Persistent per-project memory for Claude Code. Auto-loads project context on session start, tracks sessions with git activity, and writes to native memory. Commands run deterministic Node.js scripts — behavior is consistent across model versions.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "ck" skill from askskill: 1. Download https://raw.githubusercontent.com/affaan-m/ECC/main/skills/ck/SKILL.md 2. Save it as ~/.claude/skills/ck/SKILL.md 3. Reload skills and tell me it's ready
You are the Context Keeper assistant. When the user invokes any /ck:* command,
run the corresponding Node.js script and present its stdout to the user verbatim.
Scripts live at: ~/.claude/skills/ck/commands/ (expand ~ with $HOME).
~/.claude/ck/
├── projects.json ← path → {name, contextDir, lastUpdated}
└── contexts/<name>/
├── context.json ← SOURCE OF TRUTH (structured JSON, v2)
└── CONTEXT.md ← generated view — do not hand-edit
/ck:init — Register a Projectnode "$HOME/.claude/skills/ck/commands/init.mjs"
The script outputs JSON with auto-detected info. Present it as a confirmation draft:
Here's what I found — confirm or edit anything:
Project: <name>
Description: <description>
Stack: <stack>
Goal: <goal>
Do-nots: <constraints or "None">
Repo: <repo or "none">
Wait for user approval. Apply any edits. Then pipe confirmed JSON to save.mjs --init:
echo '<confirmed-json>' | node "$HOME/.claude/skills/ck/commands/save.mjs" --init
Confirmed JSON schema: {"name":"...","path":"...","description":"...","stack":["..."],"goal":"...","constraints":["..."],"repo":"..." }
/ck:save — Save Session StateThis is the only command requiring LLM analysis. Analyze the current conversation:
summary: one sentence, max 10 words, what was accomplishedleftOff: what was actively being worked on (specific file/feature/bug)nextSteps: ordered array of concrete next stepsdecisions: array of {what, why} for decisions made this sessionblockers: array of current blockers (empty array if none)goal: updated goal string only if it changed this session, else omitShow a draft summary to the user: "Session: '<summary>' — save this? (yes / edit)"
Wait for confirmation. Then pipe to save.mjs:
echo '<json>' | node "$HOME/.claude/skills/ck/commands/save.mjs"
JSON schema (exact): {"summary":"...","leftOff":"...","nextSteps":["..."],"decisions":[{"what":"...","why":"..."}],"blockers":["..."]}
Display the script's stdout confirmation verbatim.
/ck:resume [name|number] — Full Briefingnode "$HOME/.claude/skills/ck/commands/resume.mjs" [arg]
Display output verbatim. Then ask: "Continue from here? Or has anything changed?"
If user reports changes → run /ck:save immediately.
/ck:info [name|number] — Quick Snapshotnode "$HOME/.claude/skills/ck/commands/info.mjs" [arg]
Display output verbatim. No follow-up question.
/ck:list — Portfolio Viewnode "$HOME/.claude/skills/ck/commands/list.mjs"
Display output verbatim. If user replies with a number or name → run /ck:resume.
/ck:forget [name|number] — Remove a ProjectFirst resolve the project name (run /ck:list if needed).
Ask: "This will permanently delete context for '<name>'. Are you sure? (yes/no)"
If yes:
node "$HOME/.claude/skills/ck/commands/forget.mjs" [name]
Display confirmation verbatim.
/ck:migrate — Convert v1 Data to v2node "$HOME/.claude/skills/ck/commands/migrate.mjs"
For a dry run first:
node "$HOME/.claude/skills/ck/commands/migrate.mjs" --dry-run
Display output verbatim. Migrates all v1 CONTEXT.md + meta.json files to v2 context.json.
Originals are backed up as meta.json.v1-backup — nothing is deleted.
The hook at ~/.claude/skills/ck/hooks/session-start.mjs must be registered in
~/.claude/settings.json to auto-load project context on session start:
{
"hooks": {
"SessionStart": [
{ "hooks": [{ "type": "command", "command": "node \"~/.claude/skills/ck/hooks/session-start.mjs\"" }] }
]
}
}
The hook injects ~100 tokens per session (compact 5-line summary). It also detects unsaved sessions, git activity since last save, and goal mismatches vs CLAUDE.md.
…
Generate test scenarios and measure how well agents follow skills and rules.
Identify remotely exploitable, bounty-worthy security issues in code repositories.
Write, review, and refactor C++ with modern, safe, idiomatic standards.
Run a four-voice council to evaluate tradeoffs before making tough decisions.
Apply consistent Java coding standards for Spring Boot and Quarkus services.
Query genomic databases, run sequence searches, and log reproducible bioinformatics evidence.