agentleFS
ariaxhanariaxhan/kernel-claude/skills/context-mgmt/SKILL.md

context-mgmt

Skill15 starsChanged 4 months agoMITView on GitHub

Context engineering: compaction, progressive disclosure, structured notes via AgentDB. Triggers: tokens, compaction, memory, handoff, summarize, context window.

1 other project uses a version of this.

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The file

We split this skill into the rules it gives an agent

4 rules. About 0 are common, 3 are distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈3

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Use native /context command to check usage.

    line 24
  2. Distinctive:

    CHECK fill level via /context.

    line 50
  3. Very specific:

    Write critical state: agentdb write-end '{"did":"X","next":"Y","blocked":"Z"}'

    line 51
  4. Distinctive:

    Commit any uncommitted changes.

    line 52

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md
<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. Cap the return at ~1,000-2,000 tokens (conclusion + path:line evidence, no raw dumps); the exploration context stays in the subagent.
  4. MINIMAL READS: grep/glob to find, then read specific sections.
  5. TOOL RESULT CLEARING: Old tool output rarely needs to stay in context. When calling the Claude API directly (not via this harness), prefer the server-side context-management-2025-06-27 beta over hand-rolled truncation: clear_tool_uses_20250919 clears old tool results past a token trigger (client keeps full history, no sync needed), clear_thinking_20251015 clears old thinking blocks while preserving cache hits. Client-side SDK compaction (compaction_control, TypeScript/Ruby) is deprecated; use server-side compact_20260112 for summarization, which takes its own beta header compact-2026-01-12, not context-management-2025-06-27: trigger is input_tokens only (default 150000, minimum 50000), and custom instructions replace the default summarization prompt entirely, so restate the full framing (what to keep: goal, decisions, paths, errors) rather than appending. pause_after_compaction: true returns stop_reason: "compaction" with only the compaction block, so you can re-append recent messages or key instructions, then resend without the flag to continue. Ordering: clear_thinking_20251015 must precede clear_tool_uses_20250919 in edits. Set exclude_tools for results that must survive; clearing tool results invalidates the cached prefix, so set clear_at_least so each clear pays for itself. Pair with the memory_20250818 tool: Claude is warned as the threshold nears and saves state to memory files before results clear. clear_tool_inputs (default false) also drops call parameters. clear_thinking_20251015 keep defaults are model-specific (newer models keep all turns, older keep the last), so set it explicitly across model tiers. On Fable 5.1/Opus 5.5/Sonnet 5.5/Haiku 5.5, server-side edits never invalidate thinking blocks, but hand-editing earlier turns client-side can; for accounts created on/after 2026-08-31 a replayed invalidated block is rejected unless you opt into dropping it. Verify clearing fired from context_management.applied_edits in the response (cleared_tool_uses / cleared_thinking_turns and cleared_input_tokens; in streaming, the final message_delta). Preview savings by passing context_management to count_tokens: it returns original_input_tokens beside the post-clearing input_tokens. </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>

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