Backward Traceability

Input

• $0

  — Paper project directory containing code and LaTeX files

References

• Traceability patterns and LaTeX commands:

  ~/.claude/skills/backward-traceability/references/traceability-patterns.md

Scripts

Scan hypertarget/hyperlink references

python ~/.claude/skills/backward-traceability/scripts/ref_numeric_values.py \
  --scan paper/main.tex --output report.json

Verify cross-reference integrity

python ~/.claude/skills/backward-traceability/scripts/ref_numeric_values.py \
  --verify paper/main.tex --code-output results.txt

Workflow

Step 1: Tag Code Outputs

# In experiment code output:
print(f"\\hypertarget{{R1a}}{{45.3}}")  # Mean accuracy
print(f"\\hypertarget{{R1b}}{{2.1}}")   # Std deviation

{prefix}{line_number}{letter}

Step 2: Reference in Paper Text

\hyperlink

Our method achieves \hyperlink{R1a}{45.3}\% accuracy
($\pm$\hyperlink{R1b}{2.1}).

Step 3: Use \num for Computed Values

\num{}

% \num{formula, "explanation"} → evaluated at compile time
The improvement is \num{45.3 - 38.7, "accuracy gain"}\%.

Step 4: Generate Appendix Code Listing

\hypertarget

\section*{Appendix: Code Listing}
\begin{lstlisting}[escapechar=@]
@\hypertarget{code1}{}@result = model.evaluate(test_data)
@\hypertarget{code2}{}@accuracy = result['accuracy']
\end{lstlisting}

Step 5: Verify Traceability

• Every number in the paper text must have a corresponding

  \hypertarget

  in the code
• Every

  \num{}

  formula must evaluate correctly
• Click-test: every hyperlink in the PDF must jump to the correct code line

LaTeX Setup

\usepackage{hyperref}
\usepackage{listings}

Rules

• Every numeric result in the paper MUST trace to code output
• Never manually type numbers — always reference tagged outputs
• Use

  \num{}

  for any derived/computed values
• Code listing in appendix must match actual executed code
• Verify all hyperlinks resolve correctly after compilation

Related Skills

• Upstream: experiment-code, data-analysis
• Downstream: paper-compilation
• See also: paper-assembly