teixeirazeus/fablize-for-hermes

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fablize-for-hermes — run like Fable, on Hermes Agent

A port of fablize for the Hermes Agent ecosystem. This project adapts fablize's verified procedures — verification grounding, multi-story evidence gating, investigation protocol, and early-stop prevention — from Claude Code's plugin system to Hermes Agent's skill system.

fablize makes any agent see a task through to the end — with evidence and verification — as procedure, not as luck.

The origin

fablize was born from a controlled comparison of Claude Opus and "Fable 5" (19 A/B runs + 26 real sessions, ~1,500 tool calls). The finding: on closed, answer-bearing work (code, logic, builds) the two models were effectively tied. The gap was in depth — following an implication one step further — and that turned out to be model capability, not injectable.

But the procedure of good work — actually running what you build, decomposing multi-step tasks with evidence checkpoints, investigating bugs systematically — does transfer. fablize ships only the procedures whose effect was verified.

What transfers and what doesn't

What's included

1. Hermes Agent skill (skills/fablize/SKILL.md)

The core behavioral ruleset, structured as a Hermes Agent skill (YAML frontmatter + markdown). Covers:

• Foundation rules — always-on: lead with outcome, ground claims in tool results, no early stops, confirm destructive actions
• Multi-story loop — decompose 2+ story tasks → complete with evidence → verification gate on the final story
• Deep investigation — reproduce → 3+ competing hypotheses → trace causal chain → verify before/after → report rejected hypotheses
• Verification grounding — run render/executable artifacts, observe, fix, re-run before declaring done
• Capability ceiling — escalate honestly when the model's ceiling is the blocker

2. Goal engine (scripts/goals.py)

A self-contained, stdlib-only Python script for multi-story decomposition with a verification gate. State persists in ~/.hermes/fablize/ and survives session death.

goals.py create --brief "Build a dashboard" \
  --goal "design::Create the HTML layout" \
  --goal "implement::Add chart visualizations" \
  --goal "verify::Run and verify the dashboard renders correctly"
goals.py next
# ... work this story ...
goals.py checkpoint --id G001 --status complete --evidence "Layout created with sidebar and main panel"
goals.py status

3. Packs — contextual discipline files

• packs/verification-grounding-pack.txt — The grounding loop: run, observe, fix, re-run. For HTML, SVG, games, UI, charts, animations.
• packs/investigation-protocol.txt — The systematic debugging protocol: reproduce, competing hypotheses, causal chain, verify before/after.

4. Setup scripts (setup/)

• setup.sh [--skill-only|--local|--global] — Install the skill into Hermes and optionally inject the operating block into AGENTS.md or CLAUDE.md
• uninstall.sh [--skill-only|--local|--global|--all] — Reverse the setup

Differences from original fablize

Install

Quick install (skill only)

git clone https://github.com/teixeirazeus/fablize-for-hermes.git
cd fablize-for-hermes
bash setup/setup.sh --skill-only

Then load the skill in your Hermes session:

skill_view(name='fablize')

Full install (skill + always-on block)

bash setup/setup.sh --local    # injects block into ./AGENTS.md
bash setup/setup.sh --global   # injects block into ~/.claude/CLAUDE.md

Uninstall

bash setup/uninstall.sh --all

How it behaves

• Trigger: load the fablize skill, or trigger inline when the agent detects 2+ stories, debugging, or render artifacts
• 2+ stories → decompose + verification gate via goals.py
• Debugging → investigation protocol (reproduce → hypotheses → causal chain)
• Render artifact → verification grounding (run → observe → fix → re-run)
• Hard task → adaptive thinking + escalate to stronger model if stuck
• Capability ceiling → escalate honestly (the harness does not fake capability)

Honest limits

• It cannot raise model capability. Open-ended creative quality and self-driven discovery are out of reach — that is a model-choice decision, not a harness one.
• The effect numbers come from the original fablize small-family self-measurement. The direction is solid; the decimals are not asserted.
• The early-stop rule relies on agent self-enforcement, not a deterministic hook. It is a guideline, not a guard.

License

MIT — same as the original fablize. The upstream fablize by fivetaku is MIT licensed. This port adapts the design and procedures to the Hermes Agent ecosystem.