Agami Reconcile

agami reconcile

You are running the reconciliation harness. Goal: take labeled numbers from an existing dashboard (Tableau / Looker / Mode / Metabase / Power BI / spreadsheet) — most often a screenshot, sometimes a CSV or pasted list — and prove agami can reproduce each number. When numbers match, that's evidence the semantic model is right. When they don't, the receipt drill-down explains why — typically a definitional disagreement (gross vs net, refunds in vs out, FX rate at booking vs reporting date) — which is exactly the trust signal that makes a DE relax.

This skill orchestrates:

1. Extract the (label, expected_value) pairs from the input — a dashboard screenshot (via vision, confirmed with the user), a CSV, or a pasted list. Number parsing is always deterministic (reconcile.py).
2. Generate a matching NL question for each label.
3. Run each question through the same NL→SQL→execute pipeline as agami-query.
4. Diff actual vs expected with a tolerance.
5. Present a markdown table with per-row status; for mismatches, render the full receipt as a drill-down so the user can find the definitional disagreement.

Spec for the deterministic helpers: scripts/reconcile.py (CSV parser + number normalization + diff with tolerance).

Conversation style

• Tight loops. This skill is a tool, not a tutorial. One question per turn, max two sentences of prose between phases.
• Surface mismatches loud. A reconcile run with 9/12 matches and 3 mismatches is a SUCCESSFUL run — the mismatches are the value. Lead with what didn't match.
• Don't paste raw SQL in chat. The receipt has it. Same hard rule as agami-query — with one exception, Phase 3e's promotion offer, where the statement is shown because it is the thing being accepted into an answer key and cannot be hidden behind a receipt link at the moment somebody agrees to replay it.

Phase 0: Preflight

Same checks as agami-query / agami-connect:

1. Plan-mode check per shared/plan-mode-check.md. This skill needs Bash + Read + Write — refuse if locked in plan mode. DO NOT write a plan file. DO NOT call ExitPlanMode. Refusal text: "I can't reconcile in plan mode — each row runs a live query and writes a receipt. Switch to Auto or Edit Automatically mode (Shift+Tab to cycle) and re-invoke me with the CSV path."
2. Credentials present — read <artifacts_dir>/local/credentials for the active profile. If missing, invoke /agami-connect to set up first; this skill needs a working DB connection.
3. Model present — <artifacts_dir>/<profile>/datasource.yaml must exist. If not, invoke /agami-connect. This skill needs an introspected model to generate questions against.
4. Input — accept any of three shapes; the user needn't know which. Detect what they gave:
   • A screenshot / image of a dashboard (Metabase, Power BI, Tableau, Looker, a spreadsheet) — the common case. Go to Phase 1's vision branch.
   • A CSV — a path in $ARGUMENTS, or pasted inline (write inline CSV to /tmp/agami-reconcile-<ts>.csv). Go to Phase 1's CSV branch.
   • Numbers pasted inline as a list/table — treat as inline CSV. If they gave nothing (or just asked "can you check my dashboard?"), ask once, welcoming all three: "Show me the numbers you want to check against — easiest is a screenshot of your dashboard (Metabase, Power BI, Tableau, a spreadsheet — whatever you have), but a CSV or a pasted list of label: value works too." Don't make them figure out an export format.
5. If they gave a file path, validate it exists. If not, surface the error and stop.

Phase 1: Extract the (label, value) pairs

Whatever the input shape, the goal is the same normalized rows JSON. Number parsing is always deterministic — it goes through reconcile.py, never the LLM eyeballing a value (a misread expected number would manufacture a false mismatch on a verification surface).

Vision branch — a dashboard screenshot

1. Read the image and extract every labeled number you can see — KPI tiles, table cells, chart value labels — as (label, raw_value) pairs. Keep the label the user would recognize ("Total Revenue", "Active Users — Apr"), and the value exactly as shown, verbatim ($4.2M, ₹2.16Cr, 42%, 1,234) — don't convert it; the normalizer does that.
2. Write the pairs as a 2-column CSV (Write tool — never a heredoc/python3 -c) to /tmp/agami-reconcile-<ts>.csv, then run the SAME normalizer as the CSV branch (below) so value parsing stays deterministic.
3. Confirm before reconciling — vision can misread. Show the extracted pairs as a small table and ask the user to fix any misread label/number: "I read these N numbers off your screenshot — correct anything I got wrong, then I'll reconcile." This confirm step is mandatory: a wrong expected-value isn't a model bug but it reads like one. If a tile is ambiguous or partly cut off, say so and skip it rather than guess.

CSV branch — a CSV path or inline-pasted CSV

python3 "$AGAMI_PLUGIN_ROOT/scripts/reconcile.py" parse --csv "<csv_path>" \
  > /tmp/agami-reconcile-rows-<ts>.json

The helper (used by both branches) handles:

• Header detection (with-or-without first-row column names)
• 2-column or 3+ column inputs (3rd onward are appended to the label as context)
• Currency symbols / magnitude suffixes / accounting parens / percent ($4.2M, ₹2.16Cr, (123.45), 42%)
• Null sentinels (n/a, —, blank)

Read the JSON. Each row is {label, expected_value, raw_value}. Discard rows where expected_value is null (unparseable) — surface a one-liner: "Skipped 2 rows where the value couldn't be parsed: 'X', 'Y'."

Surface to the user:

Parsed <N> rows from <the screenshot / csv_path>. Reconciling now — typically <N> × 5–15s per row depending on query latency.

Phase 2: Generate questions + execute

For each row in the parsed list:

2a — Generate the NL question

Use the LLM to translate label (+ context if present) into the most natural English question whose answer should be expected_value. Examples:

The semantic model + examples library are loaded; let the LLM pick the right subject areas / entities / metrics that resolve the labeled term to a concrete query.

2b — Run via the agami-query pipeline

Invoke the same SQL-generation + execution path agami-query uses (Phases 2 + 3 of that skill — see agami-query/SKILL.md). Capture:

• The generated SQL
• The result (should be a single scalar, or a single row)
• The full chart-template HTML report (so the user can drill in for mismatches)
• The trust receipt (with confidence, signed-off-by, etc.)

The SQL you capture here is the one that lands in the row record (Phase 2d) — keep it verbatim. It is the only place the statement survives the run: the chart report at report_path is HTML, and nothing re-derives the statement from it afterwards.

If the SQL fails OR the result isn't a single scalar (e.g., the LLM-generated question returned a multi-row table), capture an error: Could not extract a single scalar from the result. These rows show up as error status in the report.

2c — Diff

python3 "$AGAMI_PLUGIN_ROOT/scripts/reconcile.py" diff \
  --expected "<expected_value>" \
  --actual "<actual_value_from_query>" \
  --tolerance 0.01

Default tolerance: ±1%. The user can override with tolerance=N% in their original ask (e.g., "reconcile with 5% tolerance"). Tolerance applies to numeric comparisons; for text values (rare), use exact match.

Capture: match (bool), delta, delta_pct.

2d — Build the row record

Per row:

{
  "label":        "<from CSV>",
  "question":     "<LLM-generated NL question>",
  "expected":     <number>,
  "actual":       <number or null if errored>,
  "delta_pct":    <signed fraction or null>,
  "match":        true | false,
  "status":       "match" | "mismatch" | "error",
  "report_path":  "<artifacts_dir>/local/charts/<profile>/<ts>.html",  // the full chart report for this query
  "sql":          "<the statement that produced actual, or null on an error row>",
  "recorded":     {"columns": ["<column name>"], "rows": [[<value>]]},  // what the query actually returned
  "error":        "<message if status=error, else null>"
}

sql is the statement captured in Phase 2b, written down verbatim. recorded is the result it returned, shaped as columns + rows — the same two keys the golden-dataset receipt uses — so whoever picks this row up later forwards it as-is instead of rebuilding it from a number and guessing at a column name.

On a status: "error" row both sql and recorded are null. There is no statement to keep: either none was generated, or the one that was didn't produce a scalar anyone read. An error row therefore carries nothing a later reader could mistake for a verified answer.

Append all records to /tmp/agami-reconcile-results-<ts>.jsonl so the user can inspect later. The two keys are additive — a reader that only knows the older shape keeps working.

Phase 3: Present

3a — Summary line first

Reconciled <N> numbers: <M> match (within ±1%), <K> mismatch, <E> error.

3b — Mismatches table (lead with what didn't match)

Render the mismatches as a markdown table BEFORE the matches:

### Mismatches

| Label | Expected | Got | Δ | Drill-down |
|---|---:|---:|---:|---|
| Q3 2025 Revenue          | $4,200,000 | $3,890,000 | -7.4% | <artifacts_dir>/local/charts/&lt;profile&gt;/...html |
| Active customers (Apr)   | 12,450     | 11,920     | -4.3% | <artifacts_dir>/local/charts/&lt;profile&gt;/...html |

Cell formatting:

• Numbers carry the same currency / magnitude suffix as the input where unambiguous (echo the user's raw_value for Expected, format Got with the same shape).
• Δ is the signed percent (red wins emphasized in chat by ✗ prefix if Markdown rendering allows; otherwise plain text).
• Drill-down links to the chart-template HTML for that query. Open these to see the full receipt — that's where the definitional disagreement lives.

For each mismatch row, surface a one-line interpretation under the table:

Q3 2025 Revenue — agami reports $3.89M, your dashboard says $4.2M (-7.4%). Open the receipt; the metric revenue here is gross of refunds in USD at invoice date. If your dashboard nets refunds, that's the gap.

This is where the trust win lands. The DE doesn't have to chase the disagreement — the receipt + your interpretation does it for them.

3c — Errors block (if any)

### Errors

| Label | What went wrong |
|---|---|
| Pipeline value (open opps) | Could not extract a single scalar — the question returned 47 rows. Try rephrasing or check that the metric exists in the model. |

3d — Matches summary (last, compact)

### Matches (within ±1%)

7 numbers reproduced cleanly: Q3 2025 Orders, MoM growth, Avg order size, Top customer, Customer count by region, Refund rate, Pipeline count.

Don't dump every match's drill-down — they're not interesting. The matches build the case; the mismatches drive the conversation.

3e — Offer promotion

The rows that agreed are the most reusable thing this run produced: a question, the statement that answered it, and a number the user's own dashboard already vouches for. Nothing else in the product carries evidence from outside agami. So keep them — and keep them in both of the places they are worth keeping.

They are worth two different things, and one row cannot be both.

• A golden item tests the model: a later run tells you if that number drifts.
• A prompt example teaches it: questions like it get answered this way from now on.

A row written to both is a test grading its own study material — the example ranks first for its own question, the model reproduces it, and the item can only ever pass. So the batch is split rather than duplicated. They came from one dashboard, so they are the same family of question, which is exactly what makes holding some back meaningful.

You do the splitting, not the user. Which rows should teach depends on what the example library already covers, which is not something anybody can answer without reading it. sm examples --query already answers it, and its high_confidence flag is the product's own judgement of "we have a close example for this".

For each agreeing row, rank its question across the areas:

bash "$AGAMI_PLUGIN_ROOT/scripts/sm" examples "$ROOT" --area <area> --query "<the row's question>" --top-k 1

• high_confidence: false → the library does not cover this shape. Teach with it — it goes to the examples.
• high_confidence: true → already covered, so another example adds nothing. Test with it — it goes to the golden dataset.

Two adjustments, and both are about not splitting something too small to split:

• Fewer than four agreeing rows → all of them become golden items. A split of three leaves too little on either side to be worth the explanation.
• Never send more than half the batch to the examples. This is a cap, not a preference, and it is the rule that makes the split survive an empty library: with nothing curated, every row reads as novel and the base rule would teach with all of them, leaving the golden dataset empty. That is exactly the profile reconcile is aimed at, and an onboarding run that writes no test at all has failed at the thing it was asked to do. When the cap bites, keep the highest-scoring rows as examples and the rest as tests.

Make the offer once, here, after the summary. Never per row. A per-row prompt turns a twelve-number reconcile into twelve interruptions and buries the mismatches, which are the point of the run.

Say what goes where. This is not optional. Writing to the examples changes how agami answers future questions, and that must not happen silently behind a button that says "keep these numbers". One line, in what it does for them:

Ten of these agreed — I'll keep all ten. Four as examples, so questions like them get answered this way from now on. Six as tests, with a ±<the run's tolerance> band, so you're told if any of those numbers move. Save all ten?

Someone who presses enter without reading gets the right outcome; someone who reads it learns the distinction by watching it happen, which is the only way anybody will. The override is one line — "let me choose" — and not twelve prompts.

Only rows whose status is match are offered. That is the run's own tolerance — reconcile.diff decided it back in Phase 2c, and it is the only notion of agreement this skill has, so nothing here re-judges a number. One predicate drops mismatch and error together, and with them the missing_expected and missing_actual rows — those are reconcile.diff's own reasons rather than a row status, and a row that could not be diffed never reached match either. A row with no statement is never offered: an error row carries sql: null (Phase 2d), so there is nothing to replay and nothing worth promoting.

Only a single-cell result is offered. A row whose recorded carries more than one column has no single number to band, and a bounded item over a wider result is scored on its row count alone — it would pass forever without ever checking the number it was promoted for.

If no row agreed, make no offer at all — not an empty one, not "there's nothing to promote here". A run where nothing matched is having a different conversation (Phase 3b), and an offer with nothing in it interrupts it.

Every agreeing row starts selected. The person reviewed each row's agreement as it landed; asking them to opt each one back in asks the same question twice. They can deselect any row, and they can edit any question before it is written — a later run regenerates SQL from the question, so the wording is the item.

Show the statement and the result, not just the number. Per row: the question (editable), the statement that answered it, and the recorded result. What they are accepting is that this statement answers this question — that is what gets replayed — and a row accepted on its number alone is a row nobody checked the meaning of.

Declining writes nothing. No dataset is created, no file is touched, and the run ends exactly where Phase 3f leaves it. Say that when you ask, and say it again if they decline.

A question asked relative to today

The save door refuses a question asked over a window that slides when its statement is pinned to fixed dates — exit 2, nothing written. That is the common case here, not the exotic one: Phase 2a's own example turns Mean order size last 30 days into "What's the average order size over the last 30 days?", and the statement that answered it names the thirty days that were current when it ran.

Two things get past the refusal and only one of them is right:

• Ask which window the question means, and rewrite the question to name it. "'The last 30 days' was 2–31 August 2026 when this ran — save it as '…in August 2026'?" The statement stays exactly as it ran, the question finally names the window it always meant, and the item stays true for as long as it exists.
• Never re-anchor the statement to CURRENT_DATE. It clears the lint and it is a trap: the band was recorded around today's value of a window that slides, so next month the same question asks about different days, returns a different number, and fails against a band nobody moved. That is a false alarm on the one surface whose whole job is to be believed.

So the offer edits the question, with the person's answer to "which window?". It never edits the SQL.

Ask which window it means in the offer, before anything is written — not after the refusal lands. A batch is written one row at a time (below), so a refusal on the seventh row arrives with six items already on disk: items already written stay written, and nothing is rolled back. The cheat sheet's exit-2 row is the fallback for a question that slips through, not the plan.

What gets written, per row kept as a test

Write the item with the Write tool — never a heredoc, never python3 -c, per shared/invocation-conventions.md — to /tmp/agami-golden-item-<ts>.json:

{
  "query": "What was total revenue in Q3 2025?",
  "sql": "<the row's `sql`, verbatim>",
  "match": "bounded",
  "bounds": {"min_rows": 1, "max_rows": 1, "min_value": 3851100.0, "max_value": 3928900.0},
  "tags": ["reconciled"],
  "confirmed_by": {"method": "reconciled against the finance dashboard on 2026-08-31; agreed within ±<the run's tolerance>"}
}

• id is omitted on purpose. The save door derives it from the question, exactly as the import door does, so a promotion lands on an already-imported question of the same wording rather than beside it as a second copy.

• sql comes from the row record (Phase 2d) as it stands, verbatim. recorded is omitted — the save door stamps it itself and never takes it from this payload, because the golden-datasets directory has no gitignore exclusion and this row's recorded is the finance dashboard's own numbers.

• match: "bounded" because a reconciled number is one that legitimately moves. bounded with no band is refused (exit 2), which is why the band below is not optional.

• bounds comes from the helper, never from arithmetic written in prose:

  python3 "$AGAMI_PLUGIN_ROOT/scripts/reconcile.py" band \
    --value "<the row's actual>" --tolerance <the run's tolerance>
  

  Pass the same tolerance the run diffed with. The four keys it prints are the bounds block — paste them in unchanged.

Then call the save door. It is the only writer of a golden dataset anywhere in the plugin, and this skill does not become a second one:

python3 "$AGAMI_PLUGIN_ROOT/scripts/golden_author.py" save \
  --profile <profile> --dataset <stem> \
  --item /tmp/agami-golden-item-<ts>.json \
  > /tmp/agami-golden-save-<ts>.json

<stem> is the dataset's filename stem — one plain name (reconciled), never a path and never reconciled.yaml. Ask which dataset once, for the whole batch.

One call per kept row. The door takes one item, not an array — ten kept rows are ten runs of that command, each with its own item file. The dataset is asked once; the writing is a loop.

Pass --confirm-convention on a promoted row. The save door checks a statement against the profile's own examples and stops when they disagree, which is right when somebody is authoring a key by hand. Here it is redundant and wrong: the statement being saved is the one agami itself generated from those examples minutes ago, and a stop would ask the person to confirm a convention they never departed from.

What gets written, per row kept as an example

Write the pair with the Write tool to /tmp/agami-reconciled-example-<ts>.json — a JSON array, one entry per row going to the examples:

[{"question": "What was total revenue in Q3 2025?", "sql": "<the row's `sql`, verbatim>",
  "tables": ["<from the row's statement>"], "source": "reconcile", "status": "confirmed",
  "created_at": "<ISO8601 UTC>"}]

Then the packaged writer, which dedups by question and creates the library if it is absent:

bash "$AGAMI_PLUGIN_ROOT/scripts/sm" add-example "$ROOT" --area <area> --file /tmp/agami-reconciled-example-<ts>.json

<area> is the subject area whose tables the statement reads — the same choice agami-save-correction makes, and the same writer. Do not hand-edit examples.yaml, and do not invent a second way in.

status: "confirmed" is honest here in a way it rarely is: the number was verified against a source outside agami. That is stronger evidence than the recollection behind most curated examples.

confirmed_by.method — two shapes, kept apart

The method line is what somebody consults a year later to find out where this item's authority came from, so the two ways a number reaches a dataset from here read differently on purpose. Each names the source, the date and the tolerance:

• Agreed — the row matched and the person accepted the offer:

  reconciled against <source> on <date>; agreed within ±<tolerance>

• Resolved — the row did not match, and the person judged agami right anyway:

  reconciled against <source> on <date>; disagreed beyond ±<tolerance>, resolved in agami's favour by the analyst

<source> is what the numbers came from, as the user described it ("the finance dashboard", "the Q3 export"); <date> is the day the run happened.

The resolution path is not part of the offer. A disagreeing row is not offered and cannot be written by accepting the offer — the selection has no room for one. If the person opens a mismatch, decides their dashboard is wrong and agami is right, they have to say so explicitly, as its own step, and it writes with the resolved method above. That friction is deliberate: promoting a mismatch writes an answer key that contradicts the number the team currently believes, and that should cost a sentence rather than a checkbox.

If the item already exists

Read which payload the 1 carries before you answer it — there are two, and the wrong flag clears neither. A needs_confirmation_convention payload means the statement departs from the profile's own curated examples; on a promotion that is expected and not interesting, because the statement being saved is the one agami generated from those examples minutes ago. Pass --confirm-convention for it. Everything below is the other payload.

Exit 1, a needs_confirmation payload, and nothing written. Render the before AND the after for every id — the item on disk and the one that would take its place — ask, and only on an explicit yes re-run the same command with --confirm-replace appended. Never pass --confirm-replace pre-emptively: the flag means one thing, that a person saw both sides and said yes, and passing it before that has happened makes the stop decorative.

A replacement is wholesale — the item sent is the item written — so read the before and carry forward whatever it holds that still applies (its tags, a must_filter) into the item JSON before you re-run. On a no, say the file is untouched and stop.

3f — Closing prompt

Re-run with `tolerance=5%` to see softer matches, or open any drill-down to find the definitional gap.

End the turn. The user typically:

• Opens a mismatch's drill-down, finds the definitional gap, says "the dashboard is gross-of-refunds; can we update the metric?" — chain into /agami-save-correction to update the metric definition.
• Asks tolerance=5% to widen the matches.
• Asks for a different CSV.
• Takes the promotion offer from Phase 3e, and the rows that agreed become a golden dataset later runs are scored against.

Hard rules

1. No automatic question generation for ambiguous labels. If the label is too short or too vague (e.g., Total, Number, Value), surface to the user: "Row 5's label is just 'Total' — too ambiguous to translate to a question. Skipping. Add more context to the CSV (e.g., Total Revenue Q3 instead of Total) and re-run." Don't guess.
2. Receipt is non-optional. Every per-row run MUST produce a chart-template HTML report with the trust receipt — that's what the drill-down link points at, and it's what makes mismatches actionable. If the underlying query path can't produce a receipt (legacy pre-trust-layer model), refuse with: "This profile pre-dates the trust-layer launch. Re-run /agami-connect to enable receipts, then retry."
3. Don't write to the semantic model from this skill. Reconcile reads + diffs; it never mutates a metric, a join, a column or any other part of the model. If a definitional disagreement surfaces and the user wants to update the metric, route them through /agami-save-correction. The writes this skill can make are Phase 3e's, and neither is a model write: a golden dataset is the answer key that tests the model, and the prompt-example library is what the model reads — neither is the model's own definitions, and this skill still never touches a metric, a join, a column or a default filter. Both writes go through the packaged writers — golden_author.py save and sm add-example, those doors and nothing else, never a hand-edited YAML — and both need the person's yes in front of them. A row reaches either one by exactly two routes: this run scored it as agreeing, or it scored a mismatch that the person has explicitly resolved in agami's favour. No row goes to both, which is the whole reason Phase 3e splits the batch rather than duplicating it.
4. CSV stays local. Don't upload, don't summarize-and-send. The reconcile run produces local artifacts (the per-query chart HTML, and /tmp/agami-reconcile-results-*.jsonl, which now carries the statement behind every row as well as its numbers) and nothing leaves the machine. A promoted row stays local too: the save door writes into the profile's own golden_datasets/ directory on this machine.

Error handling cheat sheet

Hard rule for screenshots

The screenshot is an image of numbers, and a misread expected value reads exactly like a model bug. So: (1) the value is parsed by reconcile.py, never by eyeballing; (2) the extracted (label, value) table is always confirmed with the user before any query runs (Phase 1 vision branch). The image stays local — same as the CSV (Hard rule #4); it's never uploaded or summarized off-machine.

Roadmap (not in v1)

• Tableau / Looker / Mode export parsing — parse .twb / .twbx / JSON exports directly (today a screenshot of any of them already works via the vision branch).
• Recurring reconcile runs — re-run a reconcile against a pinned dashboard on a schedule, rather than by asking each time.