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The most reliable way to prevent duplicate or unnecessary code is to make reuse a required decision, not an optional preference. Before an agent edits anything, have it map the relevant behavior, search for existing helpers, components, services, and tests, read the closest matches, and explain whether it can extend one of them. Permit a new implementation only when the agent can name the existing candidates it rejected and why.
That workflow works best when paired with current, project-specific instructions and a deliberate diff review. Written rules can steer an agent, but only inspection of the actual repository can reveal parallel abstractions, obsolete utilities, hidden conventions, and scope creep.
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What “duplicate or unnecessary code” looks like
Duplication is more than two functions with similar names. An agent can create a second implementation of the same behavior, introduce a competing abstraction, copy a test fixture, or add a dependency and wrapper when the repository already has a suitable path. “Unnecessary” also includes unrelated cleanup, speculative flexibility, generated project notes promoted without review, and new files that are not required for the requested behavior.
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These changes often pass a local test because the new code is internally consistent. The cost appears later: two bug fixes are needed, conventions diverge, ownership becomes unclear, and future agents find even more choices to search. No instruction can guarantee zero duplication; the practical goal is to make reuse the default and make exceptions visible.
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The six-part workflow
Use the following sequence for feature work, bug fixes, refactors, and test changes. It separates discovery from editing so the agent cannot silently replace an existing path with a new one.
1. Ask for inspection before implementation
Start the task by requiring an inventory of the relevant code. The agent should identify where the requested behavior is implemented, where it is called, and which tests describe it. A useful first prompt is:
Before changing files:
1. Identify the current behavior and trace its call path.
2. Search the repository for helpers, components, services, and tests that already provide it.
3. Read the most relevant matches and summarize their constraints.
4. Propose the smallest change that reuses an existing implementation.
Do not edit until this inspection is complete.This is an investigation request, not a demand for a long report. Ask for file names, symbols, and a short explanation of how data moves through the existing path. If the agent cannot find a match, it should say what it searched and what evidence led it to that conclusion.
2. Search broadly, then read the closest matches
Repository search should cover names, behavior, and data shapes. Searching only for the wording in a ticket misses utilities that use different terminology.
- Search for the public behavior: route names, event names, API fields, UI labels, and error messages.
- Search for likely concepts:
parse,validate,serialize,normalize,retry, or the domain noun involved. - Search tests and fixtures as well as production code; a reference test often reveals the intended extension point.
- Search imports and call sites to learn which implementation is considered canonical.
- Inspect neighboring files for local naming, error handling, and dependency conventions.
For a repository that uses ripgrep, an initial pass might look like:
rg -n "behaviorName|domainTerm|error message" .
rg -n "(parse|validate|normalize|serialize)" src test tests
rg -n "from .*existingModule|import .*existingSymbol" src test testsAdjust directories and expressions to the project. The command is only a search aid; it is not proof that a symbol is reusable. Read the implementation, its callers, and nearby tests before deciding.
3. Make reuse an explicit decision
Tell the agent that an existing solution should be reused when it meets the requirement. Cursor’s documentation states the rule this way: “Only generate new code if no suitable existing solution is found.” Treat that as a decision gate:
- List the candidate implementations the search found.
- Compare each candidate with the requested behavior, inputs, outputs, error cases, and performance constraints.
- Select the candidate that fits the repository’s current conventions with the least additional surface area.
- Extend it or call it directly when that preserves behavior and compatibility.
- If none fits, explain why before creating a new implementation.
A short rationale is enough. For example: “The existing formatter handles the same input type and error policy, so I will add an option there rather than create a second formatter.” If a new abstraction is necessary, the explanation should identify the candidates checked and the specific mismatch—such as an incompatible lifecycle, security boundary, or contract—not merely say that a new file feels cleaner.
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4. Give the agent accurate repository context
Agents make fewer parallel-pattern decisions when the repository’s architecture and conventions are written down. Keep a concise project instruction file in the location and format supported by the editor or agent you use. Include information that cannot be inferred reliably from a quick search:
- the architectural boundaries and ownership of major directories;
- the canonical modules for shared utilities, API clients, state, and UI components;
- commands for formatting, linting, type checking, unit tests, integration tests, and builds;
- naming, error-handling, dependency, and test conventions;
- which generated files are edited indirectly and which files must not be changed by hand;
- the definition of a complete change, including required tests and documentation.
Keep these instructions focused on project-specific knowledge. Visual Studio Code’s guidance recommends documenting architecture, important directories, commands, and conventions, while removing stale or duplicate rules. An instruction that says “always create a new service” can conflict with a repository whose existing service is the intended extension point. Review the file when the architecture changes.
5. Trace behavior before editing and review notes before sharing them
Ask the agent to explain the current flow from entry point to side effect before it changes code. This catches hidden reuse opportunities such as a shared adapter called several layers below the file named in the ticket. It also exposes whether the requested behavior is already present but incorrectly wired.
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6. Extend tests in the existing style
For test work, instruct the agent to inspect both the implementation and neighboring reference tests. Anthropic’s Claude Code use-case guidance specifically describes reading implementation and reference tests and matching established test conventions. That prevents a new fixture builder, assertion helper, or setup pattern from duplicating one already used nearby.
Before writing tests:
- Find the tests for the current behavior and two nearby examples with similar inputs.
- Reuse their fixtures, factories, setup, assertions, and naming style where applicable.
- Add only the cases needed to prove this change.
- Run the narrow test target first, then the repository's required checks.Do not ask for broad “test improvements” in the same task unless they are required for the requested behavior. A large cleanup makes it harder to tell whether a new fixture or helper is necessary.
A standing instruction versus a task-specific prompt
Both mechanisms are useful, but they solve different problems. A standing instruction establishes the repository’s default behavior; a task prompt supplies the local scope and acceptance criteria.
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| Configuration | Persistence | Specificity | Maintenance cost | Best use |
|---|---|---|---|---|
| Repository instruction | Applies across tasks and contributors | Project-wide architecture, commands, and conventions | Must be reviewed as the codebase changes; stale rules can mislead every task | Canonical module locations, required checks, reuse policy, and scope boundaries |
| Task-specific prompt | Applies only to the current request | Exact behavior, files or areas in scope, and acceptance tests | Low persistence; must be supplied again for a later task | A migration constraint, a compatibility requirement, or a one-off reason not to touch a subsystem |
Use both when appropriate: keep durable facts in the repository instructions and put the ticket’s constraints in the task prompt. Do not copy the same rule into several instruction files. Duplicated guidance can drift and create contradictory priorities.
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A practical prompt that puts the workflow into effect
Adapt this template to the actual repository rather than inserting invented paths or module names:
Implement [requested behavior] within the current repository.
Scope:
- Change only what is required for [acceptance criteria].
- Do not perform unrelated cleanup or introduce speculative abstractions.
Before editing:
1. Trace the existing behavior from entry point to side effect.
2. Search production code, tests, fixtures, and configuration for an existing implementation.
3. Read the closest matches and summarize their contracts and conventions.
4. Choose reuse or extension when a suitable implementation exists.
5. If you propose new code, list the candidates you rejected and explain the incompatibility.
While editing:
- Follow the repository's established naming, error handling, dependency, and test patterns.
- Reuse neighboring fixtures and helpers where they fit.
- Keep the diff limited to the requested behavior.
Before finishing:
- Show the files changed and why each is necessary.
- Check for a second helper, component, fixture, abstraction, or dependency that duplicates existing work.
- Run the required formatter, linter, type checks, and tests.The template creates checkpoints without dictating an architecture the agent has not observed. Replace the bracketed description with the actual requirement and let the repository determine names and paths.
When should you reuse code, and when is new code justified?
Reuse is not automatically correct. An old helper may have the wrong contract, leak an abstraction across a boundary, or carry compatibility behavior that the new feature must not inherit. Decide using three questions:
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| Behavioral fit | The existing implementation handles the required inputs, outputs, errors, and edge cases, or can be extended without changing unrelated callers. | Its contract cannot satisfy the requirement without breaking callers or adding confusing exceptions. |
| Convention fit | It belongs to the established architectural layer and follows the repository’s current patterns. | The requested behavior has a genuinely different ownership, lifecycle, security boundary, or integration contract. |
| Maintenance impact | One implementation gives future fixes one place to land and keeps tests and documentation together. | Combining the behaviors would create a leaky, overloaded abstraction that is harder to test and understand than two clearly separated units. |
When creating new code, record the decision near the change or in the task summary. The purpose is not bureaucracy; it gives reviewers and future agents a searchable explanation that the existing options were considered.
Review the diff for duplication and scope
Inspection before editing reduces risk, but the final diff is the point at which you can verify what actually happened. Review the change as a set of repository decisions, not just as a collection of passing tests.
- Second implementation: Does the diff add a helper, component, service, parser, validator, or adapter that resembles an existing one?
- Parallel abstraction: Is a new wrapper or interface hiding a capability that the repository already exposes directly?
- Duplicate fixture: Did the test add a factory, builder, mock, or setup block that could use a neighboring reference?
- Unrelated cleanup: Are formatting changes, renames, dependency upgrades, or refactors outside the request mixed into the patch?
- Unneeded files: Does every new file support an acceptance criterion or a required test?
- Convention drift: Do names, error handling, imports, and test assertions match nearby code?
- Instruction drift: Did the change create a new rule that duplicates or contradicts an existing project instruction?
Ask the agent for a file-by-file explanation, then verify it yourself. A concise review prompt is:
Review the current diff only for duplication and scope creep.
For every new symbol and file, identify the acceptance criterion it serves.
Search for existing equivalents and report any overlap.
Flag unrelated cleanup, parallel abstractions, duplicate fixtures, and stale instruction updates.
Do not modify the diff during this review.Separating review from implementation matters. If the agent is allowed to “clean up” while explaining the diff, it can conceal an unnecessary change by expanding the patch again.
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Symptom: The first response is a new file or function with no repository map.
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Correction: Stop the task and request the inspection phase. Require search results, relevant files read, and a reuse decision before permitting edits. If the agent cannot explain the current call path, it is not ready to choose an extension point.
The agent searches only the ticket wording
Symptom: It reports “no existing helper” after one exact-name search.
Correction: Ask for searches by behavior, domain terms, data fields, error messages, imports, and test patterns. Then have it read the closest matches rather than treating search output as a verdict.
The repository instructions are generic or stale
Symptom: Rules mention directories that no longer exist, repeat one another, or prescribe a pattern the codebase does not use.
Correction: Replace generic slogans with current architecture, commands, conventions, and completion requirements. Remove obsolete and duplicate rules. Keep one authoritative statement for each policy.
The agent creates a “better” abstraction
Symptom: A broad interface, wrapper, or utility appears even though a small extension would satisfy the request.
Correction: Re-state the scope and ask for the smallest compatible change. Require a comparison with existing candidates and a concrete maintenance reason before accepting a new abstraction.
Tests pass, but the patch duplicates behavior
Symptom: New tests validate the new path while the old path remains, creating two sources of truth.
Correction: Review implementation and neighboring tests together. Ask whether the existing tests should be extended instead, and search for callers that still use the older implementation. Passing tests demonstrate the tested behavior, not architectural necessity.
Generated project notes become inaccurate authority
Symptom: Later tasks follow a generated map containing guessed ownership or obsolete paths.
Correction: Treat notes as drafts. Verify each claimed entry point against the repository, remove uncertainty, and publish only information someone has reviewed and agreed to maintain.
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Inspection does not require a design document for every one-line fix. Scale the evidence to the risk:
- For a tiny change in a well-known file, ask for a brief search result, the chosen existing symbol, and the focused test.
- For a cross-cutting feature, require a call-path summary, candidate comparison, affected tests, and a file-by-file plan.
- For a new subsystem, document ownership, boundaries, and why existing modules cannot meet the contract before implementation begins.
- For test-only work, spend most of the inspection time on neighboring fixtures and assertion conventions.
The objective is not maximum ceremony. It is to make the expensive decision—adding another implementation—deliberate and reviewable.
What the official guidance does and does not establish
Cursor’s reuse guidance, Visual Studio Code’s codebase-configuration and exploration guidance, and Anthropic’s Claude Code examples all support search-first investigation, project-specific context, review of generated knowledge, and matching existing test conventions. They are process and product documentation, not controlled evaluations of duplication rates.
No cited source establishes a percentage for how often coding agents duplicate code or proves that these practices eliminate duplication. Measure your own repository outcomes if you need that evidence: track repeated symbols found in review, unnecessary files removed, and follow-up fixes required in parallel implementations. Do not present those internal observations as universal statistics.
Tooling considerations
An editor or coding agent that can search the repository, read multiple files, accept project instructions, and present a reviewable diff can support this workflow. Cursor documentation describes agent and review capabilities, while Visual Studio Code documents repository-specific AI configuration and codebase exploration. The important capability is not a brand name or a particular model; it is the ability to inspect actual code, retain accurate context, and let a human verify the resulting diff.
Whichever tool you use, keep the repository as the source of truth. An agent’s summary, plan, or memory is useful only while it agrees with the files and tests that are actually present.
A compact checklist for every agent-assisted change
- State the requested behavior and the strict scope.
- Trace the current behavior before editing.
- Search by symbols, behavior, data, errors, imports, tests, and fixtures.
- Read the closest implementation and reference tests.
- Choose an existing implementation or extension point when it fits.
- If adding code, list rejected candidates and the concrete incompatibility for each.
- Follow current repository instructions and remove any stale or duplicate rule encountered.
- Reuse established fixtures, helpers, naming, and assertions.
- Run the focused checks, then the repository’s required checks.
- Review the final diff for duplicate behavior, parallel abstractions, unnecessary files, and unrelated cleanup.
This checklist turns “please avoid duplication” into observable decisions an agent and reviewer can verify.
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