claude-reflect vs SkillX
Claude Code plugin that learns from your corrections — hooks capture them in-session, /reflect syncs approved learnings to CLAUDE.md/AGENTS.md, /reflect-skills mines history into reusable commands. — versus — Research framework that auto-distills agent trajectories into a three-level skill knowledge base (planning, functional, atomic) — pluggable into weaker agents and new environments.
Both turn agent experience into reusable knowledge: claude-reflect captures your corrections into CLAUDE.md pragmatically, SkillX distills full trajectories into a transferable skill hierarchy — research-grade.
| claude-reflect | SkillX | |
|---|---|---|
| Stars | 1.3k | 261 |
| Forks | 109 | 24 |
| Language | Python | Python |
| License | MIT | MIT |
| Last activity | 4 months ago | 12 days ago |
| Topics | coding, memory | skills |
| Curated connections | 9 | 2 |
claude-reflect — the curator's take
Install claude-reflect the third time you catch yourself typing the same correction into Claude Code. It's the pragmatic take on agent memory: no vector DB, no service — hooks queue corrections, you review, markdown files get smarter, and the AGENTS.md sync means Codex/Cursor/Aider benefit too. The /reflect-skills pattern-mining is the sleeper feature: 15 similar requests become one command. When NOT: if you expect actual memory infrastructure (semantic recall, knowledge graphs) — this is disciplined note-taking with AI triage, personal-scale by design. Everything lands via human review, which is a feature, not friction.
SkillX — the curator's take
The interesting research bet: instead of storing raw trajectories or reflections, distill agent experience into a hierarchy of reusable skills that transfer — a strong backbone agent builds the library, weaker agents plug it in and improve on AppWorld/BFCL/τ2-Bench. Read it if you're building agent-improvement loops; the three-level decomposition (planning/functional/atomic) is a genuinely useful mental model. NOT production tooling — it's an academic codebase (~250 stars) built around benchmarks, so expect to adapt it to your stack rather than pip-install it.