Mentor — /insights-style report on how you work with agents

Why this exists

Agents now do what juniors used to. But juniors became seniors by getting feedback from seniors. Give agents to juniors and fire the seniors, and nobody gets that feedback loop anymore. This skill is that loop: it reads your own local session history and hands back a single report — what you build, where you lose time, and concrete changes to get better. Effectiveness, not token-golf.

The deliverable is the rendered report.html file — always. The eight-section analysis is intermediate: it goes into analysis.json and gets rendered, never pasted into chat. A wall of analysis text in the reply is the failure this skill exists to fix. If you cannot run the scripts, say so plainly — do not substitute a text summary. Modeled on Claude Code's built-in /insights.

Agents

Agent	arg	Logs
Claude Code	claude	~/.claude/projects/<slug>/<uuid>.jsonl
OpenAI Codex	codex	~/.codex/sessions/**/rollout-*.jsonl (+ ~/.codex/archived_sessions/)
both	both (default)	union of the two

Workflow — build the report

1. Collect every session in the window into one stats bundle (deterministic):

   uv run --no-project python ~/.claude/skills/mentor/scripts/collect.py \
     --agent <claude|codex|both> --days <N> --out /tmp/mentor-stats.json
   

   Default --agent both --days 30. It writes stats.json and prints a compact digest: headline numbers, tool mix, languages, projects, and every session's verbatim first prompt. Read that digest — it is your evidence.

2. Analyze into eight sections. Read references/best-practices.md for the canon, then write /tmp/mentor-analysis.json (schema in scripts/render_report.py header). Ground every claim in the digest (numbers, project names, prompt patterns). The eight sections, mirroring /insights:

   • what_you_work_on — sessions clustered into themes, with rough share.
   • how_you_use — patterns: tool ratios (Bash/exec vs Read), languages, both-agent split, checkpoints.
   • impressive — real wins, handed back.
   • friction — where time leaks. Each: problem, evidence, concrete fix. This section earns the report.
   • features_to_try — existing features they under-use.
   • claude_md_additions — copy-paste CLAUDE.md lines that kill a recurring friction.
   • new_ways — new ways to use the agent(s).
   • on_the_horizon — where the workflow is heading. Also write a summary that does not flatter — name what is hindering them.

3. Render the self-contained HTML:

   uv run --no-project python ~/.claude/skills/mentor/scripts/render_report.py \
     --stats /tmp/mentor-stats.json --analysis /tmp/mentor-analysis.json \
     --out ~/.claude/usage-data/mentor-report.html
   

4. Deliver the file. Open or hand over ~/.claude/usage-data/mentor-report.html: in Claude Code use SendUserFile with display render; in Codex or a shell, open it (open <path>) and print the path. Then add at most two spoken lines: the single highest-leverage fix. The report carries the detail; the chat does not repeat it.

Quick single-session mode (only on explicit request)

Only when the user explicitly points at ONE session ("разбери вот эту сессию"), skip collect/render and run the per-session digest, then give a short text read (TL;DR, what worked, what dragged down, one habit). Any "разбери мои сессии" / "/mentor" goes through the report pipeline above, not this.

uv run --no-project python ~/.claude/skills/mentor/scripts/digest.py <path|last> --agent <claude|codex>

Rules

• The report.html is the answer. Never paste the eight sections as a chat message.
• Analyze the process, not the code produced. Don't re-review what was built.
• Evidence or it didn't happen — every section ties to the stats digest (numbers, projects, prompts). No generic advice.
• Codex routes almost everything through one exec tool, so exec-heavy is normal there — judge read-before-edit, not tool name.
• Some judgments are model-inferred (satisfaction, themes) — the report footer already flags this; don't overclaim.
• Write the report content in the user's language, defaulting to English. Set the analysis lang field ("en" | "ru") so the static chrome matches. Keep prose clean, concrete, and free of AI-slop; for Russian, hold it to the humanizer-ru standard.
• The friction section is the point. Each friction gets a concrete, copy-pasteable fix (a CLAUDE.md line, a hook, a habit).