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Grounded Numbers: AI summaries with no made-up figures
Language models choose words well and do arithmetic badly, and a wrong number in a report costs more than a dull sentence.
$9.99
Grounded Numbers: AI summaries with no made-up figures
Example session with this skill installed
We run a small online shop, Juniper & Vale. Add a monthly sales summary to our admin dashboard,
written by Claude. The numbers have to be right: nothing Claude makes up should ever reach the
dashboard. Please include tests.
- Read your context and instructions
- Compiled the grounded numbers
This is the hand-over for the skill's worked example; the run output in it is the example's real
output. Paste everything inside the block below (not the four-backtick lines) into the Result
field.
**Summary**
I added a monthly sales summary that Claude writes and code checks. Every number is computed from
your orders in code; Claude only writes the sentences and cites the facts each one uses; a verifier
checks every figure before anything reaches the dashboard. If Claude is slow, fails, or still gets a
number wrong after two rewrites, a summary built from the same facts is shown instead. 94 tests
pass, and none of them needs an API key.
**What I built**
| File | What it does |
| --- | --- |
| `evidence.py` | Computes October's facts from orders placed up to 31 October, so later orders never change it |
| `claims.py` | Reads each number and date Claude writes, as written: `$513`, `$0.5k`, `-10.0 points`, `14 October` |
| `verifier.py` | Checks every line against the facts it cites: value, unit, citation, "up" or "down" |
| `fallback.py` | The summary shown when Claude cannot be used, built from the same facts |
| `pipeline.py` | Draft, check, at most two rewrites, a 20-second deadline per call, a final check before publishing |
| `models.py` | The model interface, plus a fake model for tests |
| 6 test files | 94 tests: planted errors, rewrites, timeouts, templates, later orders |
**A run with a planted mistake.** The first draft said revenue was $521.75 (it is $512.75) and wrote
a 10-point fall as a percent. Both were caught and sent back, and the rewrite was published:
```text
draft: problems - headline: '$521.75' is not in the evidence; the evidence says $512.75; sentences[2]: '10.0%' is written as percent but returning_share_change is in points; the evidence says -10.0 points
rewrite: ok
```
Published headline: "October 2026: revenue up 89% to $512.75".
**When Claude cannot be used**, the dashboard shows:
```text
Juniper & Vale, October 2026: revenue $512.75
Revenue was $512.75, up 89.2% on September 2026 ($271.00).
There were 10 orders, at an average of $51.28.
The best day was 14 October 2026, with $179.00 in sales.
**Desk lamp sold the most:** 4 units for $256.00.
Returning customers placed 40.0% of orders, 10.0 points lower than in September 2026.
```
**Next steps**
1. Point `build_evidence` at your orders table and check its facts against last month's books.
2. Connect Claude through the model interface (an adapter is in the skill's
`references/model-calls.md`), keeping the SDK's timeout inside the 20-second deadline.
3. Run a few real months and read the summaries before turning the feature on for everyone.
4. Decide on the extras: a second-model check for meaning, and storing each summary's trace.
Connects securely to your tools. The creator never sees your data.
About this skill
Language models choose words well and do arithmetic badly, and a wrong number in a report costs more than a dull sentence. This skill teaches your agent to split the work: code computes every number, the model only explains them, and code checks every number the model writes before it reaches a reader. When the model cannot produce a grounded draft, a template built from the same numbers is published instead.
How it works. The agent follows a workflow in SKILL.md:
- List every figure the feature shows, with its formula, source, unit and precision.
- Build an evidence pack in code for a period that has ended, so a rerun gives the same numbers.
- Ask the model for JSON in which every line cites the facts it uses.
- Check every number and date in code: rounding as written (
$513passes for 512.75,$521.75does not), units (percent is not percentage points), citations and direction words. - Send problems back for at most two rewrites, with a deadline on every model call.
- Publish a template built from the same facts on a timeout, an error or a draft that still fails.
- Check again just before publishing, and test it all with a fake model.
It builds a small core by default and adds the rest (date checks, a second-model meaning check, traces) when the request or the stakes call for it.
Where it stops. The checks prove where each number came from, not what a sentence means: a line can cite the right number for the wrong claim. An optional second-model check and human review cover meaning. Numbers written as words are not checked; the model is asked for digits.
What's included. SKILL.md; five references (evidence packs, the verifier, fallbacks, model
calls with a Claude API adapter, testing); and a worked example, a monthly sales summary for a
fictional shop in Python 3.12 with the standard library only, with 94 tests.
Does not check numbers written as words. Cannot verify semantic meaning without optional step 6. Requires exact matches for whole numbers. Verification is limited to computed facts in the pack.
How to use
- 1
Connect your agent to the Agensi MCP
Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup
- 2
Sign in to Agensi
Your agent shows a sign-in link. Sign in and click Allow.
- 3
Ask for what you need
Your agent loads this skill and uses it on your task. It always asks before any payment.
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The skill adds code and tests to your project and runs the tests. It makes no network calls and needs no API key itself; the summaries it builds call your model provider at run time, with its usual credentials.