self-improving-agent-builder

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Translated

Encodes a continuous improvement loop for goal-seeking agents: EVAL, ANALYZE, RESEARCH (hypothesis + evidence + counter-arguments), IMPROVE, RE-EVAL, DECIDE. Auto-commits improvements (+2% net, no regression >5%) and reverts failures. Works with all 4 SDK implementations. Auto-activates on "improve agent", "self-improving loop", "agent eval loop", "benchmark agents", "run improvement cycle".

1installs

Sourcerysweet/amplihack

Added on2026-03-22

NPX Install

npx skill4agent add rysweet/amplihack self-improving-agent-builder

SKILL.md Content

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Self-Improving Agent Builder

Purpose

Run a closed-loop improvement cycle on any goal-seeking agent implementation:

EVAL -> ANALYZE -> RESEARCH -> IMPROVE -> RE-EVAL -> DECIDE -> (repeat)

Each iteration measures L1-L12 progressive test scores, identifies failures with

error_analyzer.py

, runs a research step with hypothesis/evidence/ counter-arguments, applies targeted fixes, and gates promotion through regression checks.

When I Activate

"improve agent" or "self-improving loop"
"agent eval loop" or "run improvement cycle"
"benchmark agents" or "compare SDK implementations"
"iterate on agent scores" or "fix agent regressions"

Quick Start

User: "Run the self-improving loop on the mini-framework agent for 3 iterations"

Skill: Executes 3 iterations of EVAL->ANALYZE->RESEARCH->IMPROVE->RE-EVAL->DECIDE
       Reports per-iteration scores, net improvement, and commits/reverts.

Runner Script

The self-improvement loop is implemented as a Python CLI:

bash

# Basic usage
python -m amplihack.eval.self_improve.runner --sdk mini --iterations 3

# Full options
python -m amplihack.eval.self_improve.runner \
  --sdk mini \
  --iterations 5 \
  --improvement-threshold 2.0 \
  --regression-tolerance 5.0 \
  --levels L1 L2 L3 L4 L5 L6 \
  --output-dir ./eval_results/self_improve \
  --dry-run  # evaluate only, don't apply changes

Source:

src/amplihack/eval/self_improve/runner.py

The Loop (6 Phases per Iteration)

Phase 1: EVAL

Run the L1-L12 progressive test suite on the current agent implementation.

Execution:

bash

python -m amplihack.eval.progressive_test_suite \
  --agent-name <agent_name> \
  --output-dir <output_dir>/iteration_N/eval \
  --levels L1 L2 L3 L4 L5 L6

Output: Per-level scores and overall baseline.

Phase 2: ANALYZE

Classify failures using

error_analyzer.py

. Maps each failed question to a failure taxonomy (retrieval_insufficient, temporal_ordering_wrong, etc.) and the specific code component responsible.

python

from amplihack.eval.self_improve import analyze_eval_results

analyses = analyze_eval_results(level_results, score_threshold=0.6)
# Each ErrorAnalysis maps to:
#   failure_mode -> affected_component -> prompt_template

Phase 3: RESEARCH (New)

The critical thinking step that prevents blind changes. For each proposed improvement:

State hypothesis: What specific change will fix the failure?
Gather evidence: From eval results, failure patterns, baseline scores
Consider counter-arguments: What could go wrong? Risk of regression?
Make decision: Apply, skip, or defer with full reasoning

Decisions are logged in

research_decisions.json

for auditability.

Decision criteria:

Apply: Clear failure pattern + prompt template available + low score
Skip: Score above 50% (likely stochastic variation)
Defer: Ambiguous evidence, needs more data

Phase 4: IMPROVE

Apply the improvements approved by the research step. Priority order:

Prompt template improvements (safest, highest impact)
Retrieval strategy adjustments
Code logic fixes (most risky, needs careful review)

Phase 5: RE-EVAL

Re-run the same eval suite after applying fixes to measure impact.

Phase 6: DECIDE

Promotion gate:

Net improvement >= +2% overall score: COMMIT the changes
Any single level regression > 5%: REVERT all changes
Otherwise: COMMIT with marginal improvement note

Configuration

Parameter	Default	Description
`sdk_type`	`mini`	Which SDK: mini/claude/copilot/microsoft
`max_iterations`	`5`	Maximum improvement iterations
`improvement_threshold`	`2.0`	Minimum % improvement to commit
`regression_tolerance`	`5.0`	Maximum % regression on any level
`levels`	`L1-L6`	Which levels to evaluate
`output_dir`	`./eval_results/self_improve`	Results directory
`dry_run`	`false`	Evaluate only, don't apply changes

Programmatic Usage

python

from amplihack.eval.self_improve import run_self_improvement, RunnerConfig

config = RunnerConfig(
    sdk_type="mini",
    max_iterations=3,
    improvement_threshold=2.0,
    regression_tolerance=5.0,
    levels=["L1", "L2", "L3", "L4", "L5", "L6"],
    output_dir="./eval_results/self_improve",
    dry_run=False,
)

result = run_self_improvement(config)
print(f"Total improvement: {result.total_improvement:+.1f}%")
print(f"Final scores: {result.final_scores}")

4-Way Benchmark Mode

Compare all SDK implementations side by side:

User: "Run a 4-way benchmark comparing all SDK implementations"

Skill: Runs eval suite on mini, claude, copilot, microsoft
       Generates comparison table with scores, LOC, and coverage.

Integration Points

src/amplihack/eval/self_improve/runner.py

: Self-improvement loop runner

src/amplihack/eval/self_improve/error_analyzer.py

: Failure classification

src/amplihack/eval/progressive_test_suite.py

: L1-L12 eval runner

src/amplihack/agents/goal_seeking/sdk_adapters/

: All 4 SDK implementations

src/amplihack/eval/metacognition_grader.py

: Advanced eval dimensions

```
src/amplihack/eval/teaching_session.py
```
: L7 teaching quality eval