memory-informed-debug

memory-informed-debug is a skill for Claude Code from major7apps/pensyve. It costs 42 tokens per session (713 once invoked), scanned A, original, no licence file.

A debugging workflow that uses working memory to recall earlier root causes and known-good diagnostic procedures, then saves confirmed causes.

In plain words
What is it for?
Use it when debugging a non-trivial failure and when you want confirmed root causes recorded for later use.
Why use it?
It reduces repeated investigation when similar failures have already been understood.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the pensyve plugin — 3 skills, 7 commands, 2 agents, 5 hooks shipped together

Good fit Use it when debugging a non-trivial failure and when you want confirmed root causes recorded for later use.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/major7apps/pensyve/memory-informed-debug
Install

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.

Any agent
npx skills add major7apps/pensyve --skill memory-informed-debug
Clone the repo
git clone --depth 1 https://github.com/major7apps/pensyve

Made for: Claude Code.

Or install pensyve, the plugin that ships this one along with the rest of its 3 skills, 7 commands, 2 agents, 5 hooks.

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.

agentmods badge for memory-informed-debug

README.md
[![agentmods](https://agentmods.dev/badge/skills/major7apps/pensyve/memory-informed-debug/github.svg)](https://agentmods.dev/skills/major7apps/pensyve/memory-informed-debug)
Your own site
<a href="https://agentmods.dev/skills/major7apps/pensyve/memory-informed-debug"><img src="https://agentmods.dev/badge/skills/major7apps/pensyve/memory-informed-debug/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.

agentmods 80×15 button for memory-informed-debug

Your own site · 80×15
<a href="https://agentmods.dev/skills/major7apps/pensyve/memory-informed-debug"><img src="https://agentmods.dev/badge/skills/major7apps/pensyve/memory-informed-debug.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 42 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 713 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 unknown 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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00042 $0.00713
Opus 5.5 $0.00017 $0.00285
Sonnet 5.5 $0.00008 $0.00143
Haiku 4.5 $0.00004 $0.00071

Measured 1mo ago against content hash 5c452ef27596, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

memory-informed-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.

integrations/claude-code/skills/memory-informed-debug/SKILL.md · 56 lines

The source is not reproduced here

A licence we could not identify

The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.

Read it on GitHub

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.

  1. 1mo ago First seen · 56 lines · 42 tokens per session scan A 5c452ef27596

Subscribe to this mod's changes

memory-informed-debug is a skill published in the GitHub repository major7apps/pensyve (97 stars, last pushed 6d ago), with no licence file. It adds 42 tokens to every session and 713 once invoked, about $0.0002 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 2026-08-30.

Related

Other skills, from other repositories

autorag-doctor

Diagnose and repair a broken or half-working AutoRAG install so every configured source is both indexed and searchable. Checks AutoRAG, MinSync, Jikji, the embedding backend, and every CLI-backed datasource (lazykatok, discrawl, slacrawl, wacrawl, telecrawl, notcrawl, qmd, mailcrawl, rclone, Spotlight), then fixes…

Marker-Inc-Korea/AutoRAG · 152 tokens

rust-tracing

Add or change Rust diagnostic tracing in litellm-rust, including route spans, subscriber layers, and Python logger delivery.

BerriAI/litellm · 28 tokens

code-review

A code-review procedure for examining supplied code or a change diff for evidence-based bugs, compatibility problems, and data risks.

Shy2593666979/AgentChat · 41 tokens

ori-memory

Persistent agent memory with learning retrieval. Knowledge graph on markdown files — capture insights, decisions, research, and learnings during work, then retrieve them weeks or months later. Use when knowledge is too valuable to lose but too much to inject into every prompt.

aayoawoyemi/Ori-Mnemos · 54 tokens

lemmalog

Externalize working memory and logical state into the lemmalog Datalog engine (MCP). Use for ANY multi-step task where state should outlive one context window or span agents: long investigations, debugging sessions, audits, multi-agent searches, systematic explorations, planning with many interdependent constraints…

JordyZomer/lemmalog · 105 tokens

honcho-cli

Inspect and debug Honcho workspaces via the honcho CLI. Use when investigating peer representations, memory state, session context, or dialectic quality — any task that requires introspection of a Honcho deployment, including verifying that a recall/record memory loop is actually working.

plastic-labs/honcho · 61 tokens