Context mgmt

<skill id="context-mgmt"> <purpose> Context is finite. Every token competes for attention. Longer context makes things WORSE (30%+ accuracy drop for middle info). Progressive disclosure: load what's needed when it's needed.

Reasoning fidelity is the real metric, not token count. Quality degrades at ~60-70% fill, not at the limit. </purpose>

<prerequisite> Monitor context usage. Compact or hand off proactively at ~60% capacity — not 80%, not at limit. Use native /context command to check usage. This skill is for methodology. </prerequisite> <reference> Skill-specific: skills/context-mgmt/reference/context-mgmt-research.md Architecture: _meta/research/context-graph-architecture.md Graph telemetry (shadow): JSON receipts → `agentdb graph-project` / `graph-suggest` (see orchestration/agentdb/graph-project.py). Canonical JSON manifests remain authoritative. </reference>

<core_principles>

1. COMPACTION: Summarize and reinitialize. Keep architecture decisions, discard noise.
2. STRUCTURED NOTES: AgentDB + active.md persist across context resets.
3. MULTI-AGENT: Delegate research to subagents. They explore, report summaries.
4. MINIMAL READS: grep/glob to find, then read specific sections.
5. TOOL RESULT CLEARING: Old tool output rarely needs to stay in context. </core_principles>

<token_budget>

• CLAUDE.md: <150 lines (always loaded)
• rules/: <100 lines (always loaded)
• Skills: metadata only at startup, full content on demand
• Commands: only when invoked
• Reference docs: only when skill explicitly reads them </token_budget>

<compaction_protocol>

1. CHECK fill level via /context. (gate: <60% → continue; ≥60% → proceed to step 2)
2. Write critical state: agentdb write-end '{"did":"X","next":"Y","blocked":"Z"}'
3. Commit any uncommitted changes. (gate: clean working tree)
4. Generate handoff if complex work in progress: /kernel:handoff
5. Trigger compaction.
6. POST-COMPACT RESTORE: a. Read active.md for project context. b. Run agentdb read-start for failures, patterns, contracts. c. Check for pending checkpoints to review. </compaction_protocol>

<fidelity_signals> Watch for reasoning degradation BEFORE the token meter shows a problem:

• Hypothesis depth dropping: agent defaults to first idea, stops exploring alternatives
• Backtracking absent: errors patched in place instead of root-caused
• Step count contracting: multi-step plans collapse to one-shot attempts
• Cross-file awareness fading: agent forgets earlier files in the same session
• Inline checks disappearing: agent stops verifying assumptions before acting

Any one of these → compact or hand off NOW, even if /context shows <60% fill. </fidelity_signals>

<what_to_preserve> Never compact away:

• Current task context and goal
• Active decisions and their rationale
• Blocking issues and error states
• File paths currently being worked on
• Uncommitted changes description
• Contract IDs and branch names </what_to_preserve>

<what_to_compact> Aggressively compress:

• Exploratory searches → "searched X, found Y at path:line"
• File reads → "read file, key insight: Z"
• Successful operations → "completed: X"
• Debugging traces → "root cause: X, fix: Y"
• Tool call history → "N tool calls, outcome: X"
• Research context → offload to AgentDB, keep one-line summary </what_to_compact>

<failure_modes>

1. Agent tries too much at once → context exhausted mid-work Fix: Incremental progress with commits at each step.
2. Later agent assumes done prematurely → work incomplete Fix: Explicit done-when criteria with evidence required. </failure_modes>

</skill>