enzyme

Enzyme

Use Enzyme for local retrieval over an initialized markdown workspace. Run all enzyme commands from the vault/workspace root. For Hermes, this is the directory where Hermes is launched.

Vault path: -p flag > ENZYME_VAULT_ROOT env var > current directory.

For Hermes, this skill is for operational use inside a user's workspace, not for developing Hermes itself. Run Enzyme from the directory where Hermes is launched so the same AGENTS.md/.hermes.md context and markdown corpus are visible to both.

Prerequisite: check the installed binary first with enzyme --version. If the binary is missing, install Enzyme before setup. If Enzyme is installed but old, tell the user you are upgrading Enzyme to the latest release before setup, then run the normal installer path. If the pre-upgrade version is older than 0.5.8, warn that older Enzyme releases used a different auth/provider contract; after upgrading, ask before clearing stale authentication. Never delete ~/.enzyme/auth.json or run enzyme logout without explicit confirmation. If this skill is loaded, runtime instructions are already available; do not call enzyme install <runtime> as part of normal vault setup.

Auth and provider safety: let Enzyme decide when auth is needed. Do not preflight ~/.enzyme/auth.json or start login before a command asks for it. If an Enzyme command reports that login is required, start device login in the background, read JSONL events from /tmp/enzyme-login.log, show the verification URI/code when present, wait for success/error/expiry, then retry the original command. Never ask the user to paste API keys or auth tokens.

Never print API key values. Only when the user explicitly requests their own provider, inspect which LLM environment variable names exist before enzyme init:

env | cut -d= -f1 | grep -E '^(ENZYME_JEV_MODEL|OPENAI_API_KEY|OPENAI_BASE_URL|OPENAI_MODEL|ENZYME_LOCAL_MODEL)$'

Enzyme ignores inherited LLM env keys by default. Do not unset env vars as a workaround, and do not silently spend the user's personal OpenAI/OpenRouter key just because it exists in the shell. If OPENAI_API_KEY is present, warn that using env credentials intentionally should normally include the complete OpenAI-compatible triple: OPENAI_API_KEY, OPENAI_BASE_URL, and OPENAI_MODEL. A bare OPENAI_API_KEY falls back to OpenAI defaults, which can produce unauthorized/provider-mismatch errors when the key actually belongs to OpenRouter, a local proxy, or another compatible provider. If only model/base-url vars are present without a matching key, treat it as partial env config and do not use it.

Before init or generation, run enzyme model status and honor its configured and effective modes. With no user preference, preserve auto: it uses the selected local model when installed and hosted generation otherwise. Explain whether the effective path needs network access before setup proceeds. Do not download a model, run model use, or run model disable without explicit consent. If the user intentionally wants to use their own OpenAI/OpenRouter/OpenAI-compatible provider, verify only the presence of the needed env var names without printing values and pass --use-env-llm:

enzyme init --quiet --use-env-llm
enzyme refresh --quiet --use-env-llm

The dedicated setup skill owns the full provider disclosure and consent flow. An app-level approval decision never substitutes for it.

Enzyme does not replace the user's memory system. It indexes the markdown structure the user already has: folders, tags, wikilinks, dates, inboxes, daily notes, people pages, and frontmatter. Preserve that structure and use it as retrieval signal. Be exact about frontmatter: literal wikilinks work in every field, while plain string/list values become generic link entities only when the active vault/workspace config names that field in frontmatter_link_fields; the field name does not create a typed person/project model.

User-facing mental model: Enzyme compiles a workspace for agents. Init does the slow interpretive pass once. It indexes every note so search matches both the user's wording and their meaning, and it reads how the ideas that keep returning have moved over time, writing source-grounded questions about what the user decided, changed, or left open. At runtime an agent gets both halves: a map of what matters and how it has been moving, and search that finds the passages behind it whether the user names something exactly or only describes it. The agent orients on the map, searches from several angles, and answers from the user's own passages, so answers trace back to what the user actually wrote. Refresh folds new writing into both, so the map keeps up as the thinking moves.