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/memory-discipline

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by Rohit Ghumarerohitg00/agentmemory29k stars
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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.

Use this Skill: https://skilld.dev/gh/rohitg00/agentmemory/memory-discipline

This session only. Nothing lands on disk.

SKILL.md

≈71 tokens always: the name and description. ≈650 when used: this file.

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.

Quick start

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" }

Why

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.

Workflow

  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.

What qualifies

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).

Anti-patterns

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.

Checklist

  • First tool call on a nontrivial task was a project-scoped search.
  • Every save carries the reason, not just the conclusion.
  • Corrections became lessons, not memories.
  • Nothing saved that the repo or hooks already record.

See also

  • recall, remember: the user-invoked forms of the read and write sides.
  • lesson: the correction loop this discipline hands off to.

Troubleshooting

See ../_shared/TROUBLESHOOTING.md if memory_smart_search or memory_save is not available.

Source: SKILL.md on GitHub

No alerts1mo3 checks · Risk SAFE
  • Gen Agent Trust Hub1mo

    The skill describes a workflow for managing an AI agent's memory, including searching for context at the start of a task and saving decisions during the process. It includes explicit advice to avoid saving secrets. A low-severity risk is identified regarding the attack surface for indirect prompt injection, as the skill directs the agent to ingest and act upon data retrieved from a persistent memory store.

  • Socket1mo

    No alerts

  • Snyk1mo

    Risk: LOW · No issues

Signed by skilld at a2a2af9. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 2 days ago.

Activeupdated 2 months ago
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