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 DongDuong2001/pudo-code-system --skill memorygit clone --depth 1 https://github.com/DongDuong2001/pudo-code-systemWrote 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/dongduong2001/pudo-code-system/memory)<a href="https://agentmods.dev/skills/dongduong2001/pudo-code-system/memory"><img src="https://agentmods.dev/badge/skills/dongduong2001/pudo-code-system/memory/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.
<a href="https://agentmods.dev/skills/dongduong2001/pudo-code-system/memory"><img src="https://agentmods.dev/badge/skills/dongduong2001/pudo-code-system/memory.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00016 | $0.00173 |
| Opus 5.5 | $0.00006 | $0.00069 |
| Sonnet 5.5 | $0.00003 | $0.00035 |
| Haiku 4.5 | $0.00002 | $0.00017 |
Grade A, and why
memory-debug 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 1mo 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.
What it actually says
Memory Debugging Skill
This skill is designed for identifying and resolving memory leaks and excessive memory footprint allocations.
When to use this skill
- When an application slowly consumes more RAM over time without releasing it (Memory Leak).
- When facing OutOfMemoryError (OOM) crashes.
- When optimizing Garbage Collection (GC) pauses causing latency.
Guidelines
- Heap Dumps: Export and analyze heap dumps or memory snapshots to find retained objects.
- Closures & Listeners: In GC languages, look out for un-unregistered event listeners or dangling closures capturing large scopes.
- Connections: Ensure database and network connections are gracefully closed.
- Streaming: Avoid loading large files or datasets entirely into memory. Use streaming APIs instead.
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.
- 1mo ago First seen · 20 lines · 16 tokens per session scan A 40000c496c5d
memory-debug is a skill published in the GitHub repository DongDuong2001/pudo-code-system (5 stars, last pushed 11d ago), licensed MIT. It adds 16 tokens to every session and 173 once invoked, about $0.0001 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.
Other skills, from other repositories
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diagnose
Structured 6-phase debugging. Build feedback loop first, reproduce deterministically, hypothesize with ranked falsifiable theories, instrument one variable at a time, fix with regression test, cleanup. Use when a bug exists, tests fail unexpectedly, or behavior is wrong and cause is unknown.
fix
Plan-aware auto-fix loop after coding. Runs tests, lint, format, and graph checks. Updates plan task status. Use after execute/tdd.
zoom-out
One-shot module map — go up a layer of abstraction and get a domain-vocabulary module map of the codebase. Use when lost in unfamiliar code, after a long deep-dive session, or when you need to re-orient before planning.
diagnose
Diagnostic pipeline for complex/intermittent bugs. Uses diagnostics roles for Investigation, Verification, and Solution before Lead Programmer handoff. Use ONLY for non-obvious failures (root cause unclear, reproduction unstable, fixes reverted). NOT for trivial bugs with known cause — fix them directly.
systematic-debugging
Investigates bugs, failing tests, build failures, performance issues, and unexpected behavior with root-cause discipline before any fix is proposed or implemented.