analyzing-codebases
Detects project languages and monorepo state, runs language-appropriate static analysis (dependency graph, complexity,…
Scans the session, suggests what is worth reflecting on, and lets the user pick which learnings to deepen. Use when completing significant work, when the user corrects your approach, when a bug is resolved, or when new patterns emerge. Use when user says "reflect", "what did we
Claude Code
$ npx -y skills add wayne930242/Reflexive-Claude-Code --skill reflecting --agent claude-code
Codex
$ npx skills add wayne930242/Reflexive-Claude-Code --skill reflecting -a codex -y
Cursor
$ npx skills add wayne930242/Reflexive-Claude-Code --skill reflecting -a cursor -y
Gemini CLI
$ npx skills add wayne930242/Reflexive-Claude-Code --skill reflecting -a gemini-cli -y
GitHub Copilot
$ npx skills add wayne930242/Reflexive-Claude-Code --skill reflecting -a github-copilot -y
OpenCode
$ npx skills add wayne930242/Reflexive-Claude-Code --skill reflecting -a opencode -y
Windsurf
$ npx skills add wayne930242/Reflexive-Claude-Code --skill reflecting -a windsurf -y
name: reflecting description: Scans the session, suggests what is worth reflecting on, and lets the user pick which learnings to deepen. Use when completing significant work, when the user corrects your approach, when a bug is resolved, or when new patterns emerge. Use when user says "reflect", "what did we learn", "capture learnings". argument-hint: "[pain-point]"
**Reflecting IS surfacing candidate learnings, then deepening only the ones the user picks.**
Scan the session, list what is *worth* reflecting on with a one-line reason each, and let the user choose. Do not force every event into a full learning report. Capture before context fades; deepen on demand.
**When to reflect:** after a significant feature, a resolved bug, repeated failed attempts, an unexpected discovery, or a long session. Don't wait for "later" — context fades fast.
**Pattern:** suggest → pick → deepen **Hand off:** `planning-agent-systems`, opt-in (only when a learning should become a component)
Scan the conversation for moments worth reflecting on. For each, write ONE line:
`[type] short description — why it's worth a look`
Types: **correction** (user corrected you), **error** (mistake / multiple attempts), **discovery** (new insight about project/domain/tooling), **repetition** (same action repeated — automation candidate), **safety_bypass** (destructive/irreversible action without confirmation, or a safety check skipped).
Rules:
Present the list and ask: **which do you want to deepen?** (some, all, or none.)
For each candidate the user picks:
1. **Extract the learning** — what would prevent the failure or repeat the success, and where it applies. 2. **Locate the router** — which skill/rule/law/CLAUDE.md routed the behavior. Glob the path; "none" if it came from general knowledge. 3. **Suggest where the fix lands** — simplest component that works:
4. **Debug session** — capture two things separately: the bug itself (root cause + prevention rule) and any reasoning error made while debugging.
Write the picked learnings to `.rcc/{YYYY-MM-DD}-reflection.md` using `references/report-template.md`.
Default: present the suggested fixes; let the user apply them.
To turn a learning into a component automatically, hand off to `planning-agent-systems` with the reflection path. Otherwise stop — do not auto-create components.
A Claude Code plugin marketplace for skills-driven Agentic Context Engineering (ACE) — build, analyze, and maintain agent systems with structured workflows.
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Ask which skill or flow fits your situation. A router over the skills in this repo.