upskill

name	upskill
description	Turn a weak/cheap "Flash" model into a "Pro" performer by wrapping HKUDS UpSkill — captures agent session failures, has a strong Teacher model analyze them and draft a skill, then validates it against the weak Student model in a closed Ralph Loop (up to 3 rounds) before storing it for automatic reuse. Use when the user wants to install UpSkill, run `/upskill-init`, `/upskill-configure`, `/upskill-build`, `/upskill-run`, `/upskill-list`, `/upskill-status`, `/upskill-mode`, `/upskill-model`, `/upskill-remove`, or `/upskill-uninstall`, wants a cheap model to perform closer to a Pro model without switching, or wants a good session (success or failure) distilled into a validated skill. Triggers on: upskill, up-skill, flash to pro, teacher student distillation, ralph loop skill validation, distill agent failures into skills. Routes skill-quality ratcheting to `skill-autoresearch`, scaffolding to `write-a-skill`, and spec-compliance rewrites to `skill-standardization`.
allowed-tools	Bash Read Write Edit Glob Grep WebFetch
compatibility	Reference implementation targets Claude Code (session hooks + skills + CLAUDE.md context injection). Requires bash, python3, git, and curl (for remote install). The Teacher/Student roles are model presets in `~/.claude/upskill.conf`, independent of the daily driver model set in Claude Code `settings.json`. The core building pipeline (worktree isolation, Ralph Loop, skill store) is harness-agnostic per upstream docs; porting to Codex/OpenClaw/Cursor needs an equivalent session-hook + context-injection layer.
metadata	{"tags":"upskill, knowledge-distillation, teacher-student, ralph-loop, skill-generation, claude-code-hooks, terminal-bench, cost-optimization","platforms":"Claude Code","keyword":"upskill","version":"1.0.0","license":"MIT","upstream":"https://github.com/HKUDS/UpSkill","installer":"curl -sSL https://raw.githubusercontent.com/HKUDS/Upskill/main/cc-integration/install.sh | bash -s -- --remote","source":"akillness/jeo-skills"}

upskill — Distill Agent Failures Into Validated Skills

Keyword: upskill · flash to pro · teacher student distillation · ralph loop skill validation

HKUDS/UpSkill (MIT) turns a cheap/weak "Flash" model into a "Pro" performer without a model upgrade. When a session fails, it captures the full context, has a strong Teacher model analyze the failure and draft a skill, then validates that skill against the weak Student model in a closed Ralph Loop (up to 3 rounds) before storing it. Validated skills auto-inject into every future session via a CLAUDE.md index (always in context) plus a full SKILL.md loaded on demand. On Terminal-Bench 2.0, a Flash model + UpSkill (51.6% pass rate, $0.04/task) beat the Pro model it was validated against (50.0%, $0.06/task) — a 41% lower cost result documented in tb_harbor_2.0/RESULTS.md.

This skill is the routing-first wrapper: it installs the tool, wires the three roles (Daily / Teacher / Student), and walks the daily capture → build → validate → serve loop.

When to use this skill

• The user wants to install UpSkill or run any /upskill-* slash command
• The user wants a cheap model ("Flash", Haiku, mini, DeepSeek-Flash) to perform closer to a Pro model on recurring task categories
• A session just failed and the user wants to turn that failure into a reusable, validated skill instead of just retrying manually
• The user wants to distill a successful session into a reusable skill (not just failures — /upskill-build works on either)
• The user asks how the Ralph Loop, Teacher/Student roles, or skill-store serve modes (interactive vs auto) work

When not to use this skill

• The user wants generic guidance on writing or standardizing a SKILL.md from scratch → use write-a-skill or skill-standardization
• The user wants a repo-local skill-quality ratcheting loop (freeze a benchmark, mutate one change, keep/revert by score) for this jeo-skills repo → use skill-autoresearch
• The user wants a general model fine-tuning / RLHF pipeline → that changes weights; UpSkill only prepends context, it never trains anything
• The user is not on Claude Code and has no equivalent session-hook / context-injection surface → see Porting first; do not promise parity with an unadapted harness

The three roles

Role	Set in	Purpose
Daily model	Claude Code settings.json	Whatever the user runs day to day — untouched by UpSkill
Teacher	~/.claude/upskill.conf	Strong model — analyzes failures, drafts skills (e.g. claude-opus-4-7, deepseek-v4-pro[1m])
Student	~/.claude/upskill.conf	Weak model — every skill must be validated against it before storage (e.g. claude-haiku-4-5, deepseek-v4-flash)

Prerequisites

Requirement	Notes
Claude Code	Reference integration lives in cc-integration/
bash, python3, git	Required by hooks and the build pipeline
curl	Only for the remote one-line installer
API access to a Teacher and a Student model	Anthropic or DeepSeek recommended by upstream

Instructions

Step 1 — Install

# Remote (recommended)
curl -sSL https://raw.githubusercontent.com/HKUDS/Upskill/main/cc-integration/install.sh | bash -s -- --remote

# Local (from a cloned repo)
git clone https://github.com/HKUDS/UpSkill && cd UpSkill/cc-integration && bash install.sh

# Or via this skill's wrapper script (adds jeo-skills plugin registration too)
bash scripts/install.sh                 # remote install (default)
LOCAL_REPO=/path/to/UpSkill bash scripts/install.sh   # install from an existing clone

This writes ~/.claude/{hooks,skills,upskill-store}/ and ~/.claude/upskill.conf. To update later, run /upskill-init inside Claude Code — it re-runs the installer and migrates legacy config.

Step 2 — Configure Teacher / Student models

Edit ~/.claude/upskill.conf:

UPSKILL_TEACHER="deepseek-v4-pro[1m]"   # or: claude-opus-4-7
UPSKILL_STUDENT="deepseek-v4-flash"     # or: claude-haiku-4-5
UPSKILL_SERVE_MODE="interactive"        # interactive (default) | auto

The Daily model is unaffected — it stays whatever Claude Code's own settings.json selects. Run /upskill-model to view the current preset, or bash ~/.claude/hooks/upskill-store.sh sync after a manual edit.

Step 3 — Enable building per project

/upskill-configure

Merges UserPromptSubmit, SessionStart, and SessionEnd hooks plus a claudeMd pointer into .claude/settings.local.json for the current project — additive, never overwrites existing hook entries.

Step 4 — Use the agent normally; skills build themselves

A SessionEnd hook captures a failed session (verify.sh present, non-zero exit) and sets a pending flag. Next session start, Claude Code prints:

[upskill] ⚠ 1 pending failure(s) ready for building. Run /upskill-build to generate skills.

Run /upskill-build (works on failures and successes) and the pipeline runs through 5 phases in an isolated git worktree — see references/pipeline-and-ralph-loop.md for the full Teacher → generate → Ralph-validate sequence.

Step 5 — Serve

Validated skills appear automatically in the agent's context via the global ~/.claude/upskill-store/CLAUDE.md index (~5 lines/skill). In interactive mode (default), run /upskill-run to browse matches (★ = recommended) and apply one. In auto mode, skills are keyword-matched on every prompt and proactively suggested. Switch modes with /upskill-mode auto|interactive.

Step 6 — Manage the library

/upskill-list                                   # browse skills by category
/upskill-status                                 # skill count + active builds
/upskill-remove --category data-analysis --skill-id skill_20260605_001
/upskill-uninstall                              # remove hooks/skills/config

Full command reference: references/commands.md.

How it works — the Ralph Loop

Most distillation approaches stop at "Teacher writes advice, hope it helps." UpSkill closes the loop instead: the Student retries the task with the draft skill applied, and if it still fails, the Teacher revises based on "what went wrong even with guidance" — up to 3 rounds — discarding the skill if it never passes. This calibrates every stored skill to what the Student model can actually follow, not generic best practices. Architecture, worktree isolation, and the 3-file skill format (Domain Knowledge / Step-by-Step / Feedback-Lessons) are detailed in references/pipeline-and-ralph-loop.md.

Porting to other agent harnesses

The core pipeline (build script, Ralph Loop, skill store) is harness-agnostic; only the hook-integration and context-injection layer is Claude Code-specific. Porting to Codex, OpenClaw, or Cursor needs: (1) before/after-session hook equivalents, (2) a CLAUDE.md-equivalent auto-loaded context mechanism, (3) slash-command equivalents, (4) worktree/sandbox isolation for Teacher/Student runs. Do not claim full parity on an unadapted harness — state the gap.

Output format

When the user asks upskill for help, return a compact brief:

# upskill Routing Brief

## Scope
- Stage: install | configure-models | configure-project | build | serve | manage
- Roles: Teacher=<model> Student=<model> (Daily model unaffected)
- Serve mode: interactive | auto

## Recommended next move
- one concrete `/upskill-*` command or install step

## Why
- 2-3 bullets grounded in the user's packet

## Route-outs
- `skill-autoresearch` for repo-local skill-quality ratcheting
- `write-a-skill` / `skill-standardization` for hand-authoring a SKILL.md

Examples

Example 1: First-time setup for a cost-conscious team

Install with the remote one-liner, set UPSKILL_TEACHER="claude-opus-4-7" and UPSKILL_STUDENT="claude-haiku-4-5" in ~/.claude/upskill.conf, then run /upskill-configure in each active project. The Daily model stays whatever the team already uses in settings.json.

Example 2: A recurring CSV-parsing task keeps failing on Haiku

After a failure, /upskill-build picks the failed session, the Teacher drafts a skill, and the Ralph Loop retries it against Haiku up to 3 rounds. Once it passes, the skill auto-appears in the global CLAUDE.md index and is offered via /upskill-run (interactive) or auto keyword-match next time a similar CSV task starts.

Example 3: Distilling a successful session

/upskill-build is not failure-only — run it on a session that went well to capture the working approach as a reusable, Student-validated skill before the pattern is forgotten.

Best practices

1. Never let Teacher == Student. Validation is meaningless if the model analyzing failures is the same one being validated against.
2. Build from successes too, not only failures — /upskill-build accepts both, and good working patterns are just as worth distilling.
3. Trust the Ralph Loop's discard. A skill that fails all 3 rounds is dropped by design — do not manually force-save an unvalidated draft.
4. Match serve mode to review appetite. interactive (default) keeps a human in the loop per task; auto trades that for zero-friction matching.
5. Re-run /upskill-init after upstream releases instead of hand-patching ~/.claude/hooks/*.sh — it migrates legacy config safely.
6. Route pure skill-authoring or spec-compliance asks elsewhere — this skill owns the distillation pipeline, not generic SKILL.md writing.

References

• Upstream repo: https://github.com/HKUDS/UpSkill
• Full CC integration docs: https://github.com/HKUDS/UpSkill/blob/main/cc-integration/README.md
• Terminal-Bench 2.0 results: https://github.com/HKUDS/UpSkill/blob/main/tb_harbor_2.0/RESULTS.md
• Command reference: references/commands.md
• Pipeline + Ralph Loop detail: references/pipeline-and-ralph-loop.md
• Installer script: scripts/install.sh
• Adjacent skills: ../skill-autoresearch/SKILL.md, ../write-a-skill/SKILL.md, ../skill-standardization/SKILL.md
• License: MIT (see upstream LICENSE)