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`.
Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.
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.
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.
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.
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
Never let Teacher == Student. Validation is meaningless if the model
analyzing failures is the same one being validated against.
Build from successes too, not only failures — /upskill-build accepts
both, and good working patterns are just as worth distilling.
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.
Match serve mode to review appetite.interactive (default) keeps a
human in the loop per task; auto trades that for zero-friction matching.
Re-run /upskill-init after upstream releases instead of hand-patching
~/.claude/hooks/*.sh — it migrates legacy config safely.
Route pure skill-authoring or spec-compliance asks elsewhere — this
skill owns the distillation pipeline, not generic SKILL.md writing.
