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session-guard

Helps agents manage long tasks by checking for forgotten instructions and drifting conventions. It guides progress summaries, rule checks after context compaction, and handoff notes when work should continue in a fresh session.

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Use when working on complex multi-step tasks, when a session is getting long (40+ tool calls), when the agent starts ignoring rules it followed earlier, when conventions drift, when output quality seems to degrade, or after any context compaction event. Prevents long-session corruption AND context compaction amnesia through behavioral self-enforcement.

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适用场景

  • Keeping rules consistent during long agent sessions
  • Rechecking decisions after context compaction
  • Preparing handoff notes for a fresh session
  • Splitting tasks when their scope keeps growing

包含的文件(1)

  • SKILL.md3.9 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Session Guard

Overview

Long sessions corrupt silently. Context compaction drops instructions unannounced. Hooks don't fix it (confirmed by multiple developers on GitHub issues #19471, #9796, #64171). This skill prevents both through behavioral self-enforcement: monitor health, anchor critical rules through compaction, split before damage occurs. No packages, no databases - pure behavioral enforcement that works in every harness.

When to Use

  • Session exceeds 40 tool calls
  • Agent contradicts earlier decisions
  • Style/naming conventions start drifting
  • After any context compaction event
  • Task scope growing unbounded

Health Signals

SignalThresholdAction
Tool call count>40YELLOW: checkpoint + recite critical rules
Tool call count>60RED: split or compact with anchor
Agent contradicts earlier decisionAnyVERIFY: re-read source of truth
Style/naming driftAnyRECITE: state the active rules aloud
File read returns unexpected contentAnyRE-READ: don't trust cached state
Task scope growing unboundedContinuousSPLIT: one task per session

Protocol

Green Zone (0-40 tool calls)

Normal operation. No intervention needed.

Yellow Zone (40-60 tool calls)

  1. CHECKPOINT - summarize progress in one paragraph
  2. RECITE - state the 3-5 most critical active rules aloud: "Active rules: [naming convention], [file structure], [error handling pattern], [testing requirement]"
  3. ASSESS - almost done? Push through. Not done? Prepare split.
  4. REDUCE - no exploratory reads. Only targeted operations.

Red Zone (60+ tool calls OR drift signal)

  1. STOP - do not make more tool calls
  2. VERIFY - re-read project rules (don't trust memory)
  3. CHECKPOINT - write state to handoff document
  4. SPLIT - create handoff, suggest fresh session

Context Anchoring (anti-compaction)

When compaction has occurred (sudden loss of earlier context, or after /compact):

  1. RE-READ the project's rules file immediately
  2. RECITE the 3-5 critical rules aloud in your response
  3. VERIFY your planned next action matches those rules before executing
  4. If uncertain about ANY prior decision, RE-READ the source file - don't guess

What survives compaction:

  • Most recent user messages (high priority)
  • Currently-invoked skill body (capped at 5K tokens, oldest dropped first)
  • Git status and project structure
  • File contents read AFTER compaction

What gets LOST in compaction:

  • Decisions made early in conversation
  • Architectural rules stated only verbally (not in files)
  • Context from tool outputs (file reads, command outputs)

Compaction-Safe Pattern

Keep critical instructions in FILES (CLAUDE.md, CONTEXT.md), NOT in conversation. If a rule matters, it must live in a file the agent can re-read - not in something agreed on earlier.

Common Mistakes

  • Trusting that you remember the rules after 50+ tool calls (you don't - re-read)
  • Re-reading EVERYTHING to be safe (wastes tool calls - be targeted)
  • Feeling fine therefore assuming context is fine (compaction is SILENT)
  • Splitting AFTER noticing problems (split BEFORE - prevention, not recovery)

Why This Matters

Context compaction is the #1 unsolved platform problem in 2026. Hooks don't fix it (confirmed: the agent ignores post-compaction injections because the compaction summary creates narrative momentum). This skill is the lightweight behavioral countermeasure: no infrastructure, no packages - disciplined self-monitoring that works in every harness.

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