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.
Adopted by
Most recently taken up by
- LuisSambrano/antigravity-configtheir own version, first seen Nov 8, 2026
Other versions1 project uses a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 1
- LuisSambrano/antigravity-config★ 8same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository30 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- llms.txtllms.txt
- app-devSkill
- architectureSkill
- buildSkill
- checkpointSkill
- debugSkill
- dreamSkill
- evalSkill
- experimentSkill
- forgeSkill
- frontendSkill
- governance-syncSkill
- handoffSkill
- helpSkill
- human-passSkill
- ingestSkill
- initSkill
- knowledge-graphSkill
- landing-pageSkill
- marketing-siteSkill
- metricsSkill
- orchestrationSkill
- qualitySkill
- retrospectiveSkill
- reviewSkill
- shipSkill
- simplifySkill
- tearitapartSkill
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
- Distinctive: line 24
Use native /context command to check usage.
- Distinctive: line 50
CHECK fill level via /context.
- Very specific: line 51
Write critical state: agentdb write-end '{"did":"X","next":"Y","blocked":"Z"}'
- Distinctive: line 52
Commit any uncommitted changes.
≈ “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.
Files
<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>
- COMPACTION: Summarize and reinitialize. Keep architecture decisions, discard noise.
- STRUCTURED NOTES: AgentDB + active.md persist across context resets.
- 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.
- MINIMAL READS: grep/glob to find, then read specific sections.
- 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-2025-06-27beta over hand-rolled truncation:clear_tool_uses_20250919clears old tool results past a token trigger (client keeps full history, no sync needed),clear_thinking_20251015clears old thinking blocks while preserving cache hits. Client-side SDK compaction (compaction_control, TypeScript/Ruby) is deprecated; use server-sidecompact_20260112for summarization, which takes its own beta headercompact-2026-01-12, notcontext-management-2025-06-27:triggerisinput_tokensonly (default 150000, minimum 50000), and custominstructionsreplace the default summarization prompt entirely, so restate the full framing (what to keep: goal, decisions, paths, errors) rather than appending.pause_after_compaction: truereturnsstop_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_20251015must precedeclear_tool_uses_20250919inedits. Setexclude_toolsfor results that must survive; clearing tool results invalidates the cached prefix, so setclear_at_leastso each clear pays for itself. Pair with thememory_20250818tool: Claude is warned as the threshold nears and saves state to memory files before results clear.clear_tool_inputs(defaultfalse) also drops call parameters.clear_thinking_20251015keepdefaults 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 fromcontext_management.applied_editsin the response (cleared_tool_uses/cleared_thinking_turnsandcleared_input_tokens; in streaming, the finalmessage_delta). Preview savings by passingcontext_managementtocount_tokens: it returnsoriginal_input_tokensbeside the post-clearinginput_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>
- CHECK fill level via /context. (gate: <60% → continue; ≥60% → proceed to step 2)
- Write critical state:
agentdb write-end '{"did":"X","next":"Y","blocked":"Z"}' - Commit any uncommitted changes. (gate: clean working tree)
- Generate handoff if complex work in progress:
/kernel:handoff - Trigger compaction.
- POST-COMPACT RESTORE:
a. Read active.md for project context.
b. Run
agentdb read-startfor 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>
- Agent tries too much at once → context exhausted mid-work Fix: Incremental progress with commits at each step.
- Later agent assumes done prematurely → work incomplete Fix: Explicit done-when criteria with evidence required. </failure_modes>
</skill>Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

