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Agent skill

Ijfw Auto Memorize

by FerroxLabs in FerroxLabs/ijfw

Session-end auto-extraction of lessons, errors, fixes, and user feedback into structured memory.

MITAuto-check passed

Install Ijfw Auto Memorize

skills CLI
$ npx skills add FerroxLabs/ijfw --skill ijfw-auto-memorize -a claude-code

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

GitHub CLI
$ gh skill install FerroxLabs/ijfw ijfw-auto-memorize --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/claude/skills/ijfw-auto-memorize .claude/skills/ijfw-auto-memorize && 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-auto-memorize
GitHub stars
212
Token cost
~932 tokens
SKILL.md length
429 words
Files
1
Skills in repo
38
Repo updated
First seen
Licence
MIT

At a glance

Session-end auto-extraction of lessons, errors, fixes, and user feedback into structured memory.

  • Works in 5 steps: Redact secrets first. Call… → Cap sizes. Run applyCaps from… → Dedupe. Use BM25 search… → …
  • SKILL.md covers Consent gate (first run only), Inputs (all local files), Synthesis and Model routing, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Ijfw Auto Memorize is an agent skill from FerroxLabs/ijfw. Session-end auto-extraction of lessons, errors, fixes, and user feedback into structured memory. Fires at session end. Requires consent on first run.

Its SKILL.md is about 930 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It works with Model Context Protocol. 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.

Example prompts

  • “/ijfw-auto-memorize”

Workflow steps

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

  1. Redact secrets first. Call redactSecrets() from mcp-server/src/redactor.js on every field that came from transcript or tool output.
  2. Cap sizes. Run applyCaps from mcp-server/src/caps.js. content ≤4KB, why/how ≤1KB, summary ≤120.
  3. Dedupe. Use BM25 search (mcp-server/src/search-bm25.js) against project-journal.md. If score > 6 against an existing entry, skip…
  4. Classify into one of
  5. Emit via ijfw_memory_store MCP tool with fields

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 Auto Memorize loads about 932 tokens when it runs. Until then it costs about 42 tokens; SKILL.md has 429 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~42
When it runs · the whole SKILL.md, loaded when a task matches
~932

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). 429 words, ~932 tokens.

Download SKILL.mdSave it as .claude/skills/ijfw-auto-memorize/SKILL.md (or your agent's skills folder).
name
ijfw-auto-memorize
description
Session-end auto-extraction of lessons, errors, fixes, and user feedback into structured memory. Fires at session end. Requires consent on first run.

Fires at session end. Reads deterministic signals captured during the session and synthesizes structured memories. Nothing leaves the machine unless the user explicitly configured an API model via IJFW_AUTOMEM_MODEL.

Before any synthesis, check .ijfw/.automem-consent:

  • If missing: ask the user once: "IJFW can automatically extract lessons (errors hit, fixes applied, preferences you stated) at session end into local memory. OK? (y/n). Reply y, n, or ask (ask again next time)." Write answer as {"consented": true|false, "at": "<iso>"} to .ijfw/.automem-consent.
  • If "consented": false: do nothing this session.
  • If "consented": true: proceed.

Inputs (all local files)

  • .ijfw/.session-signals.jsonl -- ERROR/FAIL/Traceback lines captured by the PreToolUse hook (W3.6).
  • .ijfw/.session-feedback.jsonl -- corrections/confirmations/preferences detected by the UserPromptSubmit hook (W3.7).
  • .ijfw/.prompt-check-state -- last turn's intent + vague signals.
  • .ijfw/memory/project-journal.md -- existing entries (dedupe against these).
  • Transcript read via Claude Code's Stop-hook payload (transcript_path).

Synthesis

For each signal cluster:

  1. Redact secrets first. Call redactSecrets() from mcp-server/src/redactor.js on every field that came from transcript or tool output.
  2. Cap sizes. Run applyCaps from mcp-server/src/caps.js. content ≤4KB, why/how ≤1KB, summary ≤120.
  3. Dedupe. Use BM25 search (mcp-server/src/search-bm25.js) against project-journal.md. If score > 6 against an existing entry, skip (duplicate).
  4. Classify into one of:
    • pattern -- error→fix recurrence (same error type seen >=2x).
    • decision -- an explicit user choice ("from now on X").
    • preference -- a style/workflow preference ("I prefer Y").
    • observation -- something worth noting, single instance.
  5. Emit via ijfw_memory_store MCP tool with fields:
    • type: one of the above
    • summary: single sentence, ≤120 chars
    • content: the fact + minimal context
    • why: where this came from (e.g., "user said 'don't use X'", or "hit error Y at step Z")
    • how_to_apply: when this should surface in future sessions
    • tags: include auto-memorize and the classifier kind (correction, confirmation, preference, rule, error)
Show full SKILL.md (145 more words)Show less

Model routing

IJFW_AUTOMEM_MODEL env var controls synthesis:

  • unset or off -- skip LLM synthesis; only deterministic signals promoted 1:1.
  • claude-haiku-4-5-* -- Anthropic Haiku (~$0.001/session).
  • ollama:<model> -- local Ollama, fully offline.

Default ship: unset. Deterministic signals still become memories; only the richer "what did I learn" synthesis is gated on an LLM budget.

Output to user

One-line summary in the terminal:

Stored 3 new memories: pagination-off-by-one fix, user prefers esbuild, stopped repeating rm -rf warnings.

No summary on zero-emit sessions.

Audit trail

Every auto-stored entry carries tags: [..., "auto-memorize"]. The /ijfw memory audit command lists recent auto-entries for review/removal.

Safety

  • Never store raw transcript content -- only redacted + capped extracts.
  • Never call out to an LLM unless IJFW_AUTOMEM_MODEL is set AND consent is true.
  • Never store secrets -- the redactor runs first, always.
  • Never silently overwrite user-authored memories -- auto-entries go into the knowledge file with their distinguishing tag.

Resume normal mode after.

© 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 claude/skills/ijfw-auto-memorize of FerroxLabs/ijfw.

Open the folder on GitHubat commit eda62f3

Compare with similar skills

Ijfw Auto Memorize 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 Auto Memorize compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Ijfw Auto Memorize this skillFerroxLabs/ijfw212—~932Automated safety check: PassMIT
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MCP Integration for Pluginsanthropics/claude-plugins-official38k11 repos~3.1kAutomated safety check: PassApache-2.0
Figma use_figma Plugin API Ruleswarpdotdev/warp65k4 repos~4.4kAutomated safety check: PassAGPL-3.0
Stitch to Remotion Walkthrough Videosgoogle-labs-code/stitch-skills8.5k6 repos~3.2kAutomated safety check: NotesApache-2.0

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Works with

Questions about Ijfw Auto Memorize

What does Ijfw Auto Memorize do?

Session-end auto-extraction of lessons, errors, fixes, and user feedback into structured memory. Ijfw Auto Memorize is an agent skill from FerroxLabs/ijfw. Session-end auto-extraction of lessons, errors, fixes, and user feedback into structured memory.

How do I install Ijfw Auto Memorize in Claude Code?

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

How do I install Ijfw Auto Memorize in Codex?

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

Can I use Ijfw Auto Memorize 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-auto-memorize -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-auto-memorize, .gemini/skills/ijfw-auto-memorize, .github/skills/ijfw-auto-memorize and .opencode/skills/ijfw-auto-memorize in your project.

What does Ijfw Auto Memorize need to run?

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

Does Ijfw Auto Memorize 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 Auto Memorize 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 Auto Memorize use?

Ijfw Auto Memorize 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 Auto Memorize use?

About 932 tokens (SKILL.md is roughly 3.7k 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 Auto Memorize?

Skills that share tags, products or a category with Ijfw Auto Memorize: MCP Server Builder (anthropics/skills, 180k stars), MCP Server Builder (shareAI-lab/learn-claude-code, 78k stars), MCP Integration for Plugins (anthropics/claude-plugins-official, 38k stars) and Figma use_figma Plugin API Rules (warpdotdev/warp, 65k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Ijfw Auto Memorize?

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.