import-conversation-history

name

import-conversation-history

description

Use this skill when the user wants to import or migrate conversation history from another LLM chat or coding tool (e.g. ChatGPT, Claude.ai, Cursor, Copilot Chat, Aider, Gemini, a custom JSON export) into Turnstone. The skill teaches Turnstone's destination contracts — workstream identity, the OpenAI-shaped message rows, tool-call/result pairing, provider-fidelity blobs, attachments, and archive-vs-resumable choice — so the agent can map any source format onto them. Trigger phrases: "import my chats", "migrate this transcript into Turnstone", "bring my Claude.ai history over", "load this export as a workstream".

Importing Conversation History into Turnstone

Overview

Source formats vary; the destination does not. Your job is to translate whatever the user hands you (JSON dump, ZIP export, scraped HTML, screenshot OCR, raw transcript) into Turnstone's internal shape: one workstream row plus an ordered sequence of conversation rows in OpenAI message format. This skill documents the destination so you can write a correct mapper for any source.

Two questions to settle with the user before writing anything:

1. Archive or resumable? An archive ("saved" workstream — state="closed") is read-only history. A resumable workstream (state="idle") lets the user continue the conversation; this only works cleanly when the source LLM matches a Turnstone-supported provider/model and tool definitions still resolve.
2. One workstream per source thread, or merge? Default to one-to-one unless the user explicitly asks to merge.

Default to archive when in doubt — resuming a foreign transcript with mismatched tool schemas or stale provider signatures will fail at the next turn.

Turnstone Data Model (the destination)

Two tables carry the conversation:

workstreams (one row per imported thread)

Column	Required	Notes
ws_id	yes	32-char lowercase hex. Auto-generate with secrets.token_hex(16) if you don't already have one. First 4 hex chars are the routing bucket — see "Identity & Routing" below.
name	yes	Short title. Pull from source thread title; fall back to first ~60 chars of first user message.
state	yes	"closed" for archive, "idle" for resumable. Never set "running" on import.
kind	yes	"interactive" for normal threads. Do NOT use "coordinator" for imports — that's reserved for cluster-spawned coordinator workstreams.
parent_ws_id	no	Leave NULL. Only set if you're importing a coordinator-spawned subtree and re-parenting it; rare.
user_id	yes	Owner. Must exist in users; importer must know which Turnstone user owns the imported history.
node_id	yes (multi-node)	Denormalized cache of the node that owns this ws_id's bucket. Single-node deployments can leave it NULL or set it to the only node.
alias	no	Human-typeable short name. Optional; must be unique cluster-wide if set.
title	no	Auto-titled later by the LLM; safe to leave NULL on import.
skill_id, skill_version	yes	Default "" and 0 unless the source thread was scoped to a Turnstone skill.
created, updated	yes	ISO8601 strings. Use the source's first/last message timestamps when available.

conversations (many rows per thread, ordered by id/timestamp)

Column	Notes
ws_id	The workstream this row belongs to.
timestamp	ISO8601 string. Preserve source timestamps; fall back to monotonically increasing values if unknown. Order is canonical via id (autoincrement), not timestamp — but always insert in conversational order so both agree.
role	One of system, user, assistant, tool, developer. See role mapping below.
content	Text. May be NULL for assistant rows that are only tool calls.
tool_name	Set on role="tool" rows (the tool whose result this is). NULL otherwise.
tool_call_id	Set on role="tool" rows (matches the assistant row's tool_calls[].id). NULL otherwise.
tool_calls	JSON-encoded list, on role="assistant" rows that issued tool calls. OpenAI shape — see "Tool Calls" below.
provider_data	JSON blob preserving provider-native content blocks (Anthropic signature, Gemini thought_signature, etc.). Optional; only matters for resumable imports against the same provider. Skip for archives.

The internal format is OpenAI-shaped, even when the source was Anthropic or Gemini. Providers translate at their own API boundary; storage stays uniform.

Identity & Routing (ws_id)

• ws_id is 32-char lowercase hex (i.e. secrets.token_hex(16)).
• The routing bucket is int(ws_id[:4], 16) — the first 4 hex chars place this workstream on a specific node via the consistent hash ring.
• For multi-node imports: either insert through the console's routing proxy (which forwards to the owning node), or generate ws_ids and write directly to each node's database in batches grouped by bucket.
• For single-node imports: bucket math is irrelevant; any ws_id works.
• Do not reuse the source platform's IDs as ws_id unless they happen to be 32-char hex. Generate fresh; if you need the old ID for traceability, store it in workstream_config under a key like import.source_id.

Recommended Import Path

Three options, in order of preference:

1. Storage protocol (recommended for full history)

Use turnstone.core.storage.Storage.save_messages_bulk(rows). This is the canonical bulk-insert primitive and bypasses the LLM round-trip entirely.

from turnstone.core.storage import get_storage  # construct via the same path the server uses

storage = get_storage(...)  # see turnstone.core.storage.__init__ for the project's wiring

storage.create_workstream(  # or whatever the project's exposed creator is — check turnstone/core/storage/_protocol.py
    ws_id=ws_id,
    user_id=user_id,
    name=name,
    state="closed",
    kind="interactive",
    ...
)

storage.save_messages_bulk([
    {"ws_id": ws_id, "role": "user", "content": "Hello"},
    {"ws_id": ws_id, "role": "assistant", "content": "Hi! What can I help with?"},
    {"ws_id": ws_id, "role": "assistant", "content": None,
     "tool_calls": json.dumps([{"id": "call_1", "type": "function",
                                "function": {"name": "search", "arguments": "{\"q\":\"x\"}"}}])},
    {"ws_id": ws_id, "role": "tool", "tool_name": "search", "tool_call_id": "call_1",
     "content": "result text"},
    # ...
])

save_messages_bulk handles timestamp and the workstream's updated column internally, so you don't need to compute them per row. Verify the exact creator signature by reading turnstone/core/storage/_protocol.py — table layout has shifted across migrations and the Storage protocol is the source of truth.

2. SDK create_workstream(resume_ws=...) (when the source is already a Turnstone workstream)

Only useful for Turnstone → Turnstone re-parenting. Not relevant for foreign sources.

3. SDK create_workstream(initial_message=...) + send() per turn (last resort)

Only fits archives where the source had no tool calls and you don't care about preserving assistant turns verbatim. Each send() triggers a real LLM round-trip, which is expensive and rewrites assistant content. Don't use this for full history.

Role Mapping

Common source-role conventions and how they map to Turnstone:

Source role	Turnstone role	Notes
user, human	user	Direct map.
assistant, ai, model, bot	assistant	Direct map.
system	system	Preserve only if it's content the user wrote (custom instructions). Drop boilerplate provider preambles — Turnstone composes its own system message.
developer (OpenAI o-series)	developer	Preserve.
tool, function, tool_result	tool	Must carry tool_name and tool_call_id matching the prior assistant row's tool_calls[].id.
tool_use (Anthropic)	assistant with tool_calls	Anthropic emits tool calls inside an assistant message; flatten to OpenAI shape.
human_feedback, revision	user	Treat as a follow-up user turn.

Tool Calls (the most error-prone part)

Turnstone stores tool calls in OpenAI's nested-function shape on the assistant row, and matches them with role="tool" result rows by tool_call_id.

Assistant row with tool calls

{
  "role": "assistant",
  "content": null,
  "tool_calls": [
    {
      "id": "call_abc123",
      "type": "function",
      "function": {
        "name": "search_web",
        "arguments": "{\"query\":\"turnstone import\"}"
      }
    }
  ]
}

tool_calls[].function.arguments is a JSON-encoded string, not an object. Source formats commonly get this wrong — Anthropic stores arguments as a parsed object, Gemini as a struct. Always re-serialize to a string.

Tool result row

{
  "role": "tool",
  "tool_name": "search_web",
  "tool_call_id": "call_abc123",
  "content": "..."
}

Pairing rules:

• Every assistant tool_calls[].id MUST be followed by exactly one role="tool" row with the matching tool_call_id, before the next user/assistant turn.
• If the source dropped the tool result (cut-off transcript), insert a synthetic role="tool" row with content="[tool result missing in source]" to keep the chain valid. An assistant row with an unanswered tool_calls[].id will break replay and any LLM round-trip.
• Multi-tool assistant turns: one role="tool" row per call, in any order, all before the next non-tool row.

Tool ID generation

If the source used opaque tool IDs that aren't unique within a thread (some platforms reuse them), regenerate with a stable scheme like f"call_{i}" where i is a per-thread counter. Update both the assistant and tool rows together.

Provider Fidelity (provider_data)

Skip this entirely for archive imports.

For resumable imports against the same provider, populate provider_data to preserve provider-specific tool-call metadata that the next API round-trip will require:

• Anthropic: signature field on thinking blocks; required for round-tripping extended-thinking responses.
• Gemini: thought_signature on tool calls; required for fidelity.
• OpenAI: typically nothing to preserve.

The runtime-side dict key is _provider_content (a list of provider-native blocks); the persisted column is provider_data (the same list, JSON-encoded). If you don't have provider-native blocks from the source — and you usually won't, because a foreign export won't include them — leave provider_data NULL. The first new turn will succeed without it, but the previous assistant turn's reasoning won't replay back to the model.

Attachments

If the source thread had image or file attachments:

• Size limits: images ≤ 4 MiB, text documents ≤ 512 KiB. Reject or downsample anything bigger.
• Allowed types: server validates magic bytes for images and UTF-8-decodes for text. Binary blobs that aren't images won't pass.
• Lifecycle: pending → reserved → consumed. For imports, the cleanest path is to upload as pending and immediately consume by attaching to the relevant conversations.id.

Two import paths:

1. Bulk-insert + post-attach: insert messages first, get back the assistant/user conversations.id, then write workstream_attachments rows linking the file to message_id.
2. SDK multipart create: create_workstream(attachments=[...], initial_message=...) for the first turn only — the server reserves and consumes them onto that turn. Doesn't help for mid-thread attachments.

For full-history imports with multiple attachments at different turns, path (1) is the only option.

Validation Checklist

Before declaring success, verify:

• ws_id is 32-char lowercase hex.
• workstreams row exists with the right user_id, state, kind.
• Conversation rows are inserted in order (autoincrement id will reflect insert order).
• Every assistant tool_calls[].id has a matching role="tool" row with the same tool_call_id.
• tool_calls[].function.arguments is a JSON-encoded string, not a parsed object.
• First message is typically role="user" (not system) — Turnstone composes its own system prompt at runtime.
• No empty assistant rows (content=NULL AND tool_calls=NULL is invalid).
• If multi-node: the ws_id's bucket maps to a node that exists; workstreams.node_id matches.
• Round-trip test: run Storage.load_messages(ws_id) and confirm the reconstructed list matches what you inserted (modulo timestamps).

Anti-patterns

• Don't import the source provider's system prompt verbatim. Provider boilerplate ("You are Claude...", "You are ChatGPT...") will conflict with Turnstone's composed system message and confuse the model on resume. Drop it; preserve only user-authored custom instructions.
• Don't preserve foreign tool definitions as Turnstone tools. If the source had custom tools that don't exist in Turnstone, the assistant rows that called them are still valid history (archive), but the workstream is not resumable — mark state="closed".
• Don't fabricate tool_call_ids without re-pairing. Mismatched ids silently break the replay chain on the next turn.
• Don't skip the tool_name field on role="tool" rows. Some load paths use it for display and audit; NULL there will render as "unknown tool".
• Don't write through the LLM (send() per turn) for full history. It's expensive, rewrites assistant turns, and rate-limits will bite long imports.

Quick Reference

Task	Path
Generate ws_id	secrets.token_hex(16)
Bulk insert messages	Storage.save_messages_bulk(rows)
Archive (read-only)	state="closed", skip provider_data
Resumable	state="idle", populate provider_data if same provider
Tool call id	OpenAI shape: {"id": ..., "type": "function", "function": {"name": ..., "arguments": "<json string>"}}
Tool result row	role="tool", tool_name, tool_call_id, content
Source role → Turnstone role	See "Role Mapping" table
Per-thread metadata	Store source IDs in workstream_config under import.* keys

Files to read before writing the importer

• turnstone/core/storage/_schema.py — authoritative table definitions.
• turnstone/core/storage/_protocol.py — save_message, save_messages_bulk, load_messages signatures.
• turnstone/core/session.py (around the message-save section) — how the runtime constructs in-memory message dicts; mirror this shape on import to round-trip cleanly.
• turnstone/api/server_schemas.py — Pydantic shapes for the SDK paths if you go through HTTP.