Agent skill
iteration-control
Install this agent skill to your Project
npx add-skill https://github.com/jmagly/aiwg/tree/main/agentic/code/addons/guided-implementation/skills/iteration-control
SKILL.md
iteration-control
Manages bounded iteration loops for autonomous implementation with escalation.
Triggers
Alternate expressions and non-obvious activations (primary phrases are matched automatically from the skill description):
- "pause the loop" → loop control signal
- "stop after N cycles" → explicit iteration limit
Purpose
This skill provides iteration control logic for guided implementation workflows. It tracks retry attempts, synthesizes feedback from failures, and decides whether to retry autonomously or escalate to the user.
Based on MAGIS research finding: Developer-QA iteration loops with bounds improve code quality while preventing infinite loops.
Behavior
When invoked during a validation loop:
-
Track iteration state:
- Current iteration count
- Maximum allowed iterations (default: 3)
- Task identifier
-
Evaluate validation results:
- Test results (pass/fail)
- Review results (approve/reject/feedback)
- Error messages and stack traces
-
Synthesize feedback (on failure):
- Extract actionable items from test output
- Extract specific issues from review feedback
- Prioritize by severity
-
Decide action:
proceed: Validation passed, continue to next taskretry: Validation failed, iteration < max, retry with feedbackescalate: Validation failed, iteration >= max, pause for user
-
Format escalation (when needed):
- Summary of attempts made
- Consolidated feedback from all iterations
- Specific question or decision needed from user
Decision Logic
IF test_result == PASS AND review_result == APPROVE:
RETURN { action: "proceed" }
IF current_iteration >= max_iterations:
RETURN {
action: "escalate",
context: summarize_all_attempts(),
question: identify_blocking_issue()
}
IF test_result == FAIL:
RETURN {
action: "retry",
feedback: extract_test_feedback(),
iteration: current_iteration + 1
}
IF review_result == REJECT:
RETURN {
action: "retry",
feedback: extract_review_feedback(),
iteration: current_iteration + 1
}
Input Format
iteration_check:
task_id: "task-003"
current_iteration: 2
max_iterations: 3
test_result:
status: "fail" # pass | fail
output: |
FAIL src/auth/login.test.ts
Expected: token to contain userId
Received: undefined
review_result:
status: "pending" # approve | reject | pending
feedback: ""
Output Format
Proceed
decision:
action: "proceed"
task_id: "task-003"
message: "Validation passed. Proceeding to next task."
Retry
decision:
action: "retry"
task_id: "task-003"
iteration: 3
feedback:
summary: "Test failed: token missing userId"
actionable_items:
- "Ensure jwt.sign includes userId in payload"
- "Check that user object is populated before token generation"
priority: "high"
Escalate
decision:
action: "escalate"
task_id: "task-003"
iteration: 3
context:
attempts_summary: |
Iteration 1: Test failed - undefined token
Iteration 2: Test failed - token missing userId
Iteration 3: Test failed - userId present but wrong format
pattern_detected: "userId format mismatch between token and test expectation"
question: |
After 3 attempts, the test still fails due to userId format.
The token contains: { userId: "123" } (string)
The test expects: { userId: 123 } (number)
Which format should be used?
1. String (update test)
2. Number (update implementation)
Configuration
Default settings (can be overridden per-flow):
iteration_control:
max_iterations: 3
auto_retry_on_test_fail: true
auto_retry_on_review_reject: true
escalation_includes_diff: true
feedback_max_length: 500
Integration
Used by /flow-guided-implementation to wrap the validation loop:
FOR EACH task:
iteration = 0
LOOP:
generate_code()
run_tests() -> test_result
run_review() -> review_result
decision = iteration_control(task, iteration, test_result, review_result)
SWITCH decision.action:
"proceed": BREAK (next task)
"retry": apply_feedback(decision.feedback); iteration++; CONTINUE
"escalate": PAUSE; await_user_input(); CONTINUE or ABORT
Traceability
- @research @.aiwg/research/REF-004-magis-multi-agent-issue-resolution.md
References
- @.aiwg/working/guided-impl-analysis/SYNTHESIS.md
- @$AIWG_ROOT/agentic/code/addons/aiwg-utils/prompts/reliability/resilience.md
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