agentmemory-agents
How agentmemory wires into host coding agents via the connect command. Use when installing agentmemory into a specific agent, when asked which agents are…
The session loop that makes agentmemory pay off, recall before starting work, save at decision points, learn from corrections. Use when starting a nontrivial task, after settling a decision or debugging a gotcha, or whenever deciding if something belongs in memory.
$ npx -y skills add rohitg00/agentmemory --skill memory-discipline --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/memory-disciplineContext preview
The summary Claude sees to decide when to auto-load this skill.
The session loop that makes agentmemory pay off, recall before starting work, save at decision points, learn from corrections. Use when starting a nontrivial task, after settling a decision or debugging a gotcha, or whenever deciding if something belongs in memory.
name: memory-discipline description: The session loop that makes agentmemory pay off, recall before starting work, save at decision points, learn from corrections. Use when starting a nontrivial task, after settling a decision or debugging a gotcha, or whenever deciding if something belongs in memory. user-invocable: false
Memory only pays off when reads happen before the work and writes happen at decision points. This loop is the skill; every tool call in it is mechanical.
memory_smart_search { "query": "auth refresh flow", "project": "myrepo", "limit": 5 }at task start, then at each settled decision:
memory_save { "content": "Chose cursor pagination over offset; offset scans broke past 100k rows in db/list.ts.", "concepts": "cursor-pagination, offset-scan-limit", "files": "src/db/list.ts" }Hooks capture what happened automatically. What they cannot capture is judgment: which fact mattered, which decision was settled, which correction should change future behavior. That judgment applied at the right moments is this discipline.
1. Task start, before reading code for any nontrivial task: `memory_smart_search` with the task topic and the project name. Spend the first tool call here; a hit saves rediscovery, a miss costs one call. 2. Mid-task, the moment a decision settles or a gotcha resolves: `memory_save` with the decision AND the reason, 2-5 specific concepts, real file paths. Save at the moment of resolution; end-of-session batch saves lose the reasons. 3. On user correction of your approach: save a lesson instead of a memory (the `lesson` skill). Lessons carry confidence and resurface before similar work; memories carry facts. 4. Before repeating a task type you have been corrected on: `memory_lesson_recall` with the task type as query. 5. Session end: stop. Hooks summarize and consolidate; a manual recap save duplicates them.
Save: settled decisions with reasons, non-obvious constraints discovered by debugging, environment facts not derivable from the repo. Skip: anything readable from the code, transient state, secrets, and step-by-step narration (hooks already captured it).
WRONG: finish implementing, then search memory to double-check, and batch-save a summary of everything done.
RIGHT: search first, save each decision as it settles, let hooks own the summary.
See ../_shared/TROUBLESHOOTING.md if `memory_smart_search` or `memory_save` is not available.
#1 Persistent memory for AI coding agents based on real-world benchmarks
Repo: rohitg00/agentmemory
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