Fireworks Skill Memory

fireworks-skill-memory

Persistent experience memory for Claude Code and Codex skills. Shared memory core, runtime-specific adapters, skill-scoped lessons.

What It Does

Every coding-agent session starts from zero. The same mistakes repeat — wrong API parameters, broken sequences, proxy pitfalls — because the runtime has no durable skill memory between sessions.

fireworks-skill-memory solves this by:

1. Injecting past experience when a skill is invoked
2. Distilling new lessons into skill-scoped knowledge files
3. Keeping runtime-specific adapters thin so Claude hooks and Codex explicit flows share one memory core

Installation

Quick Install (Recommended)

In Claude Code, say:

"Help me install fireworks-skill-memory from https://github.com/yizhiyanhua-ai/fireworks-skill-memory"

Or run the one-command installer:

curl -fsSL https://raw.githubusercontent.com/yizhiyanhua-ai/fireworks-skill-memory/main/install.sh | bash

npx skills Install

npx skills add yizhiyanhua-ai/fireworks-skill-memory -g

After installing via npx skills, run the installer to set up hooks:

curl -fsSL https://raw.githubusercontent.com/yizhiyanhua-ai/fireworks-skill-memory/main/install.sh | bash

Codex Setup

Run:

./install-codex.sh

Then use:

python3 cli/skill_memory.py inject --skill <skill-name>
python3 cli/skill_memory.py checkpoint --skill <skill-name> --note "..."
python3 cli/skill_memory.py flush --skill <skill-name> --summary-file ./session-summary.md

How It Works

Claude Code installs 4 hooks that run automatically:

Codex uses explicit runtime commands instead:

Data Flow

Skill invoked → PreToolUse injects experience → Claude executes with context
                                                    ↓
Session ends → Stop hook reads transcript → Haiku distills 1-3 lessons
                                                    ↓
                              KNOWLEDGE.md updated → Ready for next session

Codex task starts → inject loads top lessons → Codex executes with context
                                                ↓
Checkpoint/summary captured → flush distills explicit lessons
                                                ↓
                      KNOWLEDGE.md updated → Ready for next session

Knowledge Storage

<memory-home>/skills/<skill-name>/KNOWLEDGE.md   ← Distilled per-skill experience
<memory-home>/skills/<skill-name>/CHECKPOINTS.md ← Raw runtime notes
<memory-home>/global/KNOWLEDGE.md                ← Global cross-skill principles

Claude legacy runtime also uses:
~/.claude/skills/<skill-name>/KNOWLEDGE.md
~/.claude/skill-usage-stats.json
~/.claude/skill-memory.log

Each entry is tagged with [YYYY-MM] timestamp and [HIT:N] usage counter. Low-frequency, old entries are evicted first.

Configuration (Optional)

All settings are optional, configured via environment variables:

Requirements

• Python 3.9+
• Claude Code CLI for the automatic Claude runtime
• Claude Haiku access for the default Claude CLI distiller backend
• CODEX_HOME for the Codex runtime setup helper
• OPENAI_API_KEY / OPENAI_BASE_URL / OPENAI_MODEL when using the OpenAI distiller backend
• SKILLS_DISTILLER_DEBUG or SKILLS_DISTILLER_LOG when debugging backend failures

More Information

• Full Documentation
• 中文文档
• Report Bug