Pp Openai Ads

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OpenAI Ads — Printing Press CLI

Prerequisites: Install the CLI

This skill drives the openai-ads-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 openai-ads --cli-only

2. Verify: openai-ads-pp-cli --version
3. Ensure the reported install directory is on $PATH for the agent/runtime that will invoke this skill.

If the npx install fails (no Node, offline, etc.), fall back to a direct Go install (requires Go 1.26.5 or newer). This installs into $GOPATH/bin (default $HOME/go/bin), so add that directory to $PATH instead:

go install github.com/mvanhorn/printing-press-library/library/marketing/openai-ads/cmd/openai-ads-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.

Covers the whole Advertiser API surface rather than a read-only slice of it, and mirrors your account into SQLite so questions the REST API structurally cannot answer become one command. Pacing, drift, creative fatigue, and structural audits all come from local snapshots. Every monetary value is rendered in your account currency instead of raw micros.

When to Use This CLI

Reach for this when working with ChatGPT Ads campaigns programmatically: auditing account structure, checking spend pacing, diffing what changed, or creating and updating campaigns, ad groups, and ads. It is strongest when the question spans more than one resource or more than one point in time, because the local mirror can join and compare where the REST API cannot.

Anti-triggers

Do not use this CLI for:

• Do not use this CLI for the OpenAI Platform API; model, file, and batch calls belong to api.openai.com and a different credential.
• Do not use this CLI to send server-side conversion events; that is a separate ingestion endpoint with its own event-scoped key.
• Do not use this CLI to complete business verification or brand review, which are console-only workflows.
• Do not expect local commands to reflect changes made in Ads Manager until after a sync.

Unique Capabilities

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

Local history the API does not keep

• pace — See whether a campaign will underspend or blow through its cap before the period ends.

  Reach for this instead of raw insights when the question is about trajectory rather than a current number.

  openai-ads-pp-cli pace --agent

• drift — Show what changed across campaigns, ad groups, and ads between two syncs.

  Use this to answer 'what changed and when', which no single API call can report.

  openai-ads-pp-cli drift --since 7d --agent

• fatigue — Rank ads by click-through decay so tired creative is obvious before spend is wasted.

  Pick this over ad insights when the question is whether performance is declining, not what it is today.

  openai-ads-pp-cli fatigue --limit 10 --agent

• review-watch — Track approval and review status transitions across the account and every ad.

  Use this to catch a flip to rejected or in_review that a status read would not reveal as a change.

  openai-ads-pp-cli review-watch --agent

Cross-entity joins the API cannot do

• bid-check — Flag ad groups whose maximum bid is irrational against the parent campaign budget.

  Catches configurations that permit only a handful of clicks per day before any spend happens.

  openai-ads-pp-cli bid-check --agent

• orphans — Find ad groups with no ads, campaigns with no delivery, and audiences nothing references.

  Use this for structural dead weight rather than performance questions.

  openai-ads-pp-cli orphans --agent

• tree — Render the whole campaign, ad group, and ad hierarchy with status, budget, and review state.

  Start here to orient in an unfamiliar account before drilling into any single resource.

  openai-ads-pp-cli tree --agent

Readability

• geo resolve — Turn the bare location IDs in campaign targeting into readable place names.

  Use this whenever targeting output shows numeric IDs you cannot interpret.

  openai-ads-pp-cli geo resolve --agent

Command Reference

ad-account — Manage ad account

• openai-ads-pp-cli ad-account activate-method — Activate the ad account.
• openai-ads-pp-cli ad-account get-insights-method — Get ad account insights aggregated by time granularity.
• openai-ads-pp-cli ad-account get-method — Get metadata for the ad account
• openai-ads-pp-cli ad-account pause-method — Pause the ad account.
• openai-ads-pp-cli ad-account update-method — Update ad account brand metadata.

ad-groups — Manage ad groups

• openai-ads-pp-cli ad-groups create-method — Create an ad group for a campaign
• openai-ads-pp-cli ad-groups get-method — Get an ad group
• openai-ads-pp-cli ad-groups list-method — Get all ad groups for a campaign
• openai-ads-pp-cli ad-groups update-method — Update an ad group

ads — Manage ads

• openai-ads-pp-cli ads create-method — Create an ad for an ad group
• openai-ads-pp-cli ads get-method — Get an ad
• openai-ads-pp-cli ads list-method — Get all ads for an ad group
• openai-ads-pp-cli ads update-method — Update an ad

campaigns — Manage campaigns

• openai-ads-pp-cli campaigns create-method — Create a campaign for an ad account
• openai-ads-pp-cli campaigns get-method — Get a campaign
• openai-ads-pp-cli campaigns list-method — Get all campaigns for an ad account
• openai-ads-pp-cli campaigns update-method — Update a campaign

conversions — Manage conversions

• openai-ads-pp-cli conversions create-api-key-method — Create a Conversions API key for the currently authenticated ad account.
• openai-ads-pp-cli conversions create-event-setting-method — Create a conversion event setting for the currently authenticated ad account.
• openai-ads-pp-cli conversions create-source-method — Create a conversion pixel.
• openai-ads-pp-cli conversions list-event-settings-method — List conversion event settings for the currently authenticated ad account.
• openai-ads-pp-cli conversions post-insights-method — Get attributed conversion totals for the authenticated ad account.

custom-audiences — Manage custom audiences

• openai-ads-pp-cli custom-audiences create-method — Create a custom audience for the authenticated ad account.
• openai-ads-pp-cli custom-audiences create-upload-method — Create a custom audience from an uploaded file and start processing.
• openai-ads-pp-cli custom-audiences get-method — Get a custom audience for the authenticated ad account.
• openai-ads-pp-cli custom-audiences list-method — List custom audiences for the authenticated ad account.

geo-lookup — Manage geo lookup

• openai-ads-pp-cli geo-lookup — Search DMA and standard region codes for advertiser geo targeting.

upload — Manage upload

• openai-ads-pp-cli upload — Upload an image URL or image file and return a file id

Finding the right command

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

openai-ads-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

Orient in an unfamiliar account

openai-ads-pp-cli tree --agent

Renders the whole hierarchy with status and budget so you can see the shape before touching anything.

Narrow a verbose campaign payload

openai-ads-pp-cli campaigns list --agent --select data.id,data.name,data.status,data.budget.daily_spend_limit_micros

Campaign objects carry targeting and landing page blocks; selecting fields keeps agent context small.

Find tired creative

openai-ads-pp-cli fatigue --limit 5 --agent

Ranks ads by click-through decay across stored snapshots rather than a single reading.

See what changed this week

openai-ads-pp-cli drift --since 7d --agent

Diffs local snapshots to surface status, budget, bid, and creative changes the API keeps no record of.

Preview a mutation safely

openai-ads-pp-cli campaigns pause campaign-method cmpn_example --dry-run

Shows the request that would be sent without changing anything in the live account.

Auth Setup

Ads Manager issues two different keys and they are easy to confuse. The Ads API key comes from the Settings tab and is scoped to one ad account; set it as OPENAI_ADS_API_KEY. The Conversions API key comes from the Conversions tab, is scoped to a single conversion event, and returns 403 Unauthorized to read ads data if used for ads calls. Set that one as OPENAI_ADS_CONVERSIONS_API_KEY only if you send server-side conversion events. Run doctor to see which key is configured.

Run openai-ads-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:

  openai-ads-pp-cli ad-account activate-method --agent --select id,name,status

• Previewable — --dry-run shows the request without sending

• Offline-friendly — sync/search commands can use the local SQLite store when available

• Non-interactive — never prompts, every input is a flag

• Explicit retries — use --idempotent only when an already-existing create should count as success

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

• Use per-kind env vars only when a specific kind must diverge: OPENAI_ADS_CONFIG_DIR, OPENAI_ADS_DATA_DIR, OPENAI_ADS_STATE_DIR, OPENAI_ADS_CACHE_DIR.

• Resolution order is per-kind env var, --home, OPENAI_ADS_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 openai-ads-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": {
      "openai-ads": {
        "command": "openai-ads-pp-mcp",
        "env": {
          "OPENAI_ADS_HOME": "/srv/openai-ads"
        }
      }
    }
  }

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

openai-ads-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>", "openai-ads-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 `openai-ads-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; openai-ads-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.