LLM Tools for Coding Automation

Model tool inputs and outputs with Python type hints and dataclasses.

Build a command-line interface for an automation script with argparse.

Represent token data as vectors and identify the shapes of tensors used in a simple language model.

Calculate dot products and cosine similarity for small vectors.

Apply a matrix transformation to a vector and interpret the resulting dimensions.

Convert a short list of scores into probabilities using softmax.

Calculate cross-entropy loss for a toy next-token prediction.

Use derivatives to interpret the direction and size of a parameter update.

Trace gradient flow through a simple computation graph.

Tokenize sample code and prose and compare how tokenization affects sequence length.

Explain how learned token and positional representations encode a model's input.

Calculate scaled dot-product attention for a small set of query, key, and value vectors.

Apply a causal attention mask to prevent access to future tokens.

Explain how multiple attention heads can capture different token relationships.

Trace information through normalization, residual connections, attention, and the MLP in a decoder block.

Trace the conversion of a decoder output into next-token probabilities.

Explain how autoregressive generation uses the context window and key-value cache.

Explain how training examples are constructed for next-token pretraining.

Calculate the loss for a short autoregressive training sequence.

Describe how mini-batch gradient descent updates model parameters.

Distinguish pretraining, continued pretraining, and supervised instruction tuning.

Explain how preference optimization changes model behavior after instruction tuning.

Compare full fine-tuning, parameter-efficient fine-tuning, and prompt-based adaptation.

Explain how quantization changes model memory use and inference quality.

Diagnose common hallucinations and reasoning failures from the model's training objective and available context.

Construct system, user, and assistant messages for a chat-completion request.

Write a prompt that separates instructions, supplied context, examples, and requested output.

Select temperature and related decoding controls for deterministic or exploratory coding tasks.

Request structured output and validate it against a typed schema.

Implement a tool-calling loop that validates arguments and returns tool results to the model.

Control context growth through selection, truncation, and summarization.

Handle API timeouts, rate limits, retries, and duplicate requests safely.

Compare candidate models using task quality, latency, context capacity, and cost.

Define an acceptance rubric for an LLM-assisted coding task.

Build a representative evaluation set from successful, ambiguous, and failure cases.

Implement deterministic checks for schema validity, citations, commands, and generated patches.

Calculate precision, recall, and F1 for an issue-classification result.

Use model-based evaluation while controlling for evaluator bias and nondeterminism.

Identify prompt-injection and data-exfiltration paths in a tool-enabled workflow.

Enforce least-privilege tool access and human approval for consequential actions.

Record traces, errors, token usage, latency, and cost for an LLM workflow.

Choose between RAG, long-context prompting, and model adaptation for a given knowledge task.

Design chunk boundaries and overlap for technical documents.

Create and query an embedding-based vector index.

Apply metadata filters to constrain retrieval by repository, language, path, or version.

Combine lexical and vector search for hybrid retrieval.

Rerank retrieved passages for relevance to a query.

Generate an answer that is grounded in retrieved evidence and includes source citations.

Measure retrieval recall using a labeled set of questions and relevant passages.

Inventory the source, build, test, documentation, and configuration artifacts in a C++, C, or Python repository.

Extract symbols and structural boundaries from C, C++, and Python source files.

Create code-aware chunks that preserve symbol identity and file location.

Construct a lightweight map of imports, includes, definitions, and references.

Index source code, documentation, and build metadata with consistent provenance.

Answer codebase architecture questions using retrieved evidence and file-level citations.

Refresh only affected index entries after a repository change.

Evaluate code retrieval with symbol, behavior, dependency, and architecture questions.

Explain the roles of MCP hosts, clients, servers, tools, resources, and prompts.

Inspect and test the capabilities exposed by an existing MCP server.

Define unambiguous JSON schemas for MCP tool inputs and outputs.

Expose a local code-search or build-analysis command as an MCP tool in Python.

Expose repository documentation or metadata as an MCP resource.

Connect an MCP server to a compatible coding host and verify a complete request lifecycle.

Restrict MCP filesystem, process, network, and credential access using explicit boundaries.

Diagnose MCP discovery, transport, validation, and tool-execution failures.

Prototype an evidence-backed workflow for orienting a developer in an unfamiliar repository.

Prototype a workflow that proposes focused GoogleTest or pytest unit tests from source and existing test patterns.

Prototype a workflow that drafts API or module documentation from code and verified repository evidence.

Prototype a workflow that reviews a patch against repository conventions and reports prioritized findings.

Prototype a workflow that classifies and enriches incoming issues with labels, evidence, and follow-up questions.

Prototype a workflow that extracts root-cause candidates and next actions from compiler, linker, test, and CI logs.

Add human verification checkpoints before generated tests, documentation, reviews, or triage decisions are accepted.

Choose between a fixed pipeline and an agentic loop for a coding-automation task.

Represent a long-running automation job as explicit states and transitions.

Route subtasks to retrieval, repository, build, test, and issue-management tools.

Implement an agent loop with bounded iterations and explicit stopping conditions.

Recover safely from malformed outputs, tool failures, and interrupted execution.

Require human approval before write operations or externally visible actions.

Execute independent analysis steps concurrently while preserving deterministic aggregation.

Persist only the task state and evidence needed to resume or audit a workflow.

Define users, scope, acceptance criteria, and safety boundaries for a repository triage assistant.

Design the assistant's components and data flow across LLM APIs, RAG, MCP tools, and human approvals.

Implement ingestion for repository metadata, issue text, and build or CI logs.

Implement cited retrieval over C, C++, and Python codebase evidence.

Integrate read-only repository and build-diagnostic capabilities through MCP tools.

Implement a triage pipeline that produces classification, evidence, likely causes, and recommended next actions.

Build an evaluation suite covering normal cases, ambiguous inputs, prompt injection, and tool failures.

Package the assistant with configuration, audit logging, usage documentation, and a reproducible launch command.