{
  "$schema": "https://a2aprotocol.org/schemas/agent-card.v1.json",
  "name": "PassbackAI",
  "description": "PassbackAI is the browser tool for the moment a long Claude, ChatGPT, Cursor, or Gemini answer comes back with five things wrong with it. Paste the answer, comment on each passage where it sits (every comment ships with the verbatim quote AND the heading it's under — and for tables, the row or column the cell sits in), copy the structured export, paste it back into the chat. The model lands every fix on its exact target — no second round needed to clarify which thing you meant. No backend, no database, no account; the document never leaves the browser.",
  "url": "https://passbackai.com/",
  "version": "1.0.0",
  "provider": {
    "organization": "PassbackAI",
    "url": "https://passbackai.com"
  },
  "documentationUrl": "https://passbackai.com/llms-full.txt",
  "capabilities": {
    "streaming": false,
    "pushNotifications": false,
    "stateTransitionHistory": false
  },
  "defaultInputModes": ["text/markdown", "text/plain"],
  "defaultOutputModes": ["text/markdown"],
  "skills": [
    {
      "id": "annotate-llm-answer",
      "name": "Annotate an LLM answer with anchored, multi-point feedback",
      "description": "Take a Markdown answer produced by an LLM, attach quoted-passage comments to specific spans, and emit a structured Markdown export pairing every comment with both the verbatim quote and the heading chain (or table row/column) it sits under. The export is designed to be pasted back into the originating chat so the model lands every fix on its exact target in one round-trip.",
      "tags": ["llm-feedback", "markdown", "annotation", "review", "prompt-engineering"],
      "examples": [
        "Give Claude five corrections on a 1,800-word answer in one paste, anchored.",
        "Tell ChatGPT exactly which row of a table to fix without it rewriting the whole document.",
        "Ship eleven scoped edits to Cursor's last README rewrite without it broadening into the rest of the file."
      ],
      "inputModes": ["text/markdown"],
      "outputModes": ["text/markdown"]
    },
    {
      "id": "question-extractor",
      "name": "Extract the open questions from any text and emit a renderable questionnaire",
      "description": "Read any input (PRD, brief, retro, email, transcript) and identify the open questions — decisions not made, ambiguities, missing info. Emit a single JSON questionnaire that pastes into https://passbackai.com and renders as an interactive form. Hard-capped at 12 questions, 3–4 short option labels per question, with optional sections, multi-select, and routing-back-to-sender. Schema at https://passbackai.com/ask; full skill at https://passbackai.com/skills/question-extractor.md.",
      "tags": ["questionnaire", "decision-extraction", "open-questions", "prompt-engineering", "claude", "chatgpt", "interview", "spec-review"],
      "examples": [
        "Turn a one-paragraph product brief into a 6-question form covering scope, auth, and storage decisions.",
        "Extract the unresolved decisions from a Slack retro and route the questionnaire back to the author.",
        "Pull the open questions out of an RFC and emit JSON the engineer can paste into passbackai.com."
      ],
      "inputModes": ["text/markdown", "text/plain"],
      "outputModes": ["application/json"]
    }
  ],
  "supportsAuthenticatedExtendedCard": false
}