OpenSpace prioritize-context-data

Ensures agents check and use provided context files for data before attempting external searches.

install

source · Clone the upstream repo

git clone https://github.com/HKUDS/OpenSpace

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/HKUDS/OpenSpace "$T" && mkdir -p ~/.claude/skills && cp -r "$T/gdpval_bench/skills/prioritize-context-data" ~/.claude/skills/hkuds-openspace-prioritize-context-data && rm -rf "$T"

manifest: gdpval_bench/skills/prioritize-context-data/SKILL.md

source content

Prioritize Context Data

Objective

Prevent data hallucination and inefficiency by mandating that agents inspect and utilize provided reference files (CSV, XLSX, PDF, TXT, etc.) before attempting web searches or generating synthetic data.

Critical Rule

If a reference file is provided in the task context, it is the source of truth. Do not fabricate data or search the web for information that exists within the provided attachments.

Workflow Steps

1. Scan Context for Attachments

At the start of every task, explicitly list all files provided in the context window or attachment panel.

Check for spreadsheets (
```
.xlsx
```
,
```
.csv
```
), documents (
```
.pdf
```
,
```
.docx
```
), or data dumps (
```
.json
```
,
```
.txt
```
).
Note the filename and inferred content type.

2. Evaluate Relevance

Determine if any provided file contains the data required to complete the task.

Match Keywords: Do filenames or column headers match task requirements?
Check Scope: Does the data cover the necessary timeframe or地域 (region)?

3. Extract Data First

If relevant files are found:

Read the file content using appropriate tools (e.g.,
```
read_file
```
,
```
pandas
```
,
```
pdf_reader
```
).
Extract the specific data points needed.
Do not proceed to web search until you have confirmed the file lacks the necessary information.

4. Cite Source Explicitly

When presenting data in the final output:

Explicitly state which file the data came from.
Example: "According to
```
Massabama_active_listings.xlsx
```
..."
This verifies to the user that real data was used, not hallucinated.

5. Fallback to Search (Only if Necessary)

If the provided files do not contain the specific data needed:

State clearly what was missing from the files.
Then proceed with web search or estimation.
Mark any non-file data as "External Search" or "Estimated".

Checklist

Did I list all attached files?
Did I open and read the relevant files?
Did I verify the data exists in the files before searching?
Did I cite the file name in my output?
Did I avoid fabricating numbers that should have come from the file?

Example Usage

Task: Create a report on active property listings. Context:

Massabama_active_listings.xlsx

is attached.

Incorrect Approach:

Ignore the Excel file.
Search web for "Massabama property listings".
Fabricate numbers based on search snippets.
Result: Inaccurate data, ignored source of truth.

Correct Approach:

Identify
```
Massabama_active_listings.xlsx
```
in context.
Load the Excel file.
Count rows and sum prices directly from the sheet.
Output: "Based on the 50 entries in
```
Massabama_active_listings.xlsx
```
, the total value is..."
Result: Accurate data, verified source.

Warnings

Hallucination Risk: Ignoring provided files is a primary cause of data fabrication.
Efficiency: Reading a local file is faster and more reliable than web scraping.
User Expectation: Users attach files expecting them to be used. Ignoring them is a failure of instruction following.