Reflect
SkillAI & modelsSpawn three parallel review subagents over the active transcript, surface learnings, and route each to a concrete edit on an existing skill. Use when the user says reflect.
Available today. Use it from your connected AI after setup.
No other account needed.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the Reflect skill
What this skill tells your AI
The instructions your AI receives, as published by scino/fstack in skills/reflect/SKILL.md and read by ahel’s review.
Mine the current conversation for durable learnings, then route them into skill edits.
Platform note. On Codex, the Claude tool names, claude-* slugs, and Claude built-in skills named below are Claude defaults. Resolve them via codex-tools.md.
When to invoke
- The user said "reflect" or "/reflect".
- A complex task (5+ tool calls) just landed cleanly and the recipe is worth keeping.
- The agent hit dead ends, found the working path, and the path generalizes.
- The user corrected the agent's approach mid-task.
- A non-trivial workflow emerged that isn't captured anywhere.
Skip when the conversation is trivial, off-topic, or already covered by an existing skill the parent followed correctly. One-offs are not learnings.
Process
1. Locate the active transcript
The parent finds its own transcript file before fanning out. The system prompt names Claude Code's per-project transcripts directory at ~/.claude/projects/<encoded-cwd>/; use that path. Do not glob across ~/.claude/projects/. That crosses workspace boundaries and reads private chats from unrelated projects.
ls -t ~/.claude/projects/<encoded-cwd>/*.jsonl 2>/dev/null | head -10
Three transcript layouts: legacy flat (<id>.jsonl), current nested (<id>/<id>.jsonl), and subagent (<parent>/subagents/<child>.jsonl).
For each candidate, read the first JSONL line and check that message.content[0].text contains the conversation's opening user prompt. Take the matching path. If no path resolves, write a tight digest of the session and pass that instead.
2. Spawn three reviewers in parallel
One message, three Agent calls, subagent_type: "general-purpose", explicit model: on each. Reviewers need MCP access for context lookups (tickets, chat threads, observability traces referenced in the transcript); pick a subagent_type that retains MCP access. The prompt forbids file writes; the parent applies edits.
| Lens | model | Prompt template |
|---|---|---|
| Judgment | your configured reflect-judgment model (default in Models) | references/judgment-reviewer.md |
| Tooling | your configured reflect-tooling model (default in Models) | references/tooling-reviewer.md |
| Divergent | your configured reflect-judgment model (default in Models) | references/divergent-reviewer.md |
Pass each template verbatim, substituting the transcript path or digest where marked. Reviewers return findings in the Agent response body.
3. Synthesize
One Agent call, subagent_type: "general-purpose", using your configured reflect-judgment model (default in Models). Pick a subagent_type that retains MCP access — the synthesizer's quality check includes spot-verifying citations, which can require MCP access. Use references/synthesizer.md verbatim, with each reviewer's full output inlined where marked. The synthesizer returns a structured Accepted / Rejected / Backlog list.
4. Structural enforcement check
Sanity-check the synthesizer's Accepted list. For any item that would be enforced more reliably by a lint rule, script, metadata flag, or runtime check, move it from Accepted to Backlog. The synthesizer already applies this criterion; this is a final pass before edits land. See the encode-lessons-in-structure principle skill.
5. Apply
Before applying any Accepted edit, present the synthesizer's full Accepted/Rejected/Backlog output to the user and wait for explicit approval. The user picks which subset to apply and may redirect routings. Skill changes affect every future agent in the org; do not auto-apply.
Backlog items file to whatever devex / backlog tracker your team uses automatically. Those are tracker submissions, not skill edits. Only the Accepted list waits for approval.
For each approved Accepted item, follow the Routing field exactly:
- Trivial existing-skill edit (a one-line bullet, a tightened sentence, a stale fact corrected): parent does directly.
- Substantive existing-skill edit (a new section, a new pattern table, more than ~10 lines): hand to the plugin-dev:skill-development skill and run its draft / test / iterate loop.
tune description: <skill path>(the skill exists but didn't trigger when it should have): hand toplugin-dev:skill-developmentand run its description-optimization loop.new skill via plugin-dev:skill-development: <kebab-name>: hand creation toplugin-dev:skill-development. Do not invent the shape ad hoc.
If your environment ships a SKILL.md validator, run it on every touched skill before declaring done. Skip this step if it doesn't.
6. Summarize for the user
Short list, no preamble:
- Edits applied:
<skill path>. What changed, one line each. - New skills created:
<skill path>. One line each (rare). - Backlog filed to the devex tracker:
<issue title>(<tags>). One line each. - Dropped: one line per rejected finding + reason from the synthesizer.
Models
Role defaults live in repo-root models.json. /setup-fstack writes a per-harness override sheet that wins at runtime.
- reflect tooling:
claude-opus-5 - reflect judgment, divergent, synthesizer:
claude-opus-5
Signals
- GitHub stars
- 21
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
reflect-scino- Source
- github.com/scino/fstack