/observe-trace
Trace agent execution by collecting spans and building a trace tree for a task
$ npx -y skills add ruvnet/claude-flow --skill observe-trace --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
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- Slash command
/observe-trace
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Trace agent execution by collecting spans and building a trace tree for a task
SKILL.md
observe-trace.SKILL.mdname: observe-trace
description: Trace agent execution by collecting spans and building a trace tree for a task
argument-hint: "<task-id>"
allowed-tools: mcp__plugin_ruflo-core_ruflo__memory_search mcp__plugin_ruflo-core_ruflo__memory_list mcp__plugin_ruflo-core_ruflo__agentdb_semantic-route mcp__plugin_ruflo-core_ruflo__agentdb_context-synthesize mcp__plugin_ruflo-core_ruflo__agentdb_pattern-search Bash
Observe Trace
Collect distributed trace spans for a task and build a visual trace tree showing the execution flow, timing, and bottlenecks.
When to use
When you need to understand how a task was executed across agents -- which spans ran, how long each took, where bottlenecks occurred, and how agents coordinated.
Steps
1. **Collect spans** -- call `mcp__plugin_ruflo-core_ruflo__memory_search --namespace observability` (or `memory_list`) to retrieve all spans matching the `<task-id>`. The `memory_*` tool family routes by namespace; `agentdb_hierarchical-*` does NOT (it routes by tier `working|episodic|semantic`), so use `memory_*` here. See [ruflo-agentdb ADR-0001 §"Namespace convention"](../../../ruflo-agentdb/docs/adrs/0001-agentdb-optimization.md). 2. **Build trace tree** -- organize spans into a parent-child hierarchy using `parentSpanId` references, with the root span at the top 3. **Calculate timing** -- for each span, compute duration (endTime - startTime), and identify the critical path (longest chain of sequential spans) 4. **Identify bottlenecks** -- flag spans where duration exceeds the p95 for that operation type, or where gaps between spans suggest idle time 5. **Synthesize** -- call `mcp__plugin_ruflo-core_ruflo__agentdb_context-synthesize` to combine span metadata into a narrative summary of the execution flow 6. **Report** -- display the trace tree with: span name, agent, duration, status (OK/ERROR), and bottleneck flag; include total trace duration and critical path duration
CLI alternative
npx @claude-flow/cli@latest memory search --query "trace spans for task TASK_ID" --namespace observability
Read more
name: observe-trace description: Trace agent execution by collecting spans and building a trace tree for a task argument-hint: "<task-id>" allowed-tools: mcp__plugin_ruflo-core_ruflo__memory_search mcp__plugin_ruflo-core_ruflo__memory_list mcp__plugin_ruflo-core_ruflo__agentdb_semantic-route mcp__plugin_ruflo-core_ruflo__agentdb_context-synthesize mcp__plugin_ruflo-core_ruflo__agentdb_pattern-search Bash
Observe Trace
Collect distributed trace spans for a task and build a visual trace tree showing the execution flow, timing, and bottlenecks.
When to use
When you need to understand how a task was executed across agents -- which spans ran, how long each took, where bottlenecks occurred, and how agents coordinated.
Steps
1. **Collect spans** -- call `mcp__plugin_ruflo-core_ruflo__memory_search --namespace observability` (or `memory_list`) to retrieve all spans matching the `<task-id>`. The `memory_*` tool family routes by namespace; `agentdb_hierarchical-*` does NOT (it routes by tier `working|episodic|semantic`), so use `memory_*` here. See [ruflo-agentdb ADR-0001 §"Namespace convention"](../../../ruflo-agentdb/docs/adrs/0001-agentdb-optimization.md). 2. **Build trace tree** -- organize spans into a parent-child hierarchy using `parentSpanId` references, with the root span at the top 3. **Calculate timing** -- for each span, compute duration (endTime - startTime), and identify the critical path (longest chain of sequential spans) 4. **Identify bottlenecks** -- flag spans where duration exceeds the p95 for that operation type, or where gaps between spans suggest idle time 5. **Synthesize** -- call `mcp__plugin_ruflo-core_ruflo__agentdb_context-synthesize` to combine span metadata into a narrative summary of the execution flow 6. **Report** -- display the trace tree with: span name, agent, duration, status (OK/ERROR), and bottleneck flag; include total trace duration and critical path duration
CLI alternative
npx @claude-flow/cli@latest memory search --query "trace spans for task TASK_ID" --namespace observability
An agent meta-harness for Claude Code and Codex. Agent = Model + Harness. The model writes; the harness gives it tools, memory, loops, sandboxes, and controls so it can actually work.
Repo: ruvnet/claude-flow
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