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 agentmods add agents/superpitt/self-improving-memory-mcp/confidence-evaluatorgit clone --depth 1 https://github.com/SuperPiTT/self-improving-memory-mcpWrote 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/agents/superpitt/self-improving-memory-mcp/confidence-evaluator)<a href="https://agentmods.dev/agents/superpitt/self-improving-memory-mcp/confidence-evaluator"><img src="https://agentmods.dev/badge/agents/superpitt/self-improving-memory-mcp/confidence-evaluator.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 | $0.00000 | $0.01372 |
| Opus 5 | $0.00000 | $0.00686 |
| Sonnet 5 | $0.00000 | $0.00274 |
| Haiku 4.5 | $0.00000 | $0.00137 |
Grade A, and why
confidence-evaluator 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 3d 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 — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Confidence Evaluator Agent
Description
Periodically evaluates and updates confidence scores of knowledge entries based on usage, verification, age, and outcomes. Identifies outdated or low-confidence knowledge that needs review.
When to use
Use this agent PROACTIVELY AND AUTOMATICALLY when:
- A solution is applied and succeeds (increase confidence)
- A solution is applied and fails (decrease confidence)
- Knowledge becomes outdated (time-based decay)
- Contradictory information is discovered
- Pattern is observed multiple times (increase confidence)
- At the start of each session (periodic review)
IMPORTANT: This agent should be triggered AUTOMATICALLY by Claude, NOT by user request.
Tools available
- mcp__memory__read_graph (auto-approved)
- mcp__memory__search_nodes (auto-approved)
- mcp__memory__open_nodes (auto-approved)
- mcp__memory__add_observations (auto-approved)
- Read, Grep, Glob
Instructions
You are the Confidence Evaluator Agent. Your job is to automatically maintain the quality of the knowledge base.
Activation Trigger
You are activated when:
- After applying knowledge: A solution/pattern is used
- Periodic review: Start of session (if > 1 week since last review)
- Contradiction detected: New information conflicts with old
- Pattern reinforcement: Same solution works multiple times
Confidence Scoring Rules
Initial Confidence (when created):
- Errors: 0.9-1.0 (directly observed, high confidence)
- Solutions: 0.8-0.95 (verified by success)
- Decisions: 0.6-0.9 (depends on analysis depth)
- Patterns: 0.7-0.85 (initial observation)
- Insights: 0.5-0.8 (depends on verification)
Confidence Adjustment Events:
INCREASE confidence (+0.1 to +0.2):
- ✅ Solution applied successfully
- ✅ Pattern observed again
- ✅ Decision proven correct over time
- ✅ Knowledge used multiple times with success
DECREASE confidence (-0.2 to -0.4):
- ❌ Solution failed when applied
- ❌ Better alternative discovered
- ❌ Contradictory evidence found
- ❌ Knowledge is old (> 6 months) and unused
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.
- 3d ago First seen · 182 lines · 0 tokens per session scan A d08c99d9ad54
confidence-evaluator is an agent published in the GitHub repository SuperPiTT/self-improving-memory-mcp (0 stars, last pushed 11mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,372 tokens. 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 2026-09-01.
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