Our team has adopted a custom internal framework for orchestrating data pipelines—it's essentially a declarative YAML-driven system that compiles down to a directed acyclic graph of Python tasks, with heavy use of decorators and a specific inheritance pattern for task classes. When I provide Claude Code with snippets from this framework, it often fails to grasp the meta-patterns, suggesting generic Python solutions that bypass our abstraction layer entirely. This leads to inefficient or incorrect refactoring suggestions, and I spend considerable time correcting its assumptions.
The core issue appears to be that Claude Code doesn't recognize our conventional structures as a coherent paradigm. For example, it misinterprets our custom decorators as unrelated functions and doesn't understand the contract between our base classes and the YAML configuration. I've attempted to provide extensive context in a single session, but the model seems to reset its understanding with each new code block.
I am seeking a systematic approach to "teach" Claude Code our framework's idioms. My hypothesis is that this requires more than just pasting code; it likely involves structuring the prompt in a specific pedagogical format. I am particularly interested in whether providing a condensed, annotated "framework primer" at the start of each session yields better results than incremental explanation.
To illustrate, here is a simplified but representative example of our task definition pattern:
```python
from framework.core import PipelineTask
class MyDataTask(PipelineTask):
"""Fetches and validates dataset X."""
# Framework expects this decorator to link config schema
@task_config(schema="data_fetch_v1")
def configure(self, params: dict):
self.source_url = params["source_url"]
self.retry_policy = params.get("retry_policy", "standard")
# The execute method signature is enforced by base class
def execute(self, context: TaskContext) -> dict:
data = self._fetch_from_api(self.source_url)
validated = self._apply_schema(data)
return {"output": validated}
def _fetch_from_api(self, url):
# Implementation details...
pass
```
When asked to "add error handling to this task," Claude Code might suggest a standard `try...except` block, which is correct in abstract Python terms, but it overlooks the framework's built-in error handling system that requires logging via `context.report_error()` and using the provided `self.retry_policy`. The suggestion is syntactically valid but violates our operational conventions.
Has anyone developed a reliable methodology for embedding such custom patterns into Claude Code's contextual understanding? I am evaluating several potential strategies:
- **Structured Primers**: Creating a reference document with key patterns, which is pasted at the beginning of each new conversation.
- **Example-Driven Prompting**: Providing multiple pairs of "before" (generic code) and "after" (framework-compliant code) transformations.
- **Constraint Explicit Prompting**: Appending specific instructions like "All suggestions must use the `TaskContext` API for logging and must respect the `@task_config` defaults."
I would appreciate insights on which techniques have proven most effective for integrating non-standard frameworks, especially those involving custom DSLs or meta-programming. Additionally, are there limitations in Claude Code's context window or architecture that make certain approaches untenable for complex pattern recognition?
Data is the source of truth.
Have you tried feeding it a minimal but complete example workflow in one go? Not just snippets, but a full YAML config paired with the exact Python task class it compiles to. I've had a bit more luck with Claude when I show the entire transformation, start to finish, even if it's a toy example.
It sounds like the decorators are key. Maybe you need to explicitly state their purpose in plain English right before the code, something like "This decorator links the YAML node name to the class." I find stating the contract upfront helps.
That's the brute force method. It works, but it shouldn't be necessary. If the tool can't infer the pattern from a few well-chosen examples, it's just pattern matching, not understanding.
And once you do get it to parse a full example, the next prompt you write will have to repeat the whole exercise. The context gets lost.
You're basically writing a manual every time. Which, sure, works. But it's a workaround for a system that's supposed to be smart.
CRM is a means, not an end.
Oh yeah, that specific failure mode with decorators and abstraction layers is so familiar. I hit similar walls trying to get Claude to "see" our internal monitoring patterns.
I've found you need to scaffold the prompt in a teaching order, almost like you're writing a tiny README before the code. Start by defining the architectural role of each component in plain English. "The @task decorator registers the class with the YAML parser." Then show the exact connection between the YAML key and the decorator argument. Finally, give it the smallest possible working example where it can see the whole loop. It's less about dumping code and more about narrating the framework's rules first.
Even then, you'll probably need to re-anchor it on every new chat. The context really does drop between prompts, which is the frustrating part. Have you considered creating a reference stub file you paste in first every time? A bit manual, but it saves repeating the explanation.
cost first, then scale
Exactly. The stub file is the only method with any consistency. I keep a single, heavily commented Python file that defines dummy decorators and a toy YAML spec. Paste it first, every new session. It's a tax, but less than rewriting the rules from scratch each time.
The real frustration is that this is basic framework comprehension. Any junior dev can infer these patterns after seeing them once. The fact that we have to brute-force context into a "smart" tool every single time is a product failure.
You're right that it's more than just pasting code, but I think the prompt structure is only half the battle. The real cost is context token burn.
Every time you paste that heavily commented stub file, you're eating into your effective working window. For complex frameworks, that tax can be significant, and you'll hit context limits faster.
What if you compress the "teaching" into a priced asset? One team I know created a tiny, purpose-built linter or type stub for their custom decorators. They feed Claude the *output* of that tool - the structured schema or the inferred types - instead of the framework source. It's a denser representation of the rules.
It's extra upfront work, but it turns framework patterns into a declarative spec the model seems to parse better than prose and code.
Stating the contract upfront does help, but it's a bit like paying a toll every time you want to drive on the road you already built. The fact that you have to re-explain the entire abstraction layer for each new session is a glaring inefficiency.
Your method works, but it shifts the cognitive load from the tool to the user, which kind of defeats the purpose. We're now spending time writing meticulous prompts instead of writing code. If I'm doing all that manual specification, I might as well just write the boilerplate myself.
—DW
You don't need a systematic approach. You need to stop using it for that.
If your framework is so custom that an LLM can't infer its patterns from a few examples, then the LLM is the wrong tool for that job. It's pattern matching on public code. Your internal abstractions are invisible.
You're trying to force a generic tool to be a specialist. That's always going to be a losing battle.
Just write the boilerplate. It's faster than teaching Claude your world every single time.
Simplicity is the ultimate sophistication
Your hypothesis is wrong. There is no systematic approach.
You're looking for a repeatable, scalable method to embed proprietary, undocumented domain logic into a generalist model that was trained on public repositories. That's not how this works. The model doesn't "learn" your framework. It temporarily aligns with a pattern in the current context window, which then evaporates.
You're describing a fundamental mismatch. You built a custom abstraction to save your team time. Now you're spending that saved time writing prompts to explain the abstraction to a tool that can't retain it. The cost-benefit has already turned negative.
The systematic approach is to stop using Claude for this specific task. Use it for the raw Python within your tasks, and handle the framework wiring yourself. Anything else is just adding a middleman that doesn't know the rules of your house.
Test the migration.