# Adform Bid Landscape

> >-

- **Type:** Skill
- **Install:** `agentstack add skill-adform-agentic-skills-adform-bid-landscape`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [adform](https://agentstack.voostack.com/s/adform)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [adform](https://github.com/adform)
- **Source:** https://github.com/adform/agentic-skills/tree/main/dist/generic/adform-bid-landscape
- **Website:** https://site.adform.com

## Install

```sh
agentstack add skill-adform-agentic-skills-adform-bid-landscape
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Adform bid landscape

The CPM-versus-win-rate curve for an RTB line item: what share of auctions you win at each CPM,
plus reachable cookies and requests. Use this to answer "what CPM do I need to win X%". Read-only.

## Connection & tooling

Runs on the Adform GraphQL MCP. Use `graphql_execute(query, variables)` to run and
`graphql_validate` to check a query first. Always validate before executing. The queries below are
**illustrative examples** — if a field or input shape isn't shown, or a query fails validation,
discover the current schema with `graphql_search` (lighter, preferred) and fall back to
`graphql_introspect` one call at a time with ≥2s between calls for complex input types, enum
values, or union/interface resolution.

---

## Building the input

`rtbLineItemForecasting(lineItem: ForecastingRtbLineItemInput!)` needs a complete line-item
shape. Build it by reading the live line item first, then mirroring its targeting, inventory,
pricing, periods, and environments into the input.

## Method

1. Read the live line item using `rtbLineItem(id:)` (the `id` is the RTB setup ID from `rtbLineItems()`, not the placement ID) to get current targeting, inventory, and pricing
2. Mirror the complete line item structure into the `ForecastingRtbLineItemInput` payload
3. Run `rtbLineItemForecasting` with the mirrored input
4. Pair `cpms` and `winRates` arrays by index to read the CPM-vs-win-rate curve

## Inline execute examples

### Read the live line item

```graphql
{
  rtbLineItem(id: "99999") {
    id name environments orderId campaignId
    inventories { id buyingType deals { id bidPrice } }
    targetings { id name bidMultiplier }
  }
}
```

### Run the forecast

```graphql
{
  rtbLineItemForecasting(lineItem: {
    name: "Forecast"
    campaignId: "3993873"
    # mirror the full targeting and inventory from the live line item
  }) {
    cpms
    winRates
    reachableCookies
    reachableRequests
  }
}
```

`cpms` and `winRates` are parallel arrays — pair index N of `cpms` with index N of `winRates`
to read the curve.

---

## Presenting

Lead with the actionable answer: the CPM needed to hit the trader's target win rate (interpolate
the curve), then show the full curve as context, and the reachable cookies and requests for
inventory sizing. State that forecasts are estimates and cross-check with adform-past-traffic if
volume looks unexpected.

---

## Supplementary: bid reason context via mcpStats

After sizing the CPM needed to win the target share of auctions, cross-check
with a mcpStats bid reason query. This shows whether current losses are
primarily price-driven (the bid landscape addresses this) or driven by other
factors (creative audit, targeting, budget) that a CPM increase alone will not
fix. Validated query:

```graphql
{
  mcpStats {
    totalRowCount
    totals
    columns {
      dimensions { bidReason { name } }
      metrics {
        rtbBids
        lostBids
        bidReasonCount
        impressions
        rtbWinRate
      }
    }
    rows(
      filter: {
        date: { from: "2026-06-01", to: "2026-06-30" }
        advertiser: { ids: ["2133936"] }
      }
      paging: { offset: 0, limit: 50 }
      sort: [{ column: 0, direction: desc }]
    )
  }
}
```

If the dominant bid reason is price-floor-related, the bid landscape CPM
recommendation is the primary fix. If non-price reasons dominate, flag them
to the trader alongside the CPM recommendation so they are not surprised that
raising the bid alone does not resolve the win rate shortfall.

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [adform](https://github.com/adform)
- **Source:** [adform/agentic-skills](https://github.com/adform/agentic-skills)
- **License:** MIT
- **Homepage:** https://site.adform.com

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-adform-agentic-skills-adform-bid-landscape
- Seller: https://agentstack.voostack.com/s/adform
- Browse the marketplace: https://agentstack.voostack.com/browse

---
Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.