Sales Call Recording Consent Metadata
Check your supplied consent policy and recording metadata for missing supporting records.
Sales operations
US$11View skill
Sales Forecast Date Contradiction
Find where your forecast fields and customer emails disagree.
A downloadable skill for your AI agent.
Your result
Conflicting expected-close dates.
Source references, missing information, and questions to review stay with the findings.
How to use it
Add the skill to your AI
Open your download and follow the start guide. Use an AI agent that can load skill files and run the included tools.
Provide your records
Forecast fields and customer emails. Confirm the case and period you want to check.
Ask for the report
Copy the request below into your agent. Add the files and any task details it needs.
Review and save
Check the findings against the original records. Choose where to save the report and follow up on open questions.
Example request
Use the Sales Forecast Date Contradiction skill. Review the attached records: forecast fields and customer emails. Prepare this result: conflicting expected-close dates. Include a source reference for each finding. List missing information and questions that need my review. Ask me to confirm the scope and where to save the report before you start.
What it checks
It puts records for the same item side by side. It keeps both original values when they differ, so you can check the source.
What you provide
Forecast fields and customer emails.
Details it records
- Forecast date
- Customer statement
- Source date
Read the full task logic
For this task, keep one review row for each opportunity + close date field. Record forecast date, customer statement, and source date. Keep the source location beside each value.
Separate identity evidence, stage claims, customer commitments, approved discounts, and executed contracts. Preserve conflicts between records.
Do not merge contacts on names alone. Use sourced commercial rules and treat forecasting judgments as review items.
A changed value does not show which source is correct. Semantic equivalence and identity aliases need an explicit mapping and review.
Example result
This synthetic example shows the included check in use. Your agent uses its findings to complete your task report.
What you provide
Synthetic example records
| Starting record | forecast date: source-A |
|---|---|
| Record to check | forecast date: source-B |
What comes back
A difference needs review
- The two records give different values for the same item.
This result comes from the checking tool in your download.
Read the source records
These are synthetic records from the bundled example.
- source-e1 · expected/e1
{"id": "e1", "key": "opportunity + close date field / SYNTHETIC-001", "value": {"forecast date": "source-A"}} - source-o1 · observed/o1
{"id": "o1", "key": "opportunity + close date field / SYNTHETIC-001", "value": {"forecast date": "source-B"}}
Checked values
- expected: forecast date: source-A; observed: forecast date: source-B
Run this example
After purchase, extract the ZIP. Run these commands from the skill folder. Use a new output folder.
python3 scripts/checks.py
python3 scripts/audit.py assets/example.json \
--files-root assets/sample-files --output-dir example-resultExpected exit code: 1. Code 0 means no helper finding. Code 1 means a finding or review item. Code 2 means incomplete or invalid input.
The check uses the records you supply. Review each finding against the original documents.
What is saved
Ask your agent to save the completed task report in a folder you choose. The report template includes the result, source references, missing information, and next actions.
- A readable check report
- The local checking tool can save its findings as report.md.
- A data copy
- The same check can be saved as report.json for use in other tools. A local audit database is optional.
Tonotes does not receive your work documents through this website. Documents you share with an AI service follow that service's storage settings.
Your download
US$11 includes one skill in a ZIP with 18 files. You receive the instructions and tools to run the task with your own records.
- A start guide to help you set up the skill.
- Task instructions that tell your AI what to check.
- Checking tools with worked examples.
- A report template with space for evidence and next actions.
Setup and file details
Use an agent that supports SKILL.md instructions and Python 3.10 or later with SQLite. Your AI-agent service is separate.
- Start guide: 1 file
- START-HERE.md explains setup and the expected example result.
- Agent instructions: 1 file
- SKILL.md defines this task and its completion checks.
- Task references: 4 files
- The task guide, input contract, tools guide, and research notes.
- Python tools: 4 files
- Check records, convert CSV input, and run the local checks.
- Examples and templates: 8 files
- The input schema, synthetic cases, expected report, and report template.
Collect the records
Start with forecast fields and customer emails. Confirm the case, period, and source versions before the review.
Extract the task fields
Use account legal entity, contact, opportunity, quote version to keep records distinct. The agent records the original value, an exact quote, and the source location.
Compare source values
Match each item, then compare its supplied values. Run the Python helper on the structured records. Check each finding against its source.
Complete the report
Prepare the task result: conflicting expected-close dates. Include missing evidence, unresolved questions, and the next action for each open item.
Eight synthetic cases check the tool locally. Live-agent accuracy on real documents has not been tested.