enrich-context

name	enrich-context
description	Augment a Wren project with business context that DB schema cannot carry — enum value meanings, units (USD vs cents, ms vs sec), NULL semantics, magic sentinels (-1 = unknown), soft-delete default filters, business synonyms, time-grain / TZ conventions, cross-system identifiers, currency rules, canonical-table preferences, AND named aggregation metrics (ARR, churn, DAU, WAU, NRR) proposed as cubes. Runs in one of two modes selected at session start: `grill` (one question at a time, user-driven) or `auto-pilot` (agent infers and applies, escalates only on conflicts and high-blast-radius additions like new cubes / views / relationships). Reads everything under <project>/raw/ (PDFs, glossaries, handbooks, code, data dictionaries) and optionally samples low-cardinality columns from the live DB (grill mode), compares against the current MDL / cubes / knowledge (rules + NL→SQL pairs), then fills gaps via the ten-category gap catalog and the cube proposal flow. Confirmed findings are written back to the right sink. Use when: user says 'enrich context', 'augment my project', 'grill me on this project', 'auto-fill my context', 'agent doesn't understand our docs / enum values / units / null meanings', 'business context is missing', 'what does status=A mean', 'is this amount in USD or cents', 'we keep getting wrong aggregations', 'add cubes for ARR / DAU / churn', 'we have a handbook / glossary / data dictionary the agent should know'; or after generating an MDL and noticing the agent lacks business semantics.
license	Apache-2.0
metadata	{"author":"wrenai"}

Wren Enrich Context — Fill the Business-Context Gap

This skill exists because most business context never lives in a DB schema — it lives in handbooks, glossaries, finance reports, support playbooks, code comments, Slack rules-of-thumb. The agent reads those raw artifacts, finds what's missing from the Wren project, and either grills the user one question at a time (grill mode) or applies its best inferences directly and hands over an audit (auto-pilot mode) before writing back. The output lands in the sinks each project already has — MDL, cubes/, knowledge/rules/, and knowledge/sql/ — no new artifact, no new tooling.

Hard rules — READ FIRST

Universal (apply to both modes)

1. Only add, never modify existing. If you find an existing MDL description / relationship / rule that looks wrong, do not edit it. Surface it on the "please fix manually" list shown in Step 9.
2. Every MDL edit must validate. Right after any MDL YAML change, run wren context validate. If it fails, revert that single change and feed the error back. Never leave a project in an invalid state.
3. Pre-draft every proposal. Whether you're showing the draft to the user (grill) or applying it directly (auto-pilot), generate the concrete content — never lazy-ask "what should the description say?".
4. Be explicit about confidence. In grill mode, open Lane 3 inference questions with "I'm guessing — ". In auto-pilot, tag every Lane 3 inference and partial Lane 2 match in the Step 9 audit with confidence (high / med / low) and source.

Grill mode only

5. One question at a time. Grill relentlessly. Walk every gap top-down, resolve one decision before moving to the next. Provide a recommended answer for every question.
6. Skip is final for this session. No pending queue, no nagging next round. If the user wants to revisit, they re-run the skill.

Auto-pilot mode only

7. Drop into grill for three cases. Always interrupt auto-pilot and ask the user when:

   • (a) Lane 2 conflict — raw and current MDL disagree.
   • (b) High-blast-radius proposal (any lane) — new cube, new view, new relationship, or new MDL metric/calculated column. These become public artifacts visible to every future agent session, so blast radius doesn't depend on whether the trigger was raw evidence (Lane 2) or inference (Lane 3).
   • (c) Lane 2 routing ambiguity — you can't confidently pick a sink (MDL / knowledge/rules/ / knowledge/sql/ / cubes/).

   Everything else: apply directly and log to the audit list.

Step 0 — Mode selection (before anything else)

Before touching the project or reading any file, ask the user which mode to run in. Lock the choice for the whole session — no mid-session switching; the user re-runs to change.

Two modes for this session:

a) Grill mode — I walk every gap with you, one question at a time, proposing a draft and waiting for your accept / edit / skip. You stay in the driver's seat. Best when the raw material is sensitive, when you want to learn what I don't know about your project, or when you'd rather review than re-do.

b) Auto-pilot mode — I read raw + current context, make my best inferences, and apply them. I'll only stop to grill you on (1) conflicts between raw and existing MDL and (2) high-blast-radius additions like new metrics, views, or relationships. The session ends with a full diff + confidence-tagged inference list for you to audit.

Which? (a / b)

Remember the choice as MODE = grill | autopilot and use it to branch Steps 6 and 9.

Preflight

Step 1 — Choose the Wren project

Always ask the user which project to enrich before doing anything else — never assume cwd. A user can have several Wren projects and an ambient ~/.wren profile that doesn't match the one they want to augment.

Offer concrete hints in the question so the user can answer in one round-trip:

# Hint 1 — does cwd look like a project?
test -f wren_project.yml && pwd

# Hint 2 — does ~/.wren/config.yml point at a default project?
grep -E '^project_path:' ~/.wren/config.yml 2>/dev/null

Then ask:

Which Wren project do you want me to augment? a) $PWD (current directory) ← if Hint 1 matched b) <path from ~/.wren/config.yml> ← if Hint 2 matched c) something else — paste the absolute path

After the user answers, lock the path in for the whole session:

cd <chosen-path>
test -f wren_project.yml || {
  echo "Error: <chosen-path> is not a Wren project (no wren_project.yml)."
  exit 1
}
wren context show >/dev/null 2>&1 || {
  echo "Error: wren context show failed — manifest may be invalid."
  exit 1
}

If either check fails, stop and tell the user — suggest wren skills get onboarding if it's not a project, or wren context validate if the manifest is broken.

From this point on, every command and file path in this skill is relative to the chosen project root. Do not switch projects mid-session — if the user wants to work a different project, end this session and re-run.

Step 2 — Detect semantic-memory availability

wren memory status 2>/dev/null | grep -q "Backend: lancedb"

Writing NL→SQL pairs (wren memory store → knowledge/sql/*.md) works regardless — that sink is always open. This checks which recall backend is active, i.e. whether the optional memory extra is installed (LanceDB = semantic; grep = dependency-free fallback):

• Match (Backend: lancedb) → set MEMORY_AVAILABLE = true. Semantic recall and wren memory fetch are usable, and wren memory index builds an embedding index in Step 8.
• No match (grep backend) → set MEMORY_AVAILABLE = false. Skip the semantic read/index paths below; pair writeback still happens via wren memory store, and grep recall still works.

Step 3 — Ensure raw/ folder exists

From the project root (cwd is already there from Step 1):

mkdir -p raw

If you just created it (the directory was empty or new):

I've created raw/ at the project root. Drop anything you think helps explain this project's business context — PDFs, glossaries, handbooks, financial reports, data dictionaries, sample queries, code with comments, screenshots of dashboards, anything.

Heads-up: the contents may be sensitive. Decide for yourself whether to commit raw/ to git — I won't touch .gitignore.

Tell me when you've added the files and I'll start reading.

Wait for the user to confirm before continuing.

Step 4 — Read everything

Read both sides — the raw material and the current Wren context — before forming any opinion.

Raw

Read every file under raw/. Use whatever capability your agent has natively (text, markdown, code, PDF). If you genuinely can't read a particular file, tell the user once which file and suggest converting it to text or pasting the relevant excerpt — then move on to the rest. Do not install extra Python packages, do not reach for new CLI subcommands.

Current Wren context

Source	Command
MDL (full)	wren context show --output json
Business rules	wren context instructions (reads knowledge/rules/ + any legacy instructions.md)
Existing cubes (names)	wren cube list
Existing cubes (measures + dimensions)	wren cube describe <cube> for each name above
NL→SQL pairs	read knowledge/sql/*.md directly
(Memory) stored pairs	wren memory list -n 200 --output json
(Memory) schema as text	wren memory describe

The memory rows only matter when MEMORY_AVAILABLE = true. Reading cubes is essential before any Lane 3 metric proposal — see cube_proposals for the duplication guard.

Step 4.5 — Ground-truth probe (grill mode default; auto-pilot opt-out)

When raw is silent on a column's enum / unit / null / magic / time semantics, the catalog's column-local categories (#1, #2, #3, #5, #7 in gap_catalog) can often be settled directly by sampling distinct values from the live DB. Read gap_catalog before this step — its Trigger column tells you which columns are probe candidates.

Default policy by mode:

Mode	Default	How to override
Grill	Probe on. Before the first query, ask the user once: "I want to sample N columns with LIMIT 30 each to find enum / sentinel / time-grain values — OK?" Lock the answer for the session.	User says no → skip Step 4.5 entirely; rely on Lane 2 + Lane 3 instead.
Auto-pilot	Probe off. The skill never queries the live DB in auto-pilot mode.	None — user must re-run in grill mode if probe would unblock high-confidence inferences.

Candidate selection (no DB call yet):

A column is a probe candidate when all hold:

• Description is empty OR does not yet contain the relevant [tag] (catalog write format).
• Column type / name pattern matches catalog #1 (enum), #3 (NULL), #5 (magic), or #7 (time grain).
• For #3 and #7, the description also lacks event-vs-record or TZ wording.

Categories #2 (unit), #4, #6, #8, #9, #10 are not probable — SELECT DISTINCT doesn't reveal units, default filters, synonyms, external mappings, currency conventions, or canonical-table preferences. Those need raw evidence or human judgment.

Probe query:

wren --sql "SELECT DISTINCT <col> FROM <model> LIMIT 30" --output json
# For magic sentinels (catalog #5), also fetch min/max:
wren --sql "SELECT MIN(<col>) AS lo, MAX(<col>) AS hi FROM <model>" --output json

• ≤ 30 distinct values returned → enum / sentinel / grain candidate. Draft the [tag] line and surface to user (grill) with confidence "med — probed values, semantics still inferred".
• 30 returned (LIMIT hit) → cardinality too high; not an enum / sentinel candidate. Skip.
• Query fails (permissions, connection, large-table timeout) → do not retry. Log the failure to the audit list, surface in Step 9, continue with Lane 2 + Lane 3 only.

Safety:

• Probe each (model, column) at most once per session.
• Never probe a column that already has a matching [tag] line — Universal Rule 1.
• Probe results stay in working memory; do not write them to disk.

Step 5 — Three gap-detection lanes (in your head, no artifact)

Hold all three lanes in working memory. Do not write a gaps.yml.

Before sweeping, load gap_catalog — the ten business-semantic categories the schema cannot carry. Each lane consumes the catalog differently: Lane 1 walks it as type-aware mechanical triggers, Lane 2 classifies each atomic raw claim into one of the 10 categories before routing, Lane 3 seeds inference prompts when a trigger fires but raw is silent.

Lane 1 — Structural coverage (mechanical)

Scan the current MDL and check:

• Every model has a non-empty properties.description?
• Every column has a description (at least for non-PK, non-FK ones)?
• Every model has a primary_key?
• Every model has at least one relationship (orphan models are suspicious)?
• knowledge/rules/ has real content beyond the scaffold default?
• knowledge/sql/ has at least a few canonical NL→SQL pairs?

Plus, walk every column / model against gap_catalog triggers:

• For each column matching catalog #1 / #2 / #3 / #5 / #7 triggers → is the corresponding [tag] line present in properties.description?
• For each model with a soft-delete column (deleted_at, is_active, archived_at, etc.) → is there a ## Default filters rule in knowledge/rules/ covering it (catalog #4)?
• For each lookalike table pair (e.g. users / users_v3) → is there a ## Canonical tables rule (catalog #10)?
• For each *_currency / fx_rate / external-system ID column → is the matching ## Currency (#9) or ## External identifiers (#8) section present?
• Business terms in knowledge/rules/ or raw that don't map verbatim to model / column names → catalog #6 ## Naming conventions rule missing.

Each unsatisfied check is a candidate. Combine with Step 4.5 probe results (if available) before moving to Lane 2.

Lane 2 — Claim-diff (raw vs current context)

For each raw file, internally extract 5–15 atomic claims — single statements that could be true or false, e.g. "an order has exactly one customer", "user means type=default by default", "ARR equals MRR × 12 minus refunds". Then for each claim, classify against the current Wren context:

Class	Meaning	Resolution outcome
covered	already reflected in MDL / instructions / pairs	skip
partial	the topic exists but the wording / scope differs	propose tightening
new	nothing in current context matches	route to a sink
conflict	raw says A, current context says B	grill the user (both modes), but do not edit existing — surface for manual fix

Lane 3 — Inference (your own guesses)

After reading raw and the current MDL, propose additions the user did not literally state in raw but that would clearly help the agent later. Examples:

• "I see quarterly_churn referenced five times in finance.pdf. No existing cube covers it. Want me to add cubes/quarterly_churn/metadata.yml with measure = COUNT(*) FILTER (WHERE churned_at IS NOT NULL) / NULLIF(COUNT(*), 0)?" — see cube_proposals for the YAML template and duplication guard.
• "Your support handbook keeps mentioning core users without defining it. Is this users WHERE tier = 'premium'? Want me to make a view?"
• "The data dictionary says events.payload is JSON but the column has no description — let me draft one."

For any aggregation-shaped proposal (SUM, COUNT, AVG, "by month / by status / per customer" patterns), default to a cube. Run wren cube list + wren cube describe first to confirm no existing cube already covers the measure expression; if one does, skip the proposal and add a knowledge/sql/ example pointing at the existing cube instead. The full decision tree, naming rules, and validation flow live in cube_proposals.

In grill mode, open every Lane 3 question with "I'm guessing — ". In auto-pilot, tag the audit entry with agent inference so the user sees you extrapolated.

Step 6 — Resolve gaps

Branch on the MODE locked in Step 0.

Grill mode

Use this conversational pattern for every gap surfaced in Lanes 1–3:

Interview the user relentlessly about every gap until we reach a shared understanding. Walk down each branch of the decision tree, resolving dependencies between decisions one-by-one. For each question, provide your recommended answer.

Ask the questions one at a time.

If a question can be answered by exploring the codebase or the raw files, do that instead of asking.

For every grill turn:

1. State the gap and where it came from (Lane 1 / 2 / 3, and for Lane 2 quote the raw file + a short excerpt).
2. Propose the concrete answer — draft the description, the rule, the SQL pair, the relationship.
3. Propose the sink ("I'll add this to knowledge/rules/ as a rule" / "I'll add this to the users model description in MDL").
4. Let the user accept / edit / skip.
5. On accept: write back (Step 7).
6. On edit: apply their wording, then write back.
7. On skip: drop it, move to the next gap. Do not requeue.

When the user gives a curve-ball answer ("actually we don't track that") — pivot. The goal is shared understanding, not pushing a pre-built list.

Auto-pilot mode

Process every finding from Lanes 1–3 directly — except for the three escalation cases from Universal Rule 7:

• Lane 2 conflict — drop into grill for this single question, never auto-resolve.
• Lane 3 proposing a new metric / view / relationship — drop into grill before applying.
• Lane 2 routing ambiguous — drop into grill for the sink choice only, then auto-apply.

For everything else (Lane 1 mechanical fixes, Lane 2 unambiguous new claims, Lane 3 low-impact description tweaks):

1. Synthesize the concrete proposal (description / rule / SQL pair) using your best inference.
2. Decide the sink using the routing table (Step 7).
3. Write back.
4. Run wren context validate immediately after any MDL edit. On failure: revert the single change and log the revert with the error.
5. Append to the audit list: the finding, the sink, confidence tag (high / med / low), and source (raw file + excerpt for Lane 2, agent inference for Lane 3, structural for Lane 1).

Auto-pilot does not pause for confirmation on each item — the user reviews the full diff + audit list in Step 9. They are the reviewer, not the gatekeeper.

Step 7 — Routing & writeback

Decide the sink as part of the proposal (Step 6.3 in grill mode; Step 6.2 in auto-pilot), so the user can correct routing in grill mode and audit it in auto-pilot.

Finding type	Sink	How to write
Schema structure / relationship / view / model or column description	MDL YAML under models/, views/, relationships.yml	Edit the YAML file directly. For catalog #1 / #2 / #3 / #5 / #7 / PII, append a [tag] line to properties.description (prose first, then one tag per category). See gap_catalog for the exact tag format and triggers.
Aggregation metric / named measure (with measures + dimensions)	cubes/<name>/metadata.yml	New file per cube. Default sink for any SUM / COUNT / AVG / ratio metric raw defines or Lane 3 infers. See cube_proposals for the YAML template, naming policy, duplication guard, and validation flow. Run wren context validate + wren cube query --cube <name> --sql-only after writing; revert on either failure. Always escalates to grill in auto-pilot (Universal Rule 7b).
Default filter / implicit rule / business convention / naming convention / external mapping / currency / canonical table	knowledge/rules/	Append under the catalog-specified ## section heading (#4 → ## Default filters, #6 → ## Naming conventions, #8 → ## External identifiers, #9 → ## Currency, #10 → ## Canonical tables) inside a topic file under knowledge/rules/ (e.g. knowledge/rules/conventions.md). Create the file/heading if absent; never modify existing text.