Borrowing it
Nothing to install: this file belongs to gpt-cmdr/ras-commander. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/gpt-cmdr/ras-commander/main/.claude/skills/hecras_parse_compute-messages/SKILL.mdgit clone --depth 1 https://github.com/gpt-cmdr/ras-commanderWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/gpt-cmdr/ras-commander/hecras_parse_compute-messages)<a href="https://agentmods.dev/skills/gpt-cmdr/ras-commander/hecras_parse_compute-messages"><img src="https://agentmods.dev/badge/skills/gpt-cmdr/ras-commander/hecras_parse_compute-messages/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/gpt-cmdr/ras-commander/hecras_parse_compute-messages"><img src="https://agentmods.dev/badge/skills/gpt-cmdr/ras-commander/hecras_parse_compute-messages.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00114 | $0.02873 |
| Opus 5 | $0.00057 | $0.01437 |
| Sonnet 5 | $0.00023 | $0.00575 |
| Haiku 4.5 | $0.00011 | $0.00287 |
Grade A, and why
hecras_parse_compute-messages scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 423 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Parsing HEC-RAS Compute Messages
Primary Sources (navigate to these for complete details):
- HDF Class Reference:
ras_commander/hdf/AGENTS.md- Class hierarchy, decorators - HdfResultsPlan Implementation:
ras_commander/hdf/HdfResultsPlan.py- Compute message methods - Working Example:
examples/400_1d_hdf_data_extraction.ipynb- Compute message extraction
When the user asks about execution status or compute messages, use these patterns. Read the primary sources above for implementation details.
Quick Start
Check Plan Completion and Extract Messages
from ras_commander import init_ras_project, HdfResultsPlan
# Initialize project
init_ras_project("path/to/project", "7.0")
# Extract compute messages (handles HDF + .txt fallback automatically)
messages = HdfResultsPlan.get_compute_messages("01")
# Check if plan has results (runtime data exists only for completed plans)
runtime = HdfResultsPlan.get_runtime_data("01")
is_complete = runtime is not None
if is_complete:
print(f"Plan completed in {runtime['Complete Process (hr)'].values[0]:.2f} hours")
else:
print("Plan has not been executed or did not complete")
API Reference
HdfResultsPlan.get_compute_messages()
Call this to extract raw computation messages from HDF files.
Signature:
@staticmethod
@log_call
@standardize_input(file_type='plan_hdf')
def get_compute_messages(hdf_path: Path) -> str
Parameters:
hdf_path: Plan HDF file path OR plan number string (e.g., "01")
Returns: String containing all computation messages, empty string if unavailable
Fallback Behavior:
- First attempts: HDF path
/Results/Summary/Compute Messages (text) - Fallback:
.txtfile via RasControl (for pre-6.x HEC-RAS)
Example:
messages = HdfResultsPlan.get_compute_messages("01")
print(len(messages)) # Character count
HdfResultsPlan.get_compute_messages_hdf_only()
Purpose: Extract compute messages WITHOUT RasControl/COM fallback
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 423 lines · 114 tokens per session scan A 73220428f9ec
hecras_parse_compute-messages is a skill published in the GitHub repository gpt-cmdr/ras-commander (79 stars, last pushed today), licensed MIT. It adds 114 tokens to every session and 2,873 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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