.claude/skills/qmd-learning-loopRuntime, accounts, dependencies, permissions, network behavior and task quality remain untested.
Capture and promote durable agent learnings in QMD-indexed Markdown when reflection or reusable memory is requested.
These states come from the source or distribution context. None of the entries below are SkillVetAI compatibility test results.
These checks parse the fixed package against dated platform rules. They do not execute the Skill or verify task behavior.
.claude/skills/qmd-learning-loopRuntime, accounts, dependencies, permissions, network behavior and task quality remain untested.
.agents/skills/qmd-learning-loopRuntime, accounts, dependencies, permissions, network behavior and task quality remain untested.
skills/qmd-learning-loopRuntime, accounts, dependencies, permissions, network behavior and task quality remain untested.
This command is recorded from the source ecosystem and resolves the registry's latest release. The fixed release shown on this page should be inspected before adoption.
clawhub install @shakerg/qmd-learning-loopclawhub inspect @shakerg/qmd-learning-loop --version 1.1.0This automated, non-executing scan is bound to this release hash. It is not a safety certification and may contain false positives or false negatives.
ignore prior instructionsThis is registry-supplied evidence for the recorded release, not an independent SkillVetAI scan. Check the canonical source for the full report, scanner versions, scope, and current moderation state.
The catalog stores hashes and an inventory summary for change detection. It does not republish the package contents.
sha256:9520ed01e7c47ffe8e8f7597f031c58fb13278e0676b2f51fd3a96c4c430e5af.clawhubignore.github/workflows/validate.ymlCONTRIBUTING.mdLICENSEREADME.mdreferences/destination-discovery.mdreferences/evaluation-cases.mdreferences/qmd-workflow.mdreferences/review-loop.mdreferences/templates.mdscripts/validate_skill.pySECURITY.mdskill-card.mdSKILL.mdRedesigned the learning workflow with QMD-aware search, deduplication, privacy safeguards, explicit approval gates, deterministic promotion rules, richer templates, evaluation cases, MIT-0 licensing, and automated ClawHub validation.