analyze
Deep-research analysis of a codebase, a topic, or both. Clarifies the question, agrees the research angles with you, then runs a…
Self-improvement from this project's Claude Code session logs. Mines a date range (default "yesterday") through parallel signal lenses, clusters what it finds into improvement opportunities, drafts a concrete change per opportunity, adversarially validates the cited evidence,
$ npx -y skills add rsmdt/the-startup --skill reflect --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/reflectContext preview
The summary Claude sees to decide when to auto-load this skill.
Self-improvement from this project's Claude Code session logs. Mines a date range (default "yesterday") through parallel signal lenses, clusters what it finds into improvement opportunities, drafts a concrete change per opportunity, adversarially validates the cited evidence,
name: reflect description: Self-improvement from this project's Claude Code session logs. Mines a date range (default "yesterday") through parallel signal lenses, clusters what it finds into improvement opportunities, drafts a concrete change per opportunity, adversarially validates the cited evidence, and reports improve/create proposals across the project harness — skills, subagents, output styles, the CLAUDE.md/AGENTS.md instruction, and guardrails (permission rules, hooks). Advisory — it never edits an artifact; you decide what to apply. user-invocable: true argument-hint: "date range (default: yesterday), e.g. 'last 3 days' or '2026-07-10..2026-07-16'"
Act as the self-improvement orchestrator. You turn a date range into a **reflect plan** (purpose, date range, signal lenses), get the user to approve it, then author and run **one dynamic workflow** (the Workflow tool with an inline script you write) that mines, clusters, proposes, validates, and reports. You do **not** mine logs, draft changes, or write the report yourself.
**Date range**: $ARGUMENTS
**Advisory is the identity.** reflect never edits or creates an artifact. It reports proposals, each labelled **improve** (edit an existing artifact) or **create** (a new skill, subagent, hook, or permission rule), with a concrete change and cited session-log evidence. A human applies them.
**In scope** — the project harness, all git-tracked: skills, subagents, and output styles under `plugins/`; the project `CLAUDE.md` / `AGENTS.md`; guardrails in the project `.claude/settings.json` (permission rules, hooks) and `CLAUDE.md` rules. **Out of scope:** the user's global `~/.claude/` files and other projects' logs.
**Always:**
**Never:**
Derive from `$ARGUMENTS` the date range (`since`, `until`, `label`), a slug `name` (e.g. `2026-07-16`), and a terse purpose. Choose the signal lenses — default all six:
Create `.start/reflect/[name]/` with a `.tracker/` directory and write `reflect.md` from template.md with `status: draft`.
Display in the chat: the purpose, the resolved date range with ISO dates, the lenses, the target scope, and that the run is advisory. Ask the user to *Approve* or *Refine*. On refine, update `reflect.md`, re-display, and ask again. On approve, set `status: approved`.
For each lens create a mine task and record its id:
`node ${CLAUDE_PLUGIN_ROOT}/scripts/task.js create --home .start/reflect/[name] --type mine --name "{lens}"`
These instructions are the opt-in: call the Workflow tool directly with an inline script. Do not load the workflow-authoring reference or assemble the script through shell commands first — runs that detour through either stall before launching. Read reference/workflow.md, reference/surfaces.md, and the agent references. Author one workflow script that implements the contract exactly, each agent's prompt taken from its reference with placeholders filled. Launch it with the Workflow tool, passing the resolved values as `args`. Set `status: in-progress`.
Choose `model` and `effort` per stage from what the stage does. Structured mining — scanning records for errors, interrupts, and token counts — can run on a smaller, faster model. Reading dialogue for inefficiency, spotting capability gaps, auditing instructions, clustering, validating, and grading need the strongest model. A validating or grading stage never runs on a weaker model than the stage it judges. When unsure, omit `model` so the stage inherits the session model.
Present a one-line dispatch summary, e.g. `Reflecting on "{name}": {n} lenses over {label} → dynamic workflow (advisory)`.
When the workflow returns (shape in reference/workflow.md):
1. Present the report: the per-surface ranking and the validated proposals grouped by surface, each with its change, label, evidence, and tier. Say how many were dropped in validation. Show the grade (`overall` and each dimension) and name every failing dimension — the report is not
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