English | 한국어
One objective in, a verified result out - the smallest correct change, checked against the real tests.
No extra install: clone the repo, symlink it into your skills directory, then /supergoal <objective>.
Landing page: cskwork.github.io/supergoal-skill .
An agent skill that takes a single objective, surfaces the requirements that are not in the prompt, makes the smallest correct change, and verifies it against the project's own tests and spec - then stops.
A strong model with the real spec is the bar. /supergoal adds only what a plain baseline cannot do for
free: it surfaces the requirements that are not in the prompt - as FAILING tests written by an
independent critic - then makes the smallest correct change and verifies it against the project's real
tests and spec, never a generated proxy. For a trivial single edit, skip the skill and edit directly.
Each role is a bundled file in agents/, so dispatch stays harness-agnostic across Claude Code, Codex,
agy, and other agent CLIs - but dispatch is optional and single-driver by default.
- Verify against ground truth. Re-run the project's REAL tests and re-read the prose spec for rules the tests miss. Never generate a proxy checklist/verifier and optimize to it.
- Smallest correct change. Match the surrounding code; no whole-file rewrites to change a few lines.
- Surface hidden requirements first. The one place a process beats a plain baseline.
- Ask only when genuinely ambiguous. Resolve code-answerable questions by reading the code.
- Hard stops. A destructive/irreversible step needs consent; if the real tests cannot pass, report it - never fake a pass.
/supergoal detects the mode from your objective:
| Objective looks like | Mode | Approach |
|---|---|---|
| "build / ship a new app/tool" | GREENFIELD | default loop |
| "fix / broken / failing / why does" | DEBUG | default loop; reproduce with a failing test first |
| "add X to our existing/legacy code" | LEGACY | default loop; map the code first |
| "spec this first - requirements/design/tasks docs" | SPEC | grill load-bearing decisions one question at a time; requirements -> design -> tasks crystallize under docs/spec/, then the default loop runs against them |
| "explain / teach me X" (no code) | LEARN | Intake -> Source -> Bridge -> Teach -> Check (explain-back) |
| "learn / map / onboard onto this codebase" | LEARN-DOMAIN | Survey -> Map -> Ground -> Persist a .domain-agent/ wiki |
| "QA only / verify / compare data - no code" | QA-ONLY | Exercise app + read-only DB -> evidence -> report.md |
| "review / audit this code/diff/PR - no fixes" | REVIEW-ONLY | Two independent reviewers -> verified findings -> report.md |
| "improve the architecture / find refactoring opportunities" | ARCH | Friction survey -> candidates report.md -> grill the pick -> refactor routes to LEGACY/SPEC |
| "test harness effectiveness / with vs without" | HARNESS-EVAL | Cases -> baseline run -> harness run -> machine checks -> quality score -> compare |
| "make a skill from history - no product code" | SKILL-MINE | Mine history -> rank -> you pick -> forge portable SKILL.md -> install |
Default loop (GREENFIELD / DEBUG / LEGACY), role-separated: 1) Frame the goal + acceptance criteria; 2) Build the smallest correct change, test-first (bug -> failing test first); 3) an independent Critic re-reads the spec and writes a FAILING test for each required behavior the existing tests miss; 4) a Fixer makes those pass with the smallest change; 5) Verify against the real tests and re-read the spec for uncovered rules - stop on green and report what was verified with command output.
/supergoal build a habit-tracker app and ship it
/supergoal the checkout page hangs intermittently in prod. fix it
/supergoal add SSO to our legacy Django monolith
/supergoal learn this codebase and build a domain wiki
/supergoal QA the checkout flow on staging and check the order totals match the DB (no code change)
/supergoal compare this migration harness with and without the harness on 3 cases
QA-ONLY, REVIEW-ONLY, ARCH, LEARN/LEARN-DOMAIN, HARNESS-EVAL, and SKILL-MINE are kept as separate-purpose utilities (no-code QA, findings-only review, teaching/onboarding, harness measurement, skill forging). They write no product code by default and confirm with you before installing anything.
This repo is the skill. Put it where your agent CLI finds skills:
git clone https://github.com/cskwork/supergoal-skill.git # then either symlink or copy it into the skills dir your agent uses: ln -s "$(pwd)/supergoal-skill" <your-agent-skills-dir>/supergoal # examples: ~/.claude/skills/supergoal, ~/.codex/skills/supergoal, ~/.agents/skills/supergoal
Then in your agent CLI: /supergoal <your objective>.
The skill runs on Windows; the remaining gate/test scripts are POSIX shell, so run them under Git Bash
or WSL (node must be on PATH). The repo pins .gitattributes eol=lf. Install by copy if
symlinks need admin rights (cp -R in Git Bash/WSL, or mklink /D from an elevated cmd); run the
contract tests under WSL bash.
SKILL.md thin spine: baseline-first loop, modes, reference map
agents/ one persona file per role (analyst, architect, executor, debugger, explore, designer, qa-*, db-reader, code-reviewer, security-reviewer)
reference/ domain-rules · domain-context · debugging · interview · plan-grounding · market-research · qa · qa-only · db-access · learn · learn-domain · ui-ux · taste-skill-v2 · functional-ui · harness-eval · skill-mine
learn/ LEARN-mode session journals + README template + USER_PREFERENCE(.template).md
templates/ qa-gate.sh · qa-only-gate.sh · contrast-gate.mjs · learn-grounding-gate.mjs · qa-report.md · db-access/ · domain-agent/ · domain-onboarding.html · harness-eval-gate.mjs · harness-eval-cases/ · skill-mine/ · skill-frontmatter-gate.mjs · skill.md.template
docs/ DESIGN.md · research-brief.md · experiments/ (the harness evals) · changelog/ · index.html (landing)
examples/url-shortener/ a worked example service exercised across the build / debug / extend modes
The design is grounded in head-to-head evals - docs/experiments/2026-06-07-harness-eval-* and
log/changelog-2026年06月07日.md (3 cases, 2 models, 4 harness forms). The result that shapes the skill: on
tasks with an explicit spec, a strong baseline that reads the real spec is the bar to beat, and optimizing
to a generated-proxy verifier can score worse via Goodhart. examples/url-shortener/ is a worked example
service exercised across the build, debug, and extend modes.
HARNESS-EVAL reusable sample cases come from RevFactory's claude-code-harness:
https://github.com/revfactory/claude-code-harness/
Concept and workflow adapted from oh-my-symphony by cskwork (https://github.com/cskwork/oh-my-symphony). Built as a portable agent skill.
MIT. See LICENSE.