guardrails get — Get a single guardrail by ID. Management key required.
• openrouter-pp-cli guardrails list — List all guardrails for the authenticated user.
• openrouter-pp-cli guardrails list-key-assignments — List all API key guardrail assignments for the authenticated user.
• openrouter-pp-cli guardrails list-member-assignments — List all organization member guardrail assignments for the authenticated user.
• openrouter-pp-cli guardrails update — Update an existing guardrail, or materialize an unconfigured workspace default guardrail.

images — Images endpoints

• openrouter-pp-cli images create — Generates an image from a text prompt via the image generation router
• openrouter-pp-cli images list-model-endpoints — Returns the full per-endpoint records for an image model: each endpoint's definitive supported parameters, pricing
• openrouter-pp-cli images list-models — Lists every image generation model with its top-level supported-parameter superset and a URL to its full per-endpoint

key — Manage key

• openrouter-pp-cli key — Get information on the API key associated with the current authentication session

keys — Manage keys

• openrouter-pp-cli keys create — Create a new API key for the authenticated user. The plaintext key is returned only in this response.
• openrouter-pp-cli keys delete — Delete an existing API key.
• openrouter-pp-cli keys get — Get a single API key by hash. Management key required.
• openrouter-pp-cli keys list — List all API keys for the authenticated user. Management key required.
• openrouter-pp-cli keys update — Update an existing API key.

messages — Manage messages

• openrouter-pp-cli messages — Creates a message using the Anthropic Messages API format. Supports text, images, PDFs, tools, and extended thinking.

model — Model information endpoints

• openrouter-pp-cli model <author> <slug> — Returns full details for a single model identified by its author and slug (e.g. openai/gpt-4).

models — Model information endpoints

• openrouter-pp-cli models get — List all models and their properties
• openrouter-pp-cli models list-count — Get total count of available models
• openrouter-pp-cli models list-user — List models filtered by user provider preferences, [privacy settings](https://openrouter.

observability — Observability endpoints

• openrouter-pp-cli observability create-destination — Create a new observability destination. A maximum of 5 destinations per type is allowed.
• openrouter-pp-cli observability delete-destination — Delete an existing observability destination. This performs a soft delete.
• openrouter-pp-cli observability get-destination — Fetch a single observability destination by its UUID.
• openrouter-pp-cli observability list-destinations — List the observability destinations configured for the authenticated entity's default workspace.
• openrouter-pp-cli observability update-destination — Update an existing observability destination. Only the fields provided in the request body are updated.

openrouter-analytics — Manage openrouter analytics

• openrouter-pp-cli openrouter-analytics get-meta — Returns the available metrics, dimensions, filter operators, and granularities for the analytics query endpoint.
• openrouter-pp-cli openrouter-analytics query — Execute an analytics query with specified metrics, dimensions, filters, and time range.

openrouter-auth — Manage openrouter auth

• openrouter-pp-cli openrouter-auth create-keys-code — Create an authorization code for the PKCE flow to generate a user-controlled API key
• openrouter-pp-cli openrouter-auth exchange-code-for-apikey — Exchange an authorization code from the PKCE flow for a user-controlled API key

organization — Organization endpoints

• openrouter-pp-cli organization — List all members of the organization associated with the authenticated management key.

presets — Presets endpoints

• openrouter-pp-cli presets get — Retrieves a preset by its slug with its currently designated version inline.
• openrouter-pp-cli presets list — Lists all presets for the authenticated user, ordered by most recently updated first.

providers — Provider information endpoints

• openrouter-pp-cli providers — List all providers

rerank — Rerank endpoints

• openrouter-pp-cli rerank — Submits a rerank request to the rerank router

responses — OpenAI-compatible Responses API endpoints

• openrouter-pp-cli responses — Creates a streaming or non-streaming response using OpenResponses API format

scim — SCIM endpoints

• openrouter-pp-cli scim create-group-mapping — Create a SCIM group-to-workspace role mapping.
• openrouter-pp-cli scim delete-group-mapping — Delete a SCIM group-to-workspace mapping. Management key required.
• openrouter-pp-cli scim get-group-mapping — Get a SCIM group-to-workspace mapping. Management key required.
• openrouter-pp-cli scim list-group-mappings — List SCIM group-to-workspace mappings for the organization.
• openrouter-pp-cli scim list-groups — List SCIM groups for the organization. Management key required.
• openrouter-pp-cli scim update-group-mapping — Update a SCIM group mapping role. Management key required.

videos — Manage videos

• openrouter-pp-cli videos create — Submits a video generation request and returns a polling URL to check status
• openrouter-pp-cli videos get — Returns job status and content URLs when completed
• openrouter-pp-cli videos list-models — Returns a list of all available video generation models and their properties

workspaces — Workspaces endpoints

• openrouter-pp-cli workspaces create — Create a new workspace for the authenticated user.
• openrouter-pp-cli workspaces delete — Delete an existing workspace. Workspaces with active API keys cannot be deleted; remove the keys first.
• openrouter-pp-cli workspaces get — Get a single workspace by ID or slug. Management key required.
• openrouter-pp-cli workspaces list — List all workspaces for the authenticated user.
• openrouter-pp-cli workspaces update — Update an existing workspace by ID or slug. Management key required.

Finding the right command

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

openrouter-pp-cli which "<capability in your own words>"

which resolves a natural-language capability query to the best matching command from this CLI's curated feature index. Exit code 0 means at least one match; exit code 2 means no confident match — fall back to --help or use a narrower query.

Recipes

Monday cost review

openrouter-pp-cli usage cost-by --since 7d --agent

The weekly attribution pass: spend per cron/agent tag, ready to paste into a review note.

Anomaly to root cause in two commands

openrouter-pp-cli usage anomaly --since 24h --agent

When a model flags, follow with 'generation explain ' to get tokens, latency, and the cheapest-provider delta for the offending call.

Pre-dispatch health gate

openrouter-pp-cli providers degraded --agent

Run before firing a lineage; pair with 'endpoints failover ' to pick the dispatch order for the model you are about to use.

Narrow a deep endpoint payload

openrouter-pp-cli models endpoints list deepseek deepseek-v4 --agent --select data.endpoints.provider_name,data.endpoints.pricing.completion,data.endpoints.status

Per-model provider listings nest deeply; --select keeps the answer to the three fields a router actually needs.

Budget-gated cron pattern

openrouter-pp-cli budget check nightly-drafter

Exit code 0 under cap, 8 over — gate a scheduled run on this before it fires (e.g. 'budget check X && systemctl --user start X.service').

Auth Setup

Standard bearer auth with your inference key via OPENROUTER_API_KEY covers models, credits, key status, and chat. Two surfaces need the separate provisioning (management) key created in the dashboard: key administration (/keys) and account-wide usage rollups (/activity, analytics). A 401 or 403 on those commands with an otherwise-working key means you are holding the inference key where the management key is required — not a broken setup. Live-verified 2026-09: /activity returns 403 'Only management keys can fetch activity' on an inference key.

Run openrouter-pp-cli doctor to verify setup.

Agent Mode

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

• Pipeable — JSON on stdout, errors on stderr

• Filterable — --select keeps a subset of fields. Dotted paths descend into nested structures; arrays traverse element-wise. Critical for keeping context small on verbose APIs:

  openrouter-pp-cli benchmarks --agent --select accuracy,accuracy_stddev,agentic_index
  

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

• Use per-kind env vars only when a specific kind must diverge: OPENROUTER_CONFIG_DIR, OPENROUTER_DATA_DIR, OPENROUTER_STATE_DIR, OPENROUTER_CACHE_DIR.

• Resolution order is per-kind env var, --home, OPENROUTER_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 openrouter-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": {
      "openrouter": {
        "command": "openrouter-pp-mcp",
        "env": {
          "OPENROUTER_HOME": "/srv/openrouter"
        }
      }
    }
  }
  

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

openrouter-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>", "openrouter-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 `openrouter-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; openrouter-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 openrouter-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:

openrouter-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.
openrouter-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).
openrouter-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.

openrouter-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

openrouter-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.
• OPENROUTER_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:

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

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

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.

openrouter-pp-cli profile save briefing --json
openrouter-pp-cli --profile briefing benchmarks
openrouter-pp-cli profile list --json
openrouter-pp-cli profile show briefing
openrouter-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 openrouter-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

Install the MCP binary from this CLI's published public-library entry or pre-built release, then register it:

claude mcp add openrouter-pp-mcp -- openrouter-pp-mcp

Verify: claude mcp list

Direct Use

1. Check if installed: which openrouter-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:

   openrouter-pp-cli <command> [subcommand] [args] --agent
   

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