Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add IgorGanapolsky/ThumbGate --skill agent-memorygit clone --depth 1 https://github.com/IgorGanapolsky/ThumbGateWrote 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.
[](https://agentmods.dev/skills/igorganapolsky/thumbgate/agent-memory)<a href="https://agentmods.dev/skills/igorganapolsky/thumbgate/agent-memory"><img src="https://agentmods.dev/badge/skills/igorganapolsky/thumbgate/agent-memory.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00162 | $0.00873 |
| Opus 5 | $0.00081 | $0.00436 |
| Sonnet 5 | $0.00032 | $0.00175 |
| Haiku 4.5 | $0.00016 | $0.00087 |
Grade A, and why
Agent Memory 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 7d 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.
How it starts
The opening of the file, as written. The whole thing — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Memory
Give your agent persistent memory across sessions. Before starting any task, recall what went wrong last time. After completing work, capture whether it succeeded or failed. Prevention rules are auto-generated from repeated mistakes.
Setup
Add the MCP server (one-time):
claude mcp add thumbgate -- npx -y thumbgate serve
No API key needed. All data stays local.
When to Use
- Starting a new task or session — recall past context first
- After completing work that succeeded or failed — capture feedback
- When the agent keeps making the same mistake — check prevention rules
Workflow
Step 1: Recall past context (do this FIRST on every task)
Call the recall MCP tool with a description of your current task. The tool returns:
- Past feedback relevant to this task (vector similarity search)
- Active prevention rules (auto-generated from repeated failures)
- Recent feedback summary with approval rate
Read the prevention rules carefully. These are patterns that failed before — follow them.
Step 2: Do your work
Complete the task as normal. Keep track of what you did and whether it worked.
Step 3: Capture feedback
Call the capture_feedback MCP tool:
If succeeded:
- signal:
up - context: What worked and why
- tags: Category labels
If failed:
- signal:
down - context: What you were trying to do
- whatWentWrong: Specific failure description
- whatToChange: How to avoid this next time
- tags: Category labels
Vague feedback like "it failed" will be rejected. Be specific.
Step 4: Check improvement (optional)
Call the feedback_stats MCP tool to see approval rate, top failure domains, and whether the agent is trending better or worse.
Available MCP Tools
| Tool | What it does |
|---|---|
recall |
Search past feedback and prevention rules for current task |
capture_feedback |
Record what worked or failed with structured context |
prevention_rules |
View auto-generated rules from repeated mistakes |
feedback_stats |
Approval rate, trend analysis, top failure domains |
feedback_summary |
Human-readable summary of recent signals |
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.
- 7d ago First seen · 98 lines · 162 tokens per session scan A e7edbb837f0a
Agent Memory is a skill published in the GitHub repository IgorGanapolsky/ThumbGate (26 stars, last pushed yesterday), licensed MIT. It adds 162 tokens to every session and 873 once invoked, about $0.0008 per session on Opus 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 2026-08-30.
Other skills, from other repositories
agentguard
Runtime guardrails for AI coding agents. Stop loops, budget overruns, retry storms, and timeouts before they burn money. Zero dependencies, local-first, MIT licensed.
agent-carnet
Use this skill when the user asks to save, recall, find, or organize notes. Triggers on: 'remember this', 'save this', 'note this', 'what did we discuss about...', 'check the notebook', 'find in carnet'. Also use proactively when discovering findings worth preserving across sessions.
grimoire
Use when the user says 'update context', 'update claude', 'save library', or after significant project changes.
mnemos
Task-scoped memory lifecycle — typed MnemoGraph prevents lossy context compaction by treating facts/decisions/code-refs/handoffs as distinct node types with per-type eviction policies.
keep-the-why
Extract and preserve the reasoning code cannot explain - decisions, rejected alternatives, workarounds, incidents, constraints - plus project setup and maintainer interviews. Not for what changed (see Keep a Changelog) - only why.
continuous-learning-v2
Instinct-based learning system that observes sessions via hooks, creates atomic instincts with confidence scoring, and evolves them into skills/commands/agents.