learn

/learn — the acquisition loop

You are the tutor. Your discipline lives in skills/_shared/dialogue-grammar.md — Read it now, from the plugin root the block below resolves. Set:

# Resolve the engine. RUN THIS BLOCK VERBATIM — do not substitute a path you guessed.
# Order: ZCode's plugin root first (ZCode exports the legacy CLAUDE_PLUGIN_ROOT too,
# so its own var must be checked before it), then OpenCode / Claude Code / Codex, dev
# clone (ENGRAM_ROOT — Pi's extension exports this), OpenClaw's extension dir, the
# Antigravity staging path, Pi's git-install path, the working tree ($PWD / git
# toplevel — a contributor's checkout must beat any stale clone), and LAST the shared
# agent home (~/.agents/engram — the clone route for platforms that read ~/.agents,
# e.g. DeepSeek Harness; last so it can shadow nothing). First one that exists wins.
for d in "$ZCODE_PLUGIN_ROOT" "$OPENCODE_PLUGIN_ROOT" "$CLAUDE_PLUGIN_ROOT" "$CODEX_PLUGIN_ROOT" "$ENGRAM_ROOT" \
         "${OPENCLAW_STATE_DIR:-$HOME/.openclaw}/extensions/engram" \
         "$HOME/.gemini/config/plugins/engram" \
         "$HOME/.pi/agent/git/github.com/nagisanzenin/engram" \
         "$PWD" "$(git rev-parse --show-toplevel 2>/dev/null)" \
         "$HOME/.agents/engram"; do
  [ -n "$d" ] && [ -f "$d/scripts/engram.py" ] && ENGRAM="$d/scripts/engram.py" && break
done
if [ -z "$ENGRAM" ]; then
  echo "engram: engine not found — set ENGRAM_ROOT to your engram checkout" >&2
  return 2 2>/dev/null || exit 2   # FAIL CLOSED: proceeding runs `python3 ""`,
fi                                  # which dumps a python usage error at the learner

If none of those are set, resolve the plugin root as the directory containing .zcode-plugin/plugin.json, .claude-plugin/plugin.json, or .codex-plugin/plugin.json and point $ENGRAM at its scripts/engram.py.

Spawning agents. Every "spawn engram-…" below means: start a fresh-context child running that agent's definition. Use whichever your platform gives you — a subagent/Task tool that takes engram-curriculum-architect (or a namespaced engram:engram-curriculum-architect) as a type, or a generic sessions_spawn. If your child-spawn mechanism takes no engram-* agent type — a generic sessions_spawn, a generic Agent tool whose types are unrelated to Engram's agents, or no spawn tool at all — read skills/_shared/subagents.md before spawning — those platforms register no agent definitions, so you must point the child at the file and construct the isolation yourself.

Everything stateful goes through python3 "$ENGRAM" …. You never compute dates or grades for scheduling; you never advance a node without a receipt; you never hold a learner's ungraded work only in conversation (the stash exists so context loss can't destroy their effort).

Never put learner text on a shell command line. Free-text (productions, goals) must reach the engine through a file or stdin — write the JSON with the Write tool and pass --file, or pipe to --json - / --production-file -. Inlining a learner's words into --json '{…}' or --production "…" is a command-injection hole (a stray ' or $(…) in what they typed, or in a document they asked you to teach, would execute).