Borrowing it
Nothing to install: this file belongs to prebid/salesagent. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/prebid/salesagent/main/.claude/commands/research.mdgit clone --depth 1 https://github.com/prebid/salesagentWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/prebid/salesagent/research)<a href="https://agentmods.dev/commands/prebid/salesagent/research"><img src="https://agentmods.dev/badge/commands/prebid/salesagent/research/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/prebid/salesagent/research"><img src="https://agentmods.dev/badge/commands/prebid/salesagent/research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00008 | $0.01232 |
| Opus 5.5 | $0.00003 | $0.00493 |
| Sonnet 5.5 | $0.00002 | $0.00246 |
| Haiku 4.5 | $0.00001 | $0.00123 |
Grade A, and why
research scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Task: $ARGUMENTS
Instructions
You are researching beads task $ARGUMENTS before implementation begins.
Step 1: Read the Task
Run bd show $ARGUMENTS to get the full task description, acceptance criteria, and any design notes.
Step 2: Read the AdCP Specification (Spec-First)
If the task touches schemas, data models, targeting, protocol behavior, tool inputs/outputs, or buyer-facing fields — read the spec before exploring the codebase. The spec defines what's correct; the code is our current (possibly wrong) implementation of it.
Source of truth: The sibling adcp checkout at ../adcp. Read the relevant schema and doc files directly — do NOT rely on training data or assumptions about the spec. Online repo is adcontextprotocol/adcp.
Key locations:
- JSON schemas:
../adcp/static/schemas/source/- Core types:
core/targeting.json,core/package.json,core/product.json,core/media-buy.json,core/frequency-cap.json, etc. - Enums:
enums/(pricing models, statuses, etc.) - Media buy operations:
media-buy/(create/update requests/responses, package-request) - Signals:
signals/
- Core types:
- Protocol docs:
../adcp/docs/(protocols, media-buy lifecycle, reference) - Python types: The
adcpPython library may be ahead of the JSON schema (v3 structured geo types exist in Python but not yet in the JSON schema). When they diverge, note the discrepancy in the research artifact.
What to extract from the spec:
- Read the relevant JSON schema(s) — identify which fields are spec-defined, their types, and constraints
- Check required vs optional — does the spec mandate fields we might treat as optional (or vice versa)?
- Check
additionalProperties— does the spec allow extra fields? - Note any spec gaps — if the task requires behavior the spec doesn't cover, document it explicitly. Don't invent spec compliance.
Carry these findings into every subsequent step — codebase exploration, engineering checklist, and architecture decisions should all be evaluated against what the spec says.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago Changed d03f05a828f6
- 1mo ago First seen · 112 lines · 8 tokens per session scan A 34a7f4a47d94
research is a command published in the GitHub repository prebid/salesagent (39 stars, last pushed yesterday), licensed Apache-2.0. It adds 8 tokens to every session and 1,232 once invoked, about $0.0000 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-01.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
speckit.bug.fix
Apply the remediation from a bug assessment and record what was changed.