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Agent skill

Ijfw Memory Audit

by FerroxLabs in FerroxLabs/ijfw

Audit and clean project memory files. An agent skill from FerroxLabs/ijfw.

MITAuto-check passedAgent Workflows

Install Ijfw Memory Audit

skills CLI
$ npx skills add FerroxLabs/ijfw --skill ijfw-memory-audit -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install FerroxLabs/ijfw ijfw-memory-audit --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/FerroxLabs/ijfw.git skills-src && mkdir -p .claude/skills && cp -r skills-src/codex/skills/ijfw-memory-audit .claude/skills/ijfw-memory-audit && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
ijfw-memory-audit
GitHub stars
212
Token cost
~382 tokens
SKILL.md length
119 words
Files
1
Skills in repo
38
Repo updated
First seen
Licence
MIT

At a glance

Audit and clean project memory files. An agent skill from FerroxLabs/ijfw.

  • Works in 7 steps: Call ijfw_metrics to get counts and… → Scan .ijfw/memory/ for all .md files.… → Categorize entries → …
  • Tasks that involve Agent memory
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Ijfw Memory Audit is an agent skill from FerroxLabs/ijfw. Audit and clean project memory files. Trigger: 'memory audit', 'clean memory', 'memory health', /memory-audit

Its SKILL.md is about 380 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Agent Workflows, covering Agent memory. The repository describes itself as: IJFW — It Just Fcking Works. Ferrox Labs' local-first infrastructure for AI coding agents: shared memory, smart routing, multi-AI cross-audits, disciplined workflow. The licence is MIT.

When your agent uses it

  • Tasks that involve Agent memory

Example prompts

  • “memory audit”
  • “clean memory”
  • “memory health”
  • “/ijfw-memory-audit”

Workflow steps

7 steps, taken from the first numbered list in SKILL.md.

  1. Call ijfw_metrics to get counts and last-update timestamps for all tiers.
  2. Scan .ijfw/memory/ for all .md files. For each, note
  3. Categorize entries
  4. Report
  5. Pruning question: for each entry flagged STALE or ARCHIVE, ask "Would removing this rule cause the agent to make a mistake?" If no…
  6. Ask before acting
  7. On confirmation, move flagged files. Store audit result

What it can do on your machine

Read from SKILL.md and the folder at commit eda62f3. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Ijfw Memory Audit loads about 382 tokens when it runs. Until then it costs about 32 tokens; SKILL.md has 119 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~32
When it runs · the whole SKILL.md, loaded when a task matches
~382

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from FerroxLabs/ijfw at commit eda62f3, republished under its MIT licence (© FerroxLabs). 119 words, ~382 tokens.

Download SKILL.mdSave it as .claude/skills/ijfw-memory-audit/SKILL.md (or your agent's skills folder).
name
ijfw-memory-audit
description
Audit and clean project memory files. Trigger: 'memory audit', 'clean memory', 'memory health', /memory-audit

Execution

  1. Call ijfw_metrics to get counts and last-update timestamps for all tiers.

  2. Scan .ijfw/memory/ for all .md files. For each, note:

    • File name, size (lines), last modified date.
    • Days since last referenced (use file mtime as proxy).

  3. Categorize entries:

ACTIVE  -- referenced within 30 days
STALE   -- not referenced in 30-90 days
ARCHIVE -- not referenced in 90+ days, or flagged as superseded
  1. Report:
MEMORY HEALTH REPORT
  Total entries: <N>  |  Total size: ~<X> lines
  Active: <N>  |  Stale: <N>  |  Archive candidates: <N>

STALE ENTRIES (>30 days unreferenced)
  - <filename>: <one-line summary>  [last seen: <date>]

ARCHIVE CANDIDATES (>90 days or superseded)
  - <filename>: <one-line summary>  [last seen: <date>]

RECOMMENDATION
  Archive <N> entries to .ijfw/memory/archive/. No data is deleted.

  1. Pruning question: for each entry flagged STALE or ARCHIVE, ask "Would removing this rule cause the agent to make a mistake?" If no, archive. If yes, keep and tighten. Memory that doesn't change behavior is bloat that crowds out memory that does.

  2. Ask before acting:

    Archive <N> stale entries? (y/n -- files move to .ijfw/memory/archive/, not deleted)

  3. On confirmation, move flagged files. Store audit result:

    ijfw_memory_store: memory audit on <date> -- archived <N> entries

© FerroxLabs, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in codex/skills/ijfw-memory-audit of FerroxLabs/ijfw.

Open the folder on GitHubat commit eda62f3

Compare with similar skills

Ijfw Memory Audit next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Ijfw Memory Audit compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Ijfw Memory Audit this skillFerroxLabs/ijfw212—~382Automated safety check: PassMIT
Coding Agent Session Findercode-yeongyu/oh-my-openagent70k1 repos~2.8kAutomated safety check: PassCustom licence
Claude-Mem Cloud Syncthedotmack/claude-mem98k1 repos~1kAutomated safety check: NotesApache-2.0
Cognee CLI Memory Commandstopoteretes/cognee32k1 repos~2.2kAutomated safety check: NotesApache-2.0
Neat-Freak Knowledge CloseoutKKKKhazix/khazix-skills21k—~1.9kAutomated safety check: PassMIT
Claude-Mem Searchthedotmack/claude-mem98k1 repos~511Automated safety check: PassApache-2.0

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  • Ijfw Cross Audit

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Categories

Questions about Ijfw Memory Audit

What does Ijfw Memory Audit do?

Audit and clean project memory files. An agent skill from FerroxLabs/ijfw. Ijfw Memory Audit is an agent skill from FerroxLabs/ijfw. Audit and clean project memory files.

When should I use Ijfw Memory Audit?

Ijfw Memory Audit fits situations like: tasks that involve Agent memory.

How do I install Ijfw Memory Audit in Claude Code?

Run `npx skills add FerroxLabs/ijfw --skill ijfw-memory-audit -a claude-code`. Or copy the skill folder (codex/skills/ijfw-memory-audit in FerroxLabs/ijfw) into .claude/skills/ijfw-memory-audit in your project. Claude Code loads it when a task matches its description.

How do I install Ijfw Memory Audit in Codex?

Run `npx skills add FerroxLabs/ijfw --skill ijfw-memory-audit -a codex`. Or copy the skill folder (codex/skills/ijfw-memory-audit in FerroxLabs/ijfw) into .agents/skills/ijfw-memory-audit in your project. Codex loads it when a task matches its description.

Can I use Ijfw Memory Audit in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add FerroxLabs/ijfw --skill ijfw-memory-audit -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ijfw-memory-audit, .gemini/skills/ijfw-memory-audit, .github/skills/ijfw-memory-audit and .opencode/skills/ijfw-memory-audit in your project.

What does Ijfw Memory Audit need to run?

SKILL.md names no scripts, command-line tools or credentials: Ijfw Memory Audit is instructions for the agent only.

Does Ijfw Memory Audit access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Ijfw Memory Audit safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Ijfw Memory Audit use?

Ijfw Memory Audit is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Ijfw Memory Audit use?

About 382 tokens (SKILL.md is roughly 1.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Ijfw Memory Audit?

Skills that share tags, products or a category with Ijfw Memory Audit: Coding Agent Session Finder (code-yeongyu/oh-my-openagent, 70k stars), Claude-Mem Cloud Sync (thedotmack/claude-mem, 98k stars), Cognee CLI Memory Commands (topoteretes/cognee, 32k stars) and Neat-Freak Knowledge Closeout (KKKKhazix/khazix-skills, 21k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Ijfw Memory Audit?

FerroxLabs (a GitHub user) maintains it in FerroxLabs/ijfw, which has 212 GitHub stars. The repository holds 38 skills in this directory. The repository was last updated on October 5, 2026.

Source: FerroxLabs/ijfw on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.