claude-code - 💡(How to fix) Fix Feature Request: Auto-triggered memory file loading after context compression

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• This is a single feature request (not multiple features)

Problem Statement

Claude Code auto-injects CLAUDE.md and MEMORY.md into every context window — unconditionally, without any model decision. The memory files linked from MEMORY.md are not injected. After context compression, Claude has lost the context that told it to re-read those files, so critical behavioral guidance silently disappears at exactly the moment it is most needed.

This cannot be fixed by model instructions alone. We added a rule to our CLAUDE.md as a workaround ("re-read memory files after context compression"). It still fails: the model loses the rule itself during compaction.

The harness already knows how to inject content unconditionally (CLAUDE.md, MEMORY.md) and already supports auto-triggered skills. Memory file loading uses neither mechanism — there is no architectural reason for this gap.

Concrete example: The DokuWiki authentication pattern was stored verbatim in reference_dokuwiki.md. After context compression, Claude attempted three broken authentication approaches before the user pointed out the answer was already in its own memory file.

Proposed Solution

Harness-level injection (Option A): When the harness injects a context compression summary, also inject the full contents of all memory files — or at minimum those flagged type: feedback and type: reference. Same mechanism as CLAUDE.md, no new concept required.

Alternative Solutions

Auto-triggered skill (Option B): A built-in skill memory-refresh that fires automatically when a context summary is detected. It reads each file listed in MEMORY.md and injects the contents. Users could configure which memory types trigger a full read vs. index-only.

This mirrors how skills already work ("do X automatically under condition Y") and gives users granular control over what gets re-injected.

Priority

High - Significant impact on productivity

Feature Category

Configuration and settings

Use Case Example

A project has MEMORY.md with 6 linked files: 2 feedback (behavioral rules), 2 reference (API/auth patterns), 1 user (expertise profile), 1 project (current goals). After a long session triggers context compression:

• Today: Claude proceeds without those files. Behavioral rules are gone. Auth patterns are gone. The user must re-explain or point Claude to its own memory — often after Claude has already made 2–3 wrong attempts.
• With this feature: The harness re-injects the memory files alongside the compression summary. Claude resumes with full behavioral context. No user intervention needed.

Additional Context

Additional Context

The harness already reads MEMORY.md — it injects the index on every turn. That means the harness already parses the memory directory and knows which files exist. Re-injecting their contents is one step further, not a new capability.

Memory file frontmatter is machine-readable. Each file already carries type: feedback | reference | user | project. The harness could selectively inject only high-priority types (e.g. feedback + reference) without blowing up the context window — keeping token cost proportional to need.

This disproportionately affects power users in long sessions. Short sessions never hit compaction; the memory system works fine there. The failure mode is exclusive to complex, multi-hour sessions — exactly the use case where reliable behavioral memory matters most. Users who invest in building a memory system are the ones hit hardest when it silently stops working.

Workaround attempted and confirmed insufficient: We added an explicit rule to CLAUDE.md: "re-read memory files when context compression is detected." This works in fresh sessions. After compaction the rule itself is gone from the model's context, so it cannot be followed. A harness-level guarantee is the only fix that survives compaction by construction.

This is not a Model Behavior Issue. Model behavior issues describe Claude making wrong decisions given available context. Here the context is structurally missing — the harness does not provide it. The fix belongs in the harness, not in a prompt.

Related issues:

• #34556 — asks for memory to survive compaction. Our files already survive (on disk); the gap is automatic re-injection, not persistence.
• #25999 — user built MemoryForge as a workaround, confirming the pain point is real but unaddressed at the infrastructure level.
• #34776 — governance/policy discussion, not about trigger mechanisms.
• #61042 — about semantic application of recalled facts, not whether files are loaded at all.

The distinguishing argument: the harness already has the mechanism (unconditional injection, auto-triggered skills). This request asks to use it for memory files.