本文へ移動
cccskills
無料GitHub で公開

fablize

A harness that makes Opus (or any Claude model) behave like Fable — it enforces seeing a task through to the end, with evidence and verification, as procedure. Use when starting a multi-step task (2+ sequential stories), long autonomous work, debugging or root-cause investigation, building render/executable artifacts (HTML, SVG, games, charts), or when the user says "fablize", "see it through", "verify as you go", "split into goals".

インストール方法を見る

含まれるファイル(15)

  • SKILL.md6.8 KB
  • packs/investigation-protocol.txt1.4 KB
  • packs/verification-grounding-pack.txt2.7 KB
  • scripts/gate/classify_task.py2.9 KB
  • scripts/gate/ledger.py5.5 KB
  • scripts/gate/parse_tool_result.py5.8 KB
  • scripts/gate/verify_state.py2.3 KB
  • scripts/goals.py5.9 KB
  • scripts/shadow/analyze.py3.6 KB
  • scripts/shadow/outcome_collect.py3.2 KB
  • scripts/shadow/shadow_collect.py3.8 KB
  • scripts/shadow/shadow_logger.py2.5 KB
  • setup/fablize-block.md1.7 KB
  • setup/setup.sh6.1 KB
  • setup/uninstall.sh1.2 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

fablize — run Opus like Fable

Principle: a harness cannot raise a model's ceiling. It makes the model go all the way to its own ceiling — by enforcing verification, completion, and investigation as procedure. When the capability ceiling is the blocker (open-ended creative detail, self-driven discovery), escalate (§4).

Apply only what the task signals (smallest matching discipline; overlap only when genuinely multi-category). When installed always-on, this routing is automatic.

Helper root

Before running any fablize helper command, resolve the installed helper root from the repo root:

FABLIZE_ROOT="${CLAUDE_PLUGIN_ROOT:-}"
if [ -z "$FABLIZE_ROOT" ]; then
  for candidate in .claude/skills/fablize .cursor/skills/fablize .agents/skills/fablize; do
    if [ -f "$candidate/scripts/goals.py" ]; then FABLIZE_ROOT="$candidate"; break; fi
  done
fi
[ -n "$FABLIZE_ROOT" ] || { echo "fablize: installed helper root not found"; exit 1; }

0. First run — set up automatically (once)

Before doing the requested task, check whether fablize has been onboarded on this machine:

cat ~/.fablize/progress.json 2>/dev/null
  • If the file exists — skip onboarding, go straight to the task.
  • If it is missing — onboard once with a single AskUserQuestion. Phrase the question and options in the user's current conversation language (detect it from recent messages — Korean, English, Japanese, etc.).
    • Question (meaning, translate to the user's language): "Set up fablize?"
    • Options (meaning, translate): "Local — this project only (recommended)" / "Global — all projects" / "Skip".
    • On Local/Global — run setup (it injects the block and writes progress.json; it never stars anything by itself), then continue with the task:
      bash "${FABLIZE_ROOT}/setup/setup.sh" <local|global>
      
      After setup, run bash "${FABLIZE_ROOT}/setup/setup.sh" ask — if it prints STAR_ASK <lang>, ask once via AskUserQuestion (in the user's language) whether they'd like to give the repo an optional GitHub star, then run setup.sh star yes or setup.sh star no accordingly. If it prints nothing, skip silently.
    • On Skip — record it so it won't ask again, then continue:
      mkdir -p ~/.fablize && printf '{"setup_done":false,"skipped":true}' > ~/.fablize/progress.json
      

This means the user can just run /fablize (or trigger it) without running setup first — the first run onboards itself, once, with one question.

1. Multi-story loop (2+ sequential stories)

Decompose into sequential stories and complete one at a time, producing evidence as you go. Self-contained — no external goal system required. Run from the repo root; state persists in ./.fablize/ (resume with status even across sessions).

python3 "${FABLIZE_ROOT}/scripts/goals.py" create --brief "<summary>" \
  --goal "title::verifiable objective" --goal "title::..."   # the last goal must be a verification story
python3 "${FABLIZE_ROOT}/scripts/goals.py" next         # activate a story + handoff
# ... work that story only ...
python3 "${FABLIZE_ROOT}/scripts/goals.py" checkpoint --id G001 --status complete --evidence "<concrete evidence>"
# the final story is a verification gate: --verify-cmd "<command>" --verify-evidence "<result>" are required
python3 "${FABLIZE_ROOT}/scripts/goals.py" status       # first command when resuming

Rules: complete requires non-empty evidence; the final goal cannot complete without a verify command and its result (the engine refuses). If blocked, record --status blocked and report. Single-step tasks skip this loop.

2. Deep investigation (debugging / unknown cause / review)

Read and follow ${FABLIZE_ROOT}/packs/investigation-protocol.txt: reproduce first → form 3+ competing hypotheses → gather evidence per hypothesis → trace the full causal chain (removing the symptom is not removing the defect) → verify before and after → report the hypotheses you rejected. For reviews, report everything including low-confidence findings and filter in a separate step.

3. Verification grounding (render/executable artifacts — always)

For artifacts whose correctness only shows when run (HTML, SVG, games, UI, charts), follow ${FABLIZE_ROOT}/packs/verification-grounding-pack.txt: run it in the real renderer → observe the actual output → fix what the observation reveals → re-run. A static parse confirms well-formed, not correct.

3-1. Working style (always)

Lead with the outcome. Stay within the requested scope (no incidental refactors or abstractions). Ground every completion claim in a tool result from this session. Confirm before destructive or hard-to-reverse actions.

4. At the capability ceiling (escalate)

Signals you have hit the model's ceiling: stuck on the same problem 2+ times; open-ended creation where detail itself is the value; deep review that needs out-of-spec discovery. These are capability, not procedure, and a harness cannot fill them. In order: (1) adaptive thinking already scales with difficulty — recommend /effort xhigh to the user to push the current model to its ceiling; (2) reactive effort delegation — if the blocker is a bounded, hard slice (not the whole task), delegate just that slice to a background Workflow with effort:'max' (model inherited): package the evidence (symptoms, attempts, failure point, repro, the specific sub-question) as the agent() prompt, force a structured return via schema, then resume with its result as authoritative. This is the only real per-task effort knob in a normal session — the Agent tool exposes model but no effort; only Workflow/Agent SDK do. Opt-in, and not yet proven on real work (the shadow layer in docs/MEASUREMENT_PROTOCOL.md measures whether it helps): use it for a genuinely stuck slice, not routinely, and never trigger it from risk/deep classification alone — that over-escalates simple high-risk tasks (false-escalate); (3) if still short, hand off to a stronger model in a fresh session with the same evidence package; (4) otherwise report the limit honestly and name where a human must step in.

Install (always-on, optional)

Run once: bash "${FABLIZE_ROOT}/setup/setup.sh" → choose local (recommended) or global. Uninstall: bash "${FABLIZE_ROOT}/setup/uninstall.sh". The UserPromptSubmit router hook registers automatically when the plugin is installed.

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

When the user wants to plan, design, or implement an A/B test or experiment, or build a growth experimentation program. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "should I test this," "which version is better," "test two versions," "statistical significance," "how long should I run this test," "growth experiments," "experiment velocity," "experiment backlog," "ICE score," "experimentation program," or "experiment playbook." Use this whenever someone is comparing two approaches and wants to measure which performs better, or when they want to build a systematic experimentation practice. For tracking implementation, see analytics. For page-level conversion optimization, see cro.

日本語の概要は準備中です。原文の説明を表示しています。

nota-america/forgecat-agent-profiles902026年9月24日 更新

Generate tiered sentence frames for academic tasks that scaffold language production across proficiency levels. Use when EAL students need structured language support for classroom discourse.

日本語の概要は準備中です。原文の説明を表示しています。

nota-america/forgecat-agent-profiles902026年9月24日 更新

When the user wants to generate, iterate, or scale ad creative — headlines, descriptions, primary text, or full ad variations — for any paid advertising platform. Also use when the user mentions 'ad copy variations,' 'ad creative,' 'generate headlines,' 'RSA headlines,' 'bulk ad copy,' 'ad iterations,' 'creative testing,' 'ad performance optimization,' 'write me some ads,' 'Facebook ad copy,' 'Google ad headlines,' 'LinkedIn ad text,' or 'I need more ad variations.' Use this whenever someone needs to produce ad copy at scale or iterate on existing ads. For campaign strategy and targeting, see ads. For landing page copy, see copywriting.

日本語の概要は準備中です。原文の説明を表示しています。

nota-america/forgecat-agent-profiles902026年9月24日 更新

Generate a cascading hint sequence for a problem type, revealing progressively without giving answers. Use when designing tutoring dialogues or scaffolded worksheets.

日本語の概要は準備中です。原文の説明を表示しています。

nota-america/forgecat-agent-profiles902026年9月24日 更新

ads

無料

When the user wants help with paid advertising campaigns on Google Ads, Meta (Facebook/Instagram), LinkedIn, Twitter/X, or other ad platforms. Also use when the user mentions 'PPC,' 'paid media,' 'ROAS,' 'CPA,' 'ad campaign,' 'retargeting,' 'audience targeting,' 'Google Ads,' 'Facebook ads,' 'LinkedIn ads,' 'ad budget,' 'cost per click,' 'ad spend,' or 'should I run ads.' Use this for campaign strategy, audience targeting, bidding, and optimization. For bulk ad creative generation and iteration, see ad-creative. For landing page optimization, see cro.

日本語の概要は準備中です。原文の説明を表示しています。

nota-america/forgecat-agent-profiles902026年9月24日 更新

Map control, influence, and concern after systems analysis. Use when students need wise agency without being made responsible for everything.

日本語の概要は準備中です。原文の説明を表示しています。

nota-america/forgecat-agent-profiles902026年9月24日 更新

nota-america のスキルをすべて見る

このスキルの問題を報告する