Back to Ecosystem Pulse
EvaluationExperimental
forsy-trace-skill
by ray-r-ren
Open skill to capture structured agent execution traces
Python
Updated Jun 6, 2026
Share:
How It Works
Captures agent activity as structured, time-ordered traces that record actions, tool calls, observations, and decision points. Emits a consistent trace schema that makes agent execution interpretable and machine-readable for downstream analysis. Designed as an open skill to plug into agent runtimes and collect trajectory data for evaluation and monitoring.
Why It Matters
As agents interact and delegate, raw logs don't reveal decision context or failure modes; structured tracesturn opaque runs into analyzable records. This enables reproducible A2A evaluation, aggregation of agent track records, and extraction of trust signals from execution history. Making traces first-class simplifies continuous agent evaluation and auditing across multi-agent workflows. A2A evaluation and broader agent workflows benefit from having standardized traces that feed into agent registry patterns.
When to Use
Researchers and engineers who need reproducible, machine-readable agent traces for evaluation, debugging, or building reputation signals. These traces support continuous monitoring and provide a foundation for auditing multi-agent interactions.
How It's Used
- Record step-by-step agent trajectories for post-hoc failure analysis
- Aggregate execution traces to compute agent reliability and trust signals
- Feed structured traces into evaluation harnesses or reputation systems
- Instrument agent runtimes for reproducible benchmarking and audits
Topics
agent-evaluationagent-tracesagent-workflowsai-agentsllm-agentspost-trainingprocess-supervisionreinforcement-learningtool-usetrajectory-data
Similar Tools
repkitagent-playground
Keywords
agent-tracesagent track recordA2A evaluationcontinuous agent evaluation