Reflect

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

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.

1. Locate the active transcript

Before fanning out, find this conversation's transcript in the active workspace's session directory. Do not 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 agent-transcripts/ directory the system prompt names.

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

  Copy

  That covers the legacy flat (<id>.jsonl), current nested (<id>/<id>.jsonl), and subagent (<parent>/subagents/<child>.jsonl) layouts. For each candidate, read the first JSONL line. Take the file whose message.content[0].text contains the conversation's opening user prompt.

• 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.

Outside Cursor, take the newest candidates first and confirm one by finding the conversation's opening user prompt in its first user message. If none matches, pass a tight digest of the session instead.

2. Spawn three reviewers in parallel

One message, three Task calls, subagent_type: generalPurpose, agent mode (readonly: false). Reviewers need MCP access to look up the tickets, chat threads, and observability traces the transcript references, and 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, ~/.claude/rules/pstack-models.md in Claude Code, ~/.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. Pick each subagent's reasoning level and fast or standard speed for the job, per the Subagents section of the poteto-mode skill.

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, model from the reflect judgment, divergent, synthesizer line (default claude-opus-5-5-xhigh), agent mode (readonly: false). It spot-verifies citations through MCP, and readonly strips MCPs. Pass references/synthesizer.md verbatim, with each reviewer's full output inlined where marked. It returns an Accepted / Rejected / Backlog list.

4. Structural enforcement check

Move any Accepted item that a lint rule, script, metadata flag, or runtime check would enforce more reliably to Backlog. See the encode-lessons-in-structure principle skill.

5. Apply

Present the synthesizer's full Accepted / Rejected / Backlog output and wait for explicit approval before applying any Accepted edit. The user picks the subset and may redirect routings. Skill changes affect every future agent in the org. Do not auto-apply.

File each Backlog item to your team's devex or backlog tracker without waiting. Only the Accepted list waits for approval.

Never edit a skill under plugins/cache/. Before approval, show its destination: .cursor/skills/<skill>/ or ~/.cursor/skills/<skill>/ in Cursor, or your harness's project or user skill folder, such as .claude/skills/<skill>/ or ~/.agents/skills/<skill>/. After approval, copy the skill folder there and edit the copy. Never open a PR or push.

Follow each approved row's Routing exactly:

• Trivial existing-skill edit (a one-line bullet, a tightened sentence, a stale fact corrected): the parent does it 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.

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.