aqwa-batch-execution-detecting-success-vs-failure
Sub-skill of aqwa-batch-execution: Detecting Success vs. Failure (+2).
Best use case
aqwa-batch-execution-detecting-success-vs-failure is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of aqwa-batch-execution: Detecting Success vs. Failure (+2).
Teams using aqwa-batch-execution-detecting-success-vs-failure should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/detecting-success-vs-failure/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How aqwa-batch-execution-detecting-success-vs-failure Compares
| Feature / Agent | aqwa-batch-execution-detecting-success-vs-failure | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Sub-skill of aqwa-batch-execution: Detecting Success vs. Failure (+2).
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Detecting Success vs. Failure (+2)
## Detecting Success vs. Failure
```bash
# 1. Check .MES first (most direct failure indicator)
grep -qi "error\|fatal\|abort" analysis.mes && echo "FAILED"
# 2. Check for .RES (produced only on successful Stage 1 completion)
[ -f analysis.res ] || echo "NO RESTART FILE — run failed"
# 3. Check .LIS for completion marker (exact string is version-dependent)
grep -i "analysis complete\|normal termination" analysis.lis
# 4. Count panels processed (sanity check)
grep "TOTAL NUMBER OF PANELS" analysis.lis
```
## Parsing RAOs from `.LIS` (Python)
```python
import re
from pathlib import Path
def check_lis_success(lis_path: Path) -> bool:
text = lis_path.read_text(errors="replace")
if re.search(r"FATAL ERROR|ERROR DETECTED", text, re.IGNORECASE):
return False
if re.search(r"ANALYSIS COMPLETE|NORMAL TERMINATION", text, re.IGNORECASE):
return True
return False # inconclusive
def parse_rao_block(lis_path: Path) -> list[dict]:
"""Extract RAO amplitude/phase from AQWA-LINE .LIS (fixed-column format)."""
text = lis_path.read_text(errors="replace")
# First RAO section = displacement RAOs; skip velocity/acceleration
blocks = re.findall(
r"WAVE FREQUENCY\s*=\s*([\d.]+)(.*?)(?=WAVE FREQUENCY|\Z)",
text, re.DOTALL
)
results = []
for freq_str, block in blocks:
rows = re.findall(
r"([\d.]+)\s+" + r"([\d.]+)\s+([-\d.]+)\s+" * 6,
block
)
results.append({"freq_rad_s": float(freq_str), "rows": rows})
return results
```
## AqwaReader Batch Export
AqwaReader extracts results to CSV without the GUI. On Windows it must run via `workbench.bat`:
```bat
rem Windows — must use workbench.bat wrapper
"C:\Program Files\ANSYS Inc\v251\aisol\workbench.bat" -cmd ^
"C:\Program Files\ANSYS Inc\v251\aisol\bin\winx64\AqwaReader.exe" ^
--Type Graphical ^
--InFile analysis.plt ^
--OutFile results\rao ^
--Format csv ^
--Struct 1 --Freq 1 --Dir 1 ^
--PLT1 1 --PLT2 1 --PLT3 1 --PLT4 3
```
On Linux, run AqwaReader without the wrapper (path follows same `lnx64` convention).
After any interactive AqwaReader session, it prints the exact command-line used — copy
this into your script and loop over `--Freq` and `--Dir` indices.Related Skills
digitalmodel-worktree-test-execution-with-shared-venv
Run digitalmodel tests from isolated worktrees without uv editable-dependency failures by using the main repo's existing virtualenv and PYTHONPATH.
plan-gated-issue-execution-wave
Execute a multi-issue architecture/planning wave in a plan-gated repo, then safely transition approved issues into implementation with file-based Codex prompts, local approval markers, subprocess monitoring, and cleanup handling for sandbox/hook edge cases.
work-around-merge-conflicts-in-test-execution
Run tests when repo has unresolved merge conflicts in config files by bypassing broken configs and executing tests directly
wave-based-parallel-plan-execution
Orchestrate phase execution by discovering dependencies, grouping into waves, spawning subagents, and collecting results with optional wave filtering
batch-syntax-repair-from-injection-errors
Detect and fix systematic syntax errors caused by line-injection scripts that split multiline constructs
batch-syntax-fix-with-regex-line-based-fallback
Fix repeated syntax errors across many files using regex, then fall back to line-based parsing when regex fails
batch-syntax-fix-regex-iteration
Iteratively fix widespread syntax errors across many files using regex refinement when initial patterns fail
batch-syntax-fix-pattern
Identify and repair cascading import/syntax errors across multiple files using regex-based line-scanning and verification
batch-regex-fix-import-syntax
Detect and fix mid-import blank-line syntax breaks across multiple files using line-based regex
plan-governance-vs-execution-boundary-for-adversarial-review
Keep stale-approval/governance remediation out of execution-path pseudocode, TDD, files-to-change, and deliverable acceptance when hardening a GitHub issue plan under adversarial review.
large-parallel-planning-wave-environment-failure-handoff
Handle large pre-plan-review planning waves that succeed analytically but fail to persist artifacts due to quota exhaustion, sandbox write failures, or cancelled GitHub mutations.
interactive-issue-execution-worktree-guardrails
Execute approved GitHub issues in isolated worktrees with interactive Codex/Codex runs, while containing agent drift and salvaging progress when provider/runtime problems occur.