stream-of-consciousness – OpenClaw Skill

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name: stream-of-consciousness description: Export the entire conversation context into Open-Token format (including tools and optional internal traces) for agent collaboration, auditability, and reproducibility. version: 0.1.0

Open-Token Export (SKILL)

Objective

When invoked, output the entirety of the currently available conversation in Open-Token format as a single export artifact, suitable for:

• agent handoff / continuation across runtimes and providers
• audit & incident review
• reproducibility and debugging
• provenance and diffing

The output MUST be machine-parseable and complete per the chosen mode.

Invocation

Parse $ARGUMENTS as space-separated key=value pairs.

Supported options:

• mode=json|ndjson
  Default: json

• pretty=true|false
  Default: true for json; false for ndjson

• include=visible-only|include-internal
  Default: visible-only

• internal=redacted|summary|full
  Default: redacted
  Meaning:

  • redacted: include internal events only as placeholders (no content)
  • summary: include brief summaries of internal traces (if available)
  • full: include internal traces verbatim (ONLY if available and permitted)

• redact=none|secrets|pii|strict
  Default: secrets

• max_bytes=<int> (optional)
  If present, apply truncation rules defined below.

Availability rule for internal traces

If include=include-internal is requested but internal traces (hidden reasoning, hidden system routing, hidden intermediate tokens) are not available in the current runtime context, DO NOT fabricate them.

In that case:

• set conversation.internal_availability="unavailable"
• omit internal-trace content events or export them as redacted placeholders, consistent with internal=redacted
• still export tool calls/results if they are available

Non-negotiable rules

1. Export only what is available in the current conversation context; NEVER invent missing turns.
2. Preserve causal order; DO NOT reorder events.
3. Preserve attribution: correct actor, role, and event type.
4. Tool outputs MUST be isolated in tool_result events.
5. Apply redaction according to the redact option (unless redact=none).
6. If output is truncated, annotate truncation deterministically (see Truncation section).
7. Ensure the final output is valid JSON (or valid NDJSON per line) with no surrounding commentary.

Open-Token schema v0.1

Top-level object (mode=json)

Emit exactly one JSON object:

{
  "open_token_version": "0.1",
  "exported_at": "RFC3339 timestamp in UTC if available, else omit",
  "conversation": {},
  "participants": [],
  "events": [],
  "integrity": {}
}

Constraints:

• No extra top-level keys besides those listed (omit keys you cannot populate).
• events MUST be in strictly increasing sequence order.

Streaming export (mode=ndjson)

Emit newline-delimited JSON records:

1. Header line: { "type": "header", "open_token_version": "0.1", "exported_at": "..." }

2. Then one line per event: { "type": "event", ...event object... }

3. Optional footer line: { "type": "footer", "integrity": { ... } }

Constraints:

• Every line MUST be valid JSON.
• Do not wrap NDJSON in an array.

conversation object

conversation fields:

• id (required): stable identifier if provided by runtime; else generate conv_<YYYYMMDD>_<hash8>
• title (optional)
• started_at (optional; RFC3339; do not guess)
• timezone (optional)
• source_runtime (optional): "cli"|"web"|"api"|"ide"|"other"
• provider (optional): "openai"|"anthropic"|"google"|"meta"|"other"
• internal_availability (optional): "available"|"unavailable"|"unknown"
• redaction (required if redact != none):
  • mode: none|secrets|pii|strict
  • strategy: mask|drop|hash
  • notes: array of high-level notes (no secrets)

participants array

Each participant:

{ "actor_id": "act_###", "kind": "human|model|tool|system", "name": "string", "provider": "string (optional)", "model": "string (optional)", "instance_id": "string (optional)" }

Rules:

• Create one participants entry per distinct speaker/agent/tool/system originator.
• Use kind="system" for system/developer prompt originators.
• Use kind="tool" for external tools/functions.
• Use kind="model" for model/agent outputs (including subagents).

events array

Event shape

Each events[] entry MUST follow:

{ "id": "evt_000001", "seq": 1, "ts": "RFC3339 UTC timestamp (optional if unknown)", "type": "message|tool_use|tool_result|span_start|span_end|annotation", "actor_id": "act_###", "visibility": "public|internal|metadata", "role": "system|developer|user|assistant|assistant_thought|tool", "content": { "mime": "text/plain|application/json", "text": "string (optional)", "data": {} }, "links": { "parent_id": "evt_###### (optional)", "replies_to": "evt_###### (optional)", "call_id": "call_###### (optional)", "span_id": "span_###### (optional)" }, "usage": { "input_tokens": 0, "output_tokens": 0, "reasoning_tokens": 0 } }

Rules:

• id REQUIRED; seq REQUIRED and must be contiguous (1..N).
• ts OPTIONAL; do not guess timestamps.
• usage OPTIONAL; include only if available.
• If content.text is used, content.mime should be text/plain.
• If content.data is used, content.mime should be application/json.

Role mapping guidance

Map provider concepts to role + visibility:

• System prompt → role="system", visibility="internal" (or public if explicitly shown)
• Developer instruction → role="developer", visibility="internal"
• User message → role="user", visibility="public"
• Assistant final answer → role="assistant", visibility="public"
• Hidden reasoning trace:
  • if available AND include=include-internal:
    • role="assistant_thought", visibility="internal"
    • content depends on internal:
      • full: include verbatim thought content
      • summary: include a brief summary string
      • redacted: include placeholder with no thought content
  • else:
    • omit thought content; set conversation.internal_availability="unavailable" when applicable
• Tool invocation request → type="tool_use", role="assistant", visibility="internal"
• Tool output → type="tool_result", role="tool", visibility="internal"

Tool event requirements

For every tool call:

• Emit one tool_use event and one tool_result event.
• Both MUST share the same links.call_id.
• tool_use.content.data MUST include:
  • tool_name (string)
  • arguments (object/array)
• tool_result.content MUST contain ONLY the tool output.
  • If structured: content.mime="application/json" and put output in content.data
  • If text: content.mime="text/plain" and put output in content.text

If a tool call was initiated but no result exists in context:

• Still emit the tool_use event.
• Emit a tool_result event with:
  • content.mime="application/json"
  • content.data={"missing_result":true}
  • and include conversation.redaction.notes or an event-level annotation if relevant.

Nested agents and subagents

If subagents exist:

• Represent each subagent as its own participant (kind="model").
• Wrap subagent activity in a span:
  • span_start event opens span (links.span_id)
  • subagent events include links.span_id
  • span_end event closes span

If spawn metadata is available, put it in span_start.content.data:

• spawn_reason
• requested_capabilities
• tooling_scope
• model (if specified)

Redaction

Apply redaction according to redact:

• none: no redaction (still avoid emitting known-prohibited private keys if policy requires)
• secrets: mask API keys, auth tokens, passwords, session cookies, private keys
• pii: additionally mask emails, phone numbers, street addresses, direct personal identifiers
• strict: mask secrets + pii + any internal-only configuration strings and untrusted tool outputs that may contain sensitive data

Mechanics:

• Replace sensitive substrings with: [REDACTED:<type>:<hash8>]
• Do not change surrounding punctuation unless necessary.
• Record high-level notes in conversation.redaction.notes (no secret values).

Truncation

If max_bytes is set and output exceeds limit:

1. Prefer truncating large content.text fields:
   • keep first 1024 chars + last 256 chars, insert …
2. Mark truncated content:
   • If content.mime="text/plain":
     • add content.data={"truncated":true,"original_length":<int if known>} and keep text truncated
   • If content.mime="application/json":
     • include {"truncated":true} in the JSON structure where applicable
3. Do NOT drop events unless explicitly requested; if you must drop, drop oldest first and insert an annotation event describing the omission.

Integrity (optional)

If feasible, include:

"integrity": { "hash_alg": "sha256", "canonicalization": "json-c14n-like", "events_hash": "hex string" }

If not feasible, omit integrity.

Output checklist (must pass)

• Output is valid JSON (or NDJSON per line).
• seq is contiguous and strictly increasing.
• No fabricated timestamps or missing turns.
• Tool calls: tool_use paired with tool_result via call_id (or explicit missing-result marker).
• Redaction applied per redact option.
• Internal traces included only if available; otherwise marked unavailable and not fabricated.
• No extraneous text outside the export payload.

Minimal example (json)

{
  "open_token_version": "0.1",
  "exported_at": "2026-01-31T00:00:00Z",
  "conversation": {
    "id": "conv_20260131_ab12cd34",
    "provider": "unknown",
    "source_runtime": "unknown",
    "internal_availability": "unavailable",
    "redaction": {
      "mode": "secrets",
      "strategy": "mask",
      "notes": ["masked api keys"]
    }
  },
  "participants": [
    { "actor_id": "act_001", "kind": "system", "name": "system" },
    { "actor_id": "act_002", "kind": "human", "name": "user" },
    { "actor_id": "act_003", "kind": "model", "name": "assistant", "provider": "unknown", "model": "unknown" },
    { "actor_id": "act_004", "kind": "tool", "name": "CalculatorAPI" }
  ],
  "events": [
    {
      "id": "evt_000001",
      "seq": 1,
      "type": "message",
      "actor_id": "act_001",
      "visibility": "internal",
      "role": "system",
      "content": { "mime": "text/plain", "text": "You are a math tutor." }
    },
    {
      "id": "evt_000002",
      "seq": 2,
      "type": "message",
      "actor_id": "act_002",
      "visibility": "public",
      "role": "user",
      "content": { "mime": "text/plain", "text": "What is 5 factorial?" }
    },
    {
      "id": "evt_000003",
      "seq": 3,
      "type": "tool_use",
      "actor_id": "act_003",
      "visibility": "internal",
      "role": "assistant",
      "content": {
        "mime": "application/json",
        "data": { "tool_name": "CalculatorAPI", "arguments": { "op": "factorial", "n": 5 } }
      },
      "links": { "call_id": "call_000001" }
    },
    {
      "id": "evt_000004",
      "seq": 4,
      "type": "tool_result",
      "actor_id": "act_004",
      "visibility": "internal",
      "role": "tool",
      "content": { "mime": "text/plain", "text": "120" },
      "links": { "call_id": "call_000001" }
    },
    {
      "id": "evt_000005",
      "seq": 5,
      "type": "message",
      "actor_id": "act_003",
      "visibility": "public",
      "role": "assistant",
      "content": { "mime": "text/plain", "text": "5! equals 120." }
    }
  ]
}