What is supermemory-agent-learning?

A skill for giving AI agents a limited, validated memory of past workflow outcomes and lessons. It uses SuperMemory through MCP, REST, or a software library, while avoiding storage of full conversation transcripts.

Use it to capture failures and corrections, reflect on selected events, validate lessons, promote approved lessons, retrieve them later, and measure outcomes. MCP is a standard interface through which agents access external tools and data.

It helps agents reuse confirmed lessons without filling memory with entire chat histories. Validation and an approval queue reduce the risk of storing incorrect or duplicate lessons.

How YashvantHange/SuperMemory configures Claude Code

Reuse

Borrowing it

Nothing to install: this file belongs to YashvantHange/SuperMemory. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/YashvantHange/SuperMemory/main/.claude/skills/supermemory-agent-learning/SKILL.md

Clone the repo

git clone --depth 1 https://github.com/YashvantHange/SuperMemory

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/yashvanthange/supermemory/supermemory-agent-learning/github.svg)](https://agentmods.dev/skills/yashvanthange/supermemory/supermemory-agent-learning)

Your own site

<a href="https://agentmods.dev/skills/yashvanthange/supermemory/supermemory-agent-learning"><img src="https://agentmods.dev/badge/skills/yashvanthange/supermemory/supermemory-agent-learning/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/yashvanthange/supermemory/supermemory-agent-learning"><img src="https://agentmods.dev/badge/skills/yashvanthange/supermemory/supermemory-agent-learning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 77 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,283 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00077	$0.01283
Opus 5.5	$0.00031	$0.00513
Sonnet 5.5	$0.00015	$0.00257
Haiku 4.5	$0.00008	$0.00128

Measured 2mo ago against content hash 4c1dc260cca4, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

supermemory-agent-learning scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.claude/skills/supermemory-agent-learning/SKILL.md · 158 lines

How it starts

The opening of the file, as written. The whole thing — 158 lines — stays where its author put it; the contents beside it link to each section on GitHub.

SuperMemory — Agent Learning Layer

Core principle (never violate)

SuperMemory is not full observability. Capture workflow outcomes and distilled lessons only — never full conversation transcripts.

Capture	When
Workflow snapshot	Run start/end
Failure / correction / suggestion	Significant events only
Distilled lesson	After reflect(event_ids) → validate → process_promotions

Token budget at retrieval: default max_tokens=800. Store lessons, not histories.

Architecture (closed loop)

retrieve → record_failure → reflect(event_ids) → validate → process_promotions
         → retrieve again → report_outcome

Critical gates: Memory Validator (reject/merge/rewrite/approve) and Promotion Queue (no immediate store without validation).

Project layout

src/supermemory_mcp/   # FastMCP server (13 core + 16 learn.* tools, MCP resources)
packages/uall/         # UALL engine (validator, promotion, retrieval, experiments)
packages/uall_server/  # FastAPI REST
packages/uall_python/  # Python SDK
storage/adapters/      # file (.supermemory/), sqlite, postgres
skills/                # canonical agent skills (this file)
examples/              # MCP configs + agent demos
tests/                 # 69 tests incl. real stdio MCP transport

Entry point: packages/uall/service.py (UALLService).

When to use which interface

Context	Use
Claude / Cursor agents	MCP — `python -m supermemory_mcp.server`
Python agent in-process	`UALLClient` from `uall_python`
Remote / polyglot	REST — `python -m uall_server` (port 8000)
Tests	`python -m pytest tests/ -v`

Integration workflow (agent developer)

- [ ] 1. retrieve — policies + lessons before the step (stage-aware)
- [ ] 2. record_failure / record_correction — selective events only
- [ ] 3. reflect(event_ids) — evidence-first, never free-text-only
- [ ] 4. validate → process_promotions — explicit promotion step
- [ ] 5. retrieve again on next run
- [ ] 6. report_outcome — telemetry recalibrates confidence

Read the full file on GitHub · 158 lines

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

2mo ago First seen · 158 lines · 77 tokens per session scan A 4c1dc260cca4

Subscribe to this mod's changes

supermemory-agent-learning is a skill published in the GitHub repository YashvantHange/SuperMemory (2 stars, last pushed 4mo ago), licensed MIT. It adds 77 tokens to every session and 1,283 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Building agents Memory and context agent-memory claude cursor mcp model-context-protocol

supermemory-agent-learning next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
supermemory-agent-learningYashvantHange/SuperMemory	Skill	2	4mo ago	MIT	A	77	Claude Code
memoryPrismer-AI/PrismerCloud	Skill	1.4k	15d ago	MIT	A	122	Claude Code, Codex
memory-dreamPrismer-AI/PrismerCloud	Skill	1.4k	15d ago	MIT	A	133	Claude Code, Codex
conversation-compactionPrismer-AI/PrismerCloud	Skill	1.4k	15d ago	MIT	A	139	Claude Code, Codex
memory-importPrismer-AI/PrismerCloud	Skill	1.4k	15d ago	MIT	A	111	Claude Code, Codex
prismer-obsidianPrismer-AI/PrismerCloud	Skill	1.4k	15d ago	MIT	A	22	Claude Code, Codex
dashboardgrpcer/ownmem	Skill	425	23d ago	Apache-2.0	A	51	Claude Code

SuperMemory: Skill for Claude Code