botte-learn
Skill1 starsChanged 32 days agoMITView on GitHub
Guidance for upstream recorded-session analysis and proposed correction rules (scan, apply, status). Use to investigate recurring failure patterns; validate each proposed rule before applying it. Not for loop distillation (agent-intel), structural code detection or governed long-term memory (memory-hub).
In this repository114 other files it gives its agents.
- agent-cacheSkill
- agent-compressionSkill
- agent-intelSkill
- agent-overlapSkill
- agent-stateSkill
- ambient-hudSkill
- app-testSkill
- audit-dagSkill
- auto-distillSkill
- auto-memorySkill
- auto-routerSkill
- benchSkill
- bootstrapSkill
- botte-nnSkill
- botte-proxySkill
- botte-wrapSkill
- cacheSkill
- call-chainsSkill
- capabilitiesSkill
- cardinalSkill
- checkupSkill
- clarificationSkill
- clusterSkill
- code-complexitySkill
- code-duplicationSkill
- code-fingerprintSkill
- code-rulesSkill
- code-stateSkill
- code-wrappersSkill
- comfyuiSkill
- completion-proofSkill
- conductorSkill
- context-budgetSkill
- context-compressorSkill
- context-profilerSkill
- context-slicerSkill
- context-windowsSkill
- control-loopSkill
- cost-estimatorSkill
- dag-optimizerSkill
- dashboardSkill
- decision-ladderSkill
- demoSkill
- diff-languageSkill
- directives-auditSkill
- docgenSkill
- docs-stewardSkill
- dynamic-workflowsSkill
- efficiencySkill
- eventsSkill
- fallow-likeSkill
- fallowSkill
- fast-contextSkill
- fixSkill
- fleetSkill
- hailo-visionSkill
- harness-deltaSkill
- harvestSkill
- hermes-bridgeSkill
- hermes-second-brainSkill
- The first 60 shown.
The file
Files
botte-learn — from repeated failures to correction rules
Package scope and prerequisites
This catalog ships instructions, not the executable runtime. Python modules, scripts, local services and optional tools named below must be installed separately from the upstream project. Check their availability before running commands; if they are missing, explain the prerequisite instead of claiming execution or results. The host agent may use model tokens while following these instructions.
Commands, APIs and runtime behavior described below are conditional examples for a compatible upstream installation; they are not evidence of execution in this package.
Purpose
Scans session logs, extracts observed failure patterns, and proposes learned rules for review and possible application. A proposed rule is not proof that the recurring error is fixed; validate its effect against observed cases.
python -m skills.botte_learn.cli scan
python -m skills.botte_learn.cli apply
python -m skills.botte_learn.cli statusWhen NOT to use
- You want the loop distilled rather than the failures corrected — use agent_intel.
- The smell is in the code structure, not in a session — use the code_* detectors.
- You want durable, revocable memory — use memory_hub.
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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.

