Improvement Generator
PassAudited by ClawScan on May 10, 2026.
Overview
This appears to be a local improvement-candidate generator, with noteworthy but purpose-aligned use of history/trace inputs and shared local helper code.
This skill looks acceptable for generating local improvement candidates. Use explicit target/source/trace paths, keep history files scoped, and review the JSON candidates before any downstream executor applies changes. For higher assurance, inspect the shared lib.common and lib.state_machine modules because they are referenced but not included here.
Findings (3)
Artifact-based informational review of SKILL.md, metadata, install specs, static scan signals, and capability signals. ClawScan does not execute the skill or run runtime probes.
Running the script may execute helper code outside this skill package, which limits what can be verified from the provided artifacts alone.
The script depends on shared repository helper modules that are not included in the provided manifest, so the visible skill behavior is partly dependent on external local code.
_REPO_ROOT = Path(__file__).resolve().parents[3] ... sys.path.insert(0, str(_REPO_ROOT)) ... from lib.common import ... read_json ... write_json ... from lib.state_machine import ... update_state
Run it only in a trusted OpenClaw repository environment and review the referenced shared libraries if you need a complete code audit.
Bad, stale, or overly broad history files could steer future improvement suggestions in the wrong direction.
The skill intentionally uses historical memory, feedback, and failure traces to influence future generated candidates.
结合 memory 和 feedback 多源信号生成高优先级候选 ... With memory | Candidates informed by historical patterns and past successes
Provide only scoped and trusted trace, memory, or feedback files, and inspect generated candidates before passing them to discriminator, gate, or executor stages.
A poor generated candidate could affect downstream improvement workflows if later approval or gate stages are too permissive.
The generated candidates are designed to feed a larger improvement pipeline where later components may score, gate, and execute changes.
在 autoloop 场景下由 orchestrator 自动调用，注入历史 trace ... improvement-executor: Executes the top-ranked candidate approved by gate
Keep human or gate review in place before execution, especially for non-documentation changes or candidates marked medium/high risk.