- 01Vague instruction
The task starts before success is made explicit
- 02Context drift
Important state lives only inside the conversation
- 03Blind retries
The same class of failure receives more of the same response
- 04Looks done
Completion is inferred from output rather than evidence
Your model is capable of more than the way you run it
get-fable adds frontier-style execution discipline around AI coding agents with specs, persistent task state, lifecycle hooks, reusable skills, failure handling, and verification
Why do frontier models feel different?
Raw model capability is part of the answer. It is not the whole answer
A strong agent experience also depends on what happens around the model: how the task is framed, what context survives between turns, when implementation starts, how repeated failures change behavior, which skills are available, and what counts as proof that the work is finished
get-fable focuses on that part: the execution environment around the model. It does not change model weights and it does not claim equivalence with Claude Fable 5, Claude Mythos 5, GPT-5.6 Sol, or any other frontier model
How much of frontier-style execution can we bring to the coding agents we already use?
Same model. Different run
- 01Spec before work
Goal, approach, checks, dependencies, and decisions stay visible
- 02Persistent task state
The ledger keeps execution state outside the chat
- 03Failure changes strategy
Repeated command failures become a different problem to diagnose
- 04Evidence before close
Unfinished work and missing proof can block completion
Six ways to change the run without changing the model
Not more prompting. Better execution conditions around the work
- 01
Plan before implementation
Turn intent into a concrete project spec before expensive execution starts
- 02
Keep task state outside the chat
Use durable files for tasks, progress, decisions, and evidence instead of trusting conversation history alone
- 03
Carry working rules across turns
Reintroduce the active project state and execution rules when a new agent session starts
- 04
React differently when failures repeat
Move from retrying commands to identifying the class of failure when the same path keeps breaking
- 05
Reuse skills and agent instructions
Keep useful operating knowledge available as inspectable assets instead of recreating it in every prompt
- 06
Require evidence before completion
Make “done” answer to unresolved tasks and observable proof, not confidence or presentation quality
A better run has a sequence
- 01Promptintent
- 02Specdefinition
- 03Ledgerstate
- 04Hooksgates
- 05Workexecution
- 06Verifyevidence
Inspect first. Install second
get-fable is open source and the hooks are plain files you can review before adding them to your environment
Read the full quick startgit clone https://github.com/imMamdouhaboammar/get-fable.git
cd get-fable
bun ./bin/get-fable.js status
bun ./bin/get-fable.js assets
bun ./bin/get-fable.js installStrong claims are easy. Inspectable claims are better
What is implemented
- Claude CodeSkill, project rules, and four lifecycle hooks
- Antigravity / Gemini configPlugin package, skill files, and rules under the documented config target
- Agent KernelRules copied when the existing Agent Kernel directory is present
- Request proxyOpenAI-style request enrichment with one configured upstream URL
What get-fable does not claim
- No model replacementIt does not reproduce a proprietary model or private service
- No universal installerIt does not automatically configure every IDE, provider, or coding agent
- No correctness guaranteeBetter process does not eliminate hallucinations or execution risk
- No hardened public gatewayThe current request proxy is a development utility and needs an external access-control boundary before untrusted exposure
Read the code. Read the hooks. Challenge the thesis
get-fable is an independent community project. References to model, company, and upstream project names are descriptive only and do not imply affiliation, endorsement, or sponsorship