Context Hunter

Discovery Workflow (Before Coding)

1) Assess Request Completeness

Ask: "What is likely missing?"

Examples:

• Similar endpoints include auth/validation. Is that expected here?
• This area uses soft-delete semantics. Should this operation follow that?
• Similar flows emit telemetry/error states. Should this change do the same?
• Existing module boundaries suggest a different placement. Is current request still correct?

2) Run Targeted Discovery

Prioritize these in order:

1. Find analogous implementations and copy their structure.
2. Trace data flow for similar features end-to-end.
3. Identify reusable utilities before creating new helpers.
4. Inspect nearby tests to infer team priorities and edge cases.
5. Read recent commits in the same area for current direction.

Portable discovery actions:

• Search for feature/domain terms in relevant directories.
• Enumerate nearby files in the affected area.
• Inspect recent change history for touched paths.
• Run targeted validation first, then broader project checks as needed.

3) Probe for Silent Knowledge

Look for implicit rules encoded in code:

• Soft-delete, audit, or historical retention patterns (for data-touching changes).
• Naming conventions (userId vs user_id) and file placement norms.
• Existing design system choices (for this repo: Nuxt + Vue + Tailwind 4.1 patterns).
• “Dead but dangerous” APIs/functions that exist but are no longer preferred.

4) Confidence-Based Stop Rule

Stop discovery when confidence is high enough to predict likely review feedback. If you cannot anticipate reviewer concerns yet, keep looking.

5) Produce Scaled Discovery Output

For L1, write a micro-brief:

• Closest analog and chosen pattern.
• Main risk or ambiguity.

For L2, write a full context brief:

• Analogous files reviewed (with paths).
• Patterns to follow (state/data/error handling/naming).
• Reusable utilities/components/composables identified.
• Risks and unknowns.

For L1/L2, keep an internal discovery log:

• Files checked.
• Patterns inferred.
• Decisions made from evidence.
• Naming evidence for new identifiers (new name -> analog paths -> extracted pattern).

Clarification Policy

• Prefer fewer questions.
• Ask only when the answer would change implementation approach.
• If convention is clear, proceed silently.
• Escalate only genuine ambiguity/conflict or product-level tradeoffs.

During Implementation

Changes should look native to the codebase:

• Reuse existing abstractions first.
• Match existing module boundaries and naming.
• Follow established error-handling and testing style.
• Prefer consistency over novelty.

Naming derivation rule:

• Do not invent names from general priors.
• For each new identifier family (file/function/variable/class/route), derive naming from closest local analogs.
• Use at least 2 analogous examples when available before finalizing a new naming pattern.
• If no analog exists, introduce the new term explicitly and record it as a no-analog exception.

If requirements conflict with discovered conventions:

• Flag the conflict explicitly.
• Propose 1-2 alternatives aligned with existing patterns.
• Ask for decision when tradeoffs are product/architecture-level.

Verification

After coding:

• Run targeted validation for changed area first.
• Run broader checks appropriate for risk (typecheck, lint/check, tests as needed).
• Confirm no new pattern drift was introduced.

Checklist

L0:

• Confirmed change is truly trivial and safe to execute without full discovery.

L1/L2:

• Classified complexity (L0/L1/L2) before discovery
• Studied analogous features in the codebase
• Checked for reusable utilities
• Reviewed test patterns for similar functionality
• Assessed request completeness before implementation
• Identified at least one silent convention/risk
• Produced the required artifact for the chosen level
• Kept an internal discovery log for L1/L2
• All new names were derived from codebase analogs, or marked as intentional no-analog exceptions
• Verified final approach matches existing patterns