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Documentation Index

Fetch the complete documentation index at: https://docs.roboticks.io/llms.txt

Use this file to discover all available pages before exploring further.

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Natural-language search

The global search bar at the top of every page accepts plain-English queries. Behind the scenes a Haiku-tier AI call parses the intent, picks the target entity (test runs, requirements, logs, evidence packs, standards), extracts filters, and returns a structured answer with deterministic click-through URLs.

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What it can answer

Examples that work today:
You typeWhat it does
”failing tests on main from last week”Targets test_runs with branch=main, status=failed, time range 7d. Links you to /{org}/{project}/test-runs?branch=main&status=failed&since=7d.
”uncovered requirements for IEC 61508 SIL 2”Targets requirements with standard=iec-61508, sil=2, coverage=uncovered.
”what does REQ-014 confirm”Targets requirements with id=REQ-014.
”evidence packs for release v2.4.0”Targets evidence_packs with release_tag=v2.4.0.
”logs mentioning ros2_control timeout”Targets logs with full-text ros2_control timeout.
The LLM picks the target entity and the filter values. The platform builds the URL itself — the LLM never invents a link. If the platform can’t be confident, it falls back to traditional text search and surfaces the AI-suggested URLs as secondary options.

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What you get back

{
  "query": "failing tests on main from last week",
  "is_ai_query": true,
  "ai_result": {
    "answer": "Targets failed test runs on the main branch in the last 7 days.",
    "confidence": 0.92,
    "sources": [],
    "suggested_actions": [
      {"label": "View failed runs on main", "url": "/.../test-runs?branch=main&status=failed&since=7d"}
    ],
    "follow_up_questions": [
      "Just the runs whose failures touched safety-tagged tests?",
      "Group by test name to find flaky cases?"
    ]
  },
  "tokens_used": 42,
  "processing_time_ms": 410
}
The follow_up_questions are one-click — clicking them re-runs q with the new phrasing.

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Endpoint

GET /api/v1/organizations/{org}/projects/{project}/search/ai?q=<query>
Returns 402 if the org is out of ai_tokens. Returns 200 + is_ai_query: false for queries the platform decided to handle with traditional search (short queries, exact-ID lookups).

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Plan gating

Costs 1 ai-token per parse call (SEARCH_QUERY_PARSE task type — Haiku at 2k input). Free plan’s 100k grant is plenty.

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What it does NOT do

  • It does not answer the question itself. It parses intent into a filter and links you to the structured view. The answer comes from the actual matrix / list page the link opens.
  • It does not read your repo source or your private logs to “answer questions” about them. It only uses the parsed query intent.
  • It does not invent IDs. If you ask “show me REQ-9999” and REQ-9999 doesn’t exist, the structured page will render empty — the AI doesn’t hallucinate rows into existence.

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Next

AI overview

Every AI surface on the platform and what they cost.

Traceability matrix

Where most of these queries actually land.