Agent skill
episode-log-steps
Log execution steps during episode execution for detailed tracking and learning. Use when recording tool usage, decision points, errors, or milestones during task execution.
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/episode-log-steps
SKILL.md
Episode Log Steps
Log execution steps during episode execution for detailed tracking and learning.
Purpose
Record individual execution steps to build a complete picture of task execution for pattern learning.
When to Log Steps
- Tool usage: Each time a significant tool is used
- Decision points: When making architectural or implementation choices
- Error handling: When encountering and resolving errors
- Milestones: Key progress points (test passing, module complete)
Step Structure
pub struct ExecutionStep {
pub tool: String, // Tool/action used
pub action: String, // Description of action
pub latency_ms: u64, // Time taken
pub tokens: Option<u32>, // Tokens used (if applicable)
pub success: bool, // Whether step succeeded
pub observation: String, // Outcome/observations
}
Logging Guidelines
1. Batch When Appropriate
- Don't log every tiny operation
- Batch related steps when many occur quickly
- Log significant operations individually
2. Include Context
- Tool: cargo, rustfmt, clippy, git, etc.
- Action: Specific command or operation
- Observation: Result, output summary, or error
3. Track Performance
- Record latency for slow operations
- Note token usage for LLM calls
- Mark success/failure clearly
Examples
Build Step
let step = ExecutionStep {
tool: "cargo".to_string(),
action: "build --all".to_string(),
latency_ms: 12500,
tokens: None,
success: true,
observation: "Build successful, 0 warnings".to_string(),
};
memory.log_step(episode_id, step).await?;
Test Step
let step = ExecutionStep {
tool: "cargo".to_string(),
action: "test --test integration_test".to_string(),
latency_ms: 3200,
tokens: None,
success: false,
observation: "2 tests failed: test_batch_insert, test_concurrent_writes".to_string(),
};
memory.log_step(episode_id, step).await?;
Code Generation Step
let step = ExecutionStep {
tool: "claude".to_string(),
action: "generate async batch implementation".to_string(),
latency_ms: 4500,
tokens: Some(2800),
success: true,
observation: "Generated batch.rs with async Tokio patterns".to_string(),
};
memory.log_step(episode_id, step).await?;
Error Resolution Step
let step = ExecutionStep {
tool: "clippy".to_string(),
action: "check --all".to_string(),
latency_ms: 1500,
tokens: None,
success: true,
observation: "Fixed 3 warnings: unused imports, unnecessary clone".to_string(),
};
memory.log_step(episode_id, step).await?;
Batching Strategy
When multiple related steps occur in a burst (e.g., fixing multiple test failures):
let step = ExecutionStep {
tool: "cargo".to_string(),
action: "fix 5 test failures in batch_test.rs".to_string(),
latency_ms: 18000,
tokens: Some(3500),
success: true,
observation: "Fixed: test_batch_insert (await missing), test_concurrent (lock order), test_timeout (semaphore added)".to_string(),
};
memory.log_step(episode_id, step).await?;
Notes
- Steps are stored as JSON array in episode record
- Use clear, actionable observations
- Include error messages when relevant
- Keep observation strings concise but informative
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