memory-discipline
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.
Works with
---
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.
license: Apache-2.0
---
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
```json
memory_smart_search { "query": "auth refresh flow", "project": "myrepo", "limit": 5 }
```
at task start, then at each settled decision:
```json
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.More Debugging skills
diagnosing-bugs
mattpocock/skills
Diagnosis loop for hard bugs and performance regressions. Use when the user says "diagnose"/"debug this", or reports something broken/throwing/failing/slow.
explore-code
lllllllama/rigorpilot-skills
Rigor Improve implementation leaf skill for auditable candidate implementation in deep learning research repositories. Use when the researcher explicitly authorizes exploratory work on an isolated branch or worktree to transplant modules, adapt a backbone, add LoRA or adapter layers, replace a head, or stitch together meaningful low-risk migration ideas with rollback-aware records in `explore_outputs/`. Do not use for end-to-end exploration orchestration on top of `current_research`, trusted baseline reproduction, conservative debugging, environment setup, verified contribution claims, or default repository analysis.
safe-debug
lllllllama/rigorpilot-skills
Rigor Debug / Rigor Audit skill for deep learning research work. Use when the user pastes a traceback, terminal error, CUDA OOM, checkpoint load failure, shape mismatch, NaN loss symptom, or training failure and wants conservative diagnosis before any patching, with debug fixes clearly separated from research contributions. Do not use for broad refactoring, speculative adaptation, automatic exploratory patching, or general repository familiarization.

