How do I install the Context Mgmt skill?

Run npx skillmds@latest add hashgraph-online-awesome-codex-plugins/context-mgmt in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Context Mgmt safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: reads secrets. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Context Mgmt?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Context Mgmt free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Context Mgmt?

hashgraph-online (@hashgraph-online-awesome-codex-plugins) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Context Mgmt by hashgraph-online-awesome-codex-plugins

Name: Context Mgmt
Rating: 4.2 (5 reviews)
Author: hashgraph-online

Context Mgmt

Context engineering and token management. Compaction strategies, progressive disclosure, structured note-taking via AgentDB. Triggers: tokens, compaction, memory, handoff, summarize, context window.

File contents

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

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.

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

---name: context-mgmtdescription: Context engineering and token management. Compaction strategies, progressive disclosure, structured note-taking via AgentDB. Triggers: tokens, compaction, memory, handoff, summarize, context window.---<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.mdArchitecture: _meta/research/context-graph-architecture.mdGraph 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 actingAny 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>

Frequently asked questions