Debugging Claude Code's Build-Check Logic: Why Name Search Fails and Structural Footprint Search Fixes It

A developer on r/ClaudeAI reports that Claude Code repeatedly failed to detect existing features, claiming “is X built?” returned “no” four times in a single session — each time the feature already existed. The root cause: the agent searched by name (keywords, synonyms) instead of by structural footprint (routes, schemas, registered tools, scheduled jobs, documented decisions). Names drift; architectural artifacts don’t.

The pattern

Asking “is this feature already built?” triggered a confident “no, here’s how we’d build it,” even when the feature was partially implemented. Each time the user had to push back to extract the real answer. The developer diagnosed that the agent was searching, but using vocabulary-based queries that missed code with different naming conventions.

The rule (structural footprint search)

The synthesized rule forces the agent to search by shape, not name. For example, instead of “find feature X,” it asks “what plugin tools exist?” or “what routes, schemas, or registered jobs match this functionality?” This catches prior code that a name search never would have matched.

Key shift: “Searching by better synonyms is still searching by name. The footprint version catches it (the prior code registered a plugin tool, and ‘what plugin tools exist?’ is a high-signal narrow search).”

Requested feedback from the community

• Hallucination shapes structural footprint search would NOT catch
• Audit-theater patterns where the form is satisfied without substance
• Over-triggering on questions that aren’t actually absence claims
• Confidence amplification: post-audit, agent more confident in conclusions, making wrong-ontology errors harder to catch
• Wrong-ontology rigor: agent searches GraphQL patterns on a REST system, finds nothing, confirms absence

The developer is testing the rule in a separate project for 2–3 weeks before considering a global config. They invite others to share rules that solved “hallucination with rigor” (not just hallucination).