reflect

Reflect

Mine the current conversation for durable learnings, then route them into skill edits.

When to invoke

Invoke when the user says "reflect" or "/reflect". 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. Use only the active workspace's transcript directory. Never glob across every project's directory. That crosses workspace boundaries and reads private chats from unrelated projects.

Where the transcript lives depends on your harness:

• Cursor: the system prompt names the active workspace's agent-transcripts/ directory. Use that path.

  ls -t <agent-transcripts>/*.jsonl <agent-transcripts>/*/*.jsonl <agent-transcripts>/*/subagents/*.jsonl 2>/dev/null | head -10
  

  Three transcript layouts: legacy flat (<id>.jsonl), current nested (<id>/<id>.jsonl), and subagent (<parent>/subagents/<child>.jsonl).

• Claude Code: ~/.claude/projects/<slug>/<session-id>.jsonl, where <slug> is the workspace path with every character that isn't a letter or digit turned into "-".

• Pi: ~/.pi/agent/sessions/--<slug>--/*.jsonl, where <slug> is the workspace path with the leading slash dropped and each "/" turned into "-".

• Codex: ~/.codex/sessions/YYYY/MM/DD/rollout-*.jsonl. Keep only files whose first line has payload.cwd equal to the workspace path.

• Other harnesses: check the harness's session directory for this workspace.

Take the newest candidates first. Confirm a candidate by finding the conversation's opening user prompt in its first user message. 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 Task calls, subagent_type: generalPurpose, with model set as below, agent mode (readonly: false). Reviewers need MCP access for context lookups (tickets, chat threads, observability traces referenced in the transcript). Readonly strips MCPs.

Other harnesses. The spawns in this skill use Cursor's Task tool. In another harness, use its subagent tool: Agent in Claude Code (subagent_type: general-purpose), task in OpenCode (subagent_type: general), spawn_agent in Codex. Keep the prompt and the model. Drop parameters your tool doesn't have. If your harness has no subagent tool, as in Pi without an extension, run each role yourself, one after another.

Each reviewer and the synthesizer name a role line in the pstack settings file (~/.cursor/rules/pstack-models.mdc in Cursor, ~/.agents/pstack-models.md in other harnesses) and a default. Set model to that line's value, or to the default if the file or the line is missing. Leave model unset when the value is auto or inherit-parent. If your subagent tool rejects a slug, use the default and say so. If it rejects the default, use the closest valid slug of the same family from its error message or your harness's model list.

Lens	Role line	Default model	Prompt template
Judgment	reflect judgment, divergent, synthesizer	claude-opus-5-5-max	references/judgment-reviewer.md
Tooling	reflect tooling	gpt-5.6-sol-max	references/tooling-reviewer.md
Divergent	reflect judgment, divergent, synthesizer	claude-opus-5-5-max	references/divergent-reviewer.md

Pass each template verbatim, substituting the transcript path or digest where marked. Reviewers return findings in the Task response body.

3. Synthesize

One Task call, subagent_type: generalPurpose, with model from the reflect judgment, divergent, synthesizer line (default claude-opus-5-5-max), agent mode (readonly: false). The synthesizer's quality check includes spot-verifying citations, which can require MCP access. Readonly strips MCPs. 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. 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. 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 your harness's skill-authoring skill and run its draft / test / iterate loop. That is create-skill in Cursor (built in) or Anthropic's skill-creator. If you have neither, follow the Agent Skills format at agentskills.io. "create-skill" below means whichever of these you use.
• tune description: <skill path> (the skill exists but didn't trigger when it should have): hand to create-skill and run its description-optimization loop.
• new skill via create-skill: <kebab-name>: hand creation to create-skill. 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.