lanyasheng Improvement Generator
Produces ranked improvement candidates from target analysis, feedback signals, and failure traces.
When to Use
- 为目标 skill 生成结构化改进候选
- 把上次失败的 trace 注入下一轮（trace-aware reflection）
- 根据 trace 自动降低上次失败类别的候选优先级
- 结合 memory 和 feedback 多源信号生成高优先级候选
- 批量生成多个 skill 的候选列表供 discriminator 打分
- 在 autoloop 场景下由 orchestrator 自动调用，注入历史 trace
- 手动调试单个 skill 的改进方向时作为独立工具使用
- 对比有/无 trace 生成结果来验证 trace 注入是否生效
When NOT to Use
- 给候选打分 → use
improvement-discriminator - 评估 skill 结构 → use
improvement-learner - 全流程 → use
improvement-orchestrator - 执行已批准的变更 → use
improvement-executor - 门禁验证 → use
improvement-gate
Why Trace-Aware Generation Matters
问题: 没有 trace 注入时，LLM 每次都从零开始生成候选。如果上一轮在 accuracy 维度失败了，下一轮很可能再次生成相同类别的候选 — 因为 LLM 不知道上次失败了。实测中无 trace 重试的重复失败率高达 60-70%。
Tradeoff: trace 注入增加了 prompt 长度（约 200-500 tokens），但大幅降低了重复失败率。Because trace 包含失败维度、失败原因、已尝试策略三个关键信号，generator 可以在生成阶段就避开已知死路，而不是等到 discriminator 打分后才发现。这比 "生成 → 打分 → 发现重复 → 重新生成" 的循环节省 1-2 轮迭代。
Trace-Aware Generation
Previous failure on "accuracy" dimension
→ deprioritize candidates of the same category as the failed one
→ prioritize other dimensions' improvements instead
→ if same category failed ≥2 times, skip entirely and try adjacent dimensions
Trace JSON Structure
trace 文件记录上一轮失败的完整上下文，generator 解析后调整候选优先级：
{
"iteration": 2,
"failed_dimension": "accuracy",
"failed_category": "add_code_examples",
"failure_reason": "code example added but not syntactically valid",
"attempted_strategies": ["append_bash_example", "append_python_snippet"],
"scores_before": {"accuracy": 0.67, "coverage": 0.85},
"scores_after": {"accuracy": 0.63, "coverage": 0.85}
}
generator 收到这个 trace 后会：(1) 把 add_code_examples 类别的优先级降到最低，(2) 从 coverage/trigger_quality 等未失败维度寻找候选，(3) 如果 accuracy 下的其他类别（如 add_output_artifacts）未尝试过则仍可生成。
CLI
# Basic generation
python3 scripts/propose.py --target /path/to/skill --output candidates.json
# With failure trace (retry loop)
python3 scripts/propose.py --target /path/to/skill --trace failure.json --output candidates.json
# With memory/feedback sources
python3 scripts/propose.py --target /path/to/skill --source memory.json --output candidates.json
Output Artifacts
| Request | Deliverable |
|---|---|
| Generate | JSON array of ranked candidates with category, risk_level, execution_plan |
| With trace | Same format, priorities adjusted based on failure analysis |
| With memory | Candidates informed by historical patterns and past successes |
| With feedback | Candidates prioritized by user correction hotspots |
每个候选的 JSON 结构包含 category（改进类别）、risk_level（low/medium/high）、execution_plan（具体修改步骤）、priority_score（0-1 综合优先级）、trace_adjusted（是否被 trace 调整过优先级）。
Related Skills
- improvement-discriminator: Scores the candidates this skill produces
- improvement-orchestrator: Calls generator as stage 1
- improvement-learner: Provides evaluation data that informs candidate selection
- improvement-executor: Executes the top-ranked candidate approved by gate
- session-feedback-analyzer: Generates feedback.jsonl that feeds into candidate prioritization