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swe-devops-standards

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Price unconfirmed★ 22 GitHub starsRegistry updated · Sep 15, 2026agent-skill

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Geospatial SWE & DevOps Standards

Purpose: code produced as part of geospatial work should run in the user's real environment and meet peer-level engineering quality. Apply these rules only when code or repository artifacts are in scope.

1. Environment realities (the top error source)

Script-first by default: no %matplotlib inline, !pip install, or display() unless the user is explicitly in a notebook. Every file runs from a terminal via python script.py behind an if __name__ == "__main__": block. (Cell markers like # %% are fine as an addition — the script must also work without them.)
Cross-platform paths: always pathlib.Path; never string-concatenate or hardcode / or \\. Ask or detect the user's OS before giving shell commands; give CMD/PowerShell syntax on Windows, POSIX elsewhere — don't mix (export vs set, venv/bin/activate vs venv\Scripts\activate).
Encodings: explicit encoding="utf-8" on every text file open — Windows still defaults to legacy code pages, and non-ASCII content corrupts silently.
Modern Python (3.11+): X | None unions, type aliases, structural pattern matching where they clarify; state the minimum version if a feature requires it.

2. Code quality defaults

Applied to every generated function/module, even when not asked:

def compute_share(values: list[float], total: float) -> list[float]:
    """Return each value's share of the total.

    Args:
        values: Values to compute shares for.
        total: Denominator; must be non-zero.

    Returns:
        Shares in the same order as values.

    Raises:
        ValueError: If total is zero.
    """
    if total == 0:
        raise ValueError("total must be non-zero — share is undefined.")
    return [v / total for v in values]

Type hints on every signature; dataclass/TypeAlias for complex types.
Google-style docstrings; one-liners suffice for trivial functions.
Never bare except:; catch specific exceptions, handle or re-raise with raise ... from e. A silent pass costs a week of debugging.
logging over print (leveled, formatted), except user-facing CLI output.
Note algorithmic complexity where it matters ("this is O(n log n), safe at n>10⁶") — especially around nested loops and pandas apply.
Magic numbers → named module-level constants.

3. Testing and verification

Offer at least a skeleton pytest for every function carrying real logic:

# test_compute.py — run: python -m pytest -q
import pytest
from compute import compute_share

def test_basic() -> None:
    assert compute_share([1, 1], 2) == [0.5, 0.5]

def test_zero_total_raises() -> None:
    with pytest.raises(ValueError):
        compute_share([1.0], 0)

Numerical code: test edge cases — empty input, NaN, negatives, single element.
Run generated code yourself when an execution environment exists; otherwise mark it explicitly "not executed" — no silent assumptions.

4. Dependencies and reproducibility

New project → virtual environment + pinned requirements.txt (package==version); never "install the latest".
Seed randomness and put the seed in config (details in ml-experiment-standards).
Note environment-difference risks where relevant (BLAS, CUDA, locale).

5. Git practices

Conventional Commits: feat(scope): ..., fix: ..., refactor: ...; the body explains why — the diff already shows what.
Commit in meaningful units; warn against 500-line single commits.
Default .gitignore: venv/, __pycache__/, *.pyc, large data files (suggest DVC/LFS), IDE folders.

6. Automation / DevOps

CI: minimal GitHub Actions for test + lint (ruff); note OS-runner differences if jobs must run on Windows too.
Docker: start from python:3.12-slim, simple single-stage until size/caching demands more; note image size and build-cache implications.
Monitoring: any long-lived service/pipeline ships three signals minimum: structured logs, failure alerting, basic metrics (duration, volume). ML services add drift checks (see ml-experiment-standards).
Scheduled jobs: match the user's platform — cron on POSIX, Task Scheduler (schtasks) on Windows.

7. Code review mode

Review in this order and report findings by severity: correctness (edge cases, silent failures) → security (injection, secrets, path traversal) → performance (N+1, needless copies, O(n²)) → readability. Every finding ships with the suggested fix as code — never "this is bad" and nothing else.

Execution contract

Workflow: clarify the geospatial code's contract; reproduce the environment; inspect correctness and data invariants; implement the smallest safe change; test; package; document operations and rollback.
Decision rules: apply this skill to geospatial software and pipeline delivery, not generic non-spatial coding; scale CI, containers, and observability to the actual deployment risk.
Verification protocol: run focused and regression tests, lint and type checks where configured, exercise CRS/nodata/geometry edge cases, verify clean installation, and review CI artifacts.
Failure modes: block release for silent data loss, nondeterminism, mutable hidden state, unpinned critical dependencies, secrets, platform assumptions, missing rollback, or unhandled spatial edge cases.
Deliverables: reviewed code, tests, reproducible environment and lock data, CI configuration, operational notes, risk-ranked findings, observability plan, and rollback instructions.
Source freshness: consult the authoritative source registry before applying packaging, CI, testing, or supply-chain guidance.

View source ↗

File metadata

name: swe-devops-standards
description: >-
  Always invoke to review, repair, or deliver geospatial or GeoAI code,
  including contract compliance, security, error handling, transactions,
  tests, scripts, functions, notebooks, packages, CI/CD, and repository
  changes, even when deployment is not requested. Pair with the domain skill
  for ETL and other production code. Covers CRS/data invariants, dependencies,
  cross-platform reproducibility, automation, and shipping. Do not trigger for
  unrelated software or analysis requesting no code or repository artifact.
license: MIT
metadata:
  author: Muhammed Enes Duran

View original text

---
name: swe-devops-standards
description: >-
  Always invoke to review, repair, or deliver geospatial or GeoAI code,
  including contract compliance, security, error handling, transactions,
  tests, scripts, functions, notebooks, packages, CI/CD, and repository
  changes, even when deployment is not requested. Pair with the domain skill
  for ETL and other production code. Covers CRS/data invariants, dependencies,
  cross-platform reproducibility, automation, and shipping. Do not trigger for
  unrelated software or analysis requesting no code or repository artifact.
license: MIT
metadata:
  author: Muhammed Enes Duran
---

# Geospatial SWE & DevOps Standards

Purpose: code produced as part of geospatial work should run in the user's
real environment and meet peer-level engineering quality. Apply these rules
only when code or repository artifacts are in scope.

## 1. Environment realities (the top error source)

- **Script-first by default**: no `%matplotlib inline`, `!pip install`, or
  `display()` unless the user is explicitly in a notebook. Every file runs
  from a terminal via `python script.py` behind an
  `if __name__ == "__main__":` block. (Cell markers like `# %%` are fine
  as an addition — the script must also work without them.)
- **Cross-platform paths**: always `pathlib.Path`; never string-concatenate
  or hardcode `/` or `\\`. Ask or detect the user's OS before giving shell
  commands; give CMD/PowerShell syntax on Windows, POSIX elsewhere —
  don't mix (`export` vs `set`, `venv/bin/activate` vs
  `venv\Scripts\activate`).
- **Encodings**: explicit `encoding="utf-8"` on every text file open —
  Windows still defaults to legacy code pages, and non-ASCII content
  corrupts silently.
- **Modern Python (3.11+)**: `X | None` unions, `type` aliases, structural
  pattern matching where they clarify; state the minimum version if a
  feature requires it.

## 2. Code quality defaults

Applied to every generated function/module, even when not asked:

```python
def compute_share(values: list[float], total: float) -> list[float]:
    """Return each value's share of the total.

    Args:
        values: Values to compute shares for.
        total: Denominator; must be non-zero.

    Returns:
        Shares in the same order as values.

    Raises:
        ValueError: If total is zero.
    """
    if total == 0:
        raise ValueError("total must be non-zero — share is undefined.")
    return [v / total for v in values]
```

- Type hints on every signature; `dataclass`/`TypeAlias` for complex types.
- Google-style docstrings; one-liners suffice for trivial functions.
- **Never bare `except:`**; catch specific exceptions, handle or re-raise
  with `raise ... from e`. A silent `pass` costs a week of debugging.
- `logging` over `print` (leveled, formatted), except user-facing CLI
  output.
- Note algorithmic complexity where it matters ("this is O(n log n), safe
  at n>10⁶") — especially around nested loops and pandas `apply`.
- Magic numbers → named module-level constants.

## 3. Testing and verification

- Offer at least a skeleton pytest for every function carrying real logic:

```python
# test_compute.py — run: python -m pytest -q
import pytest
from compute import compute_share

def test_basic() -> None:
    assert compute_share([1, 1], 2) == [0.5, 0.5]

def test_zero_total_raises() -> None:
    with pytest.raises(ValueError):
        compute_share([1.0], 0)
```

- Numerical code: test edge cases — empty input, NaN, negatives, single
  element.
- Run generated code yourself when an execution environment exists;
  otherwise mark it explicitly "not executed" — no silent assumptions.

## 4. Dependencies and reproducibility

- New project → virtual environment + pinned `requirements.txt`
  (`package==version`); never "install the latest".
- Seed randomness and put the seed in config (details in
  `ml-experiment-standards`).
- Note environment-difference risks where relevant (BLAS, CUDA, locale).

## 5. Git practices

- Conventional Commits: `feat(scope): ...`, `fix: ...`, `refactor: ...`;
  the body explains *why* — the diff already shows *what*.
- Commit in meaningful units; warn against 500-line single commits.
- Default `.gitignore`: `venv/`, `__pycache__/`, `*.pyc`, large data files
  (suggest DVC/LFS), IDE folders.

## 6. Automation / DevOps

- **CI**: minimal GitHub Actions for test + lint (ruff); note OS-runner
  differences if jobs must run on Windows too.
- **Docker**: start from `python:3.12-slim`, simple single-stage until
  size/caching demands more; note image size and build-cache implications.
- **Monitoring**: any long-lived service/pipeline ships three signals
  minimum: structured logs, failure alerting, basic metrics (duration,
  volume). ML services add drift checks (see `ml-experiment-standards`).
- **Scheduled jobs**: match the user's platform — cron on POSIX,
  Task Scheduler (`schtasks`) on Windows.

## 7. Code review mode

Review in this order and report findings by severity: correctness (edge
cases, silent failures) → security (injection, secrets, path traversal) →
performance (N+1, needless copies, O(n²)) → readability. Every finding
ships with the suggested fix as code — never "this is bad" and nothing
else.

## Execution contract

- **Workflow:** clarify the geospatial code's contract; reproduce the environment; inspect correctness and data invariants; implement the smallest safe change; test; package; document operations and rollback.
- **Decision rules:** apply this skill to geospatial software and pipeline delivery, not generic non-spatial coding; scale CI, containers, and observability to the actual deployment risk.
- **Verification protocol:** run focused and regression tests, lint and type checks where configured, exercise CRS/nodata/geometry edge cases, verify clean installation, and review CI artifacts.
- **Failure modes:** block release for silent data loss, nondeterminism, mutable hidden state, unpinned critical dependencies, secrets, platform assumptions, missing rollback, or unhandled spatial edge cases.
- **Deliverables:** reviewed code, tests, reproducible environment and lock data, CI configuration, operational notes, risk-ranked findings, observability plan, and rollback instructions.
- **Source freshness:** consult [the authoritative source registry](references/authoritative-sources.md) before applying packaging, CI, testing, or supply-chain guidance.

Review the source

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License: MIT
Price unconfirmed: We have not confirmed a price for this skill. Existing source and install links remain available.

Free to get does not mean free to run. Price labels are not safety ratings. Submit pricing information →

Skill source recorded

Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.

Review before install: Avoid automatic install

License: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
Low GitHub adoption signal
AI review approval is missing
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 22 GitHub stars
Stars/forks activity: 22 stars, 1 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

Open full audit

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Start with one small task

1Read the source. Confirm the input, expected output, dependencies and permissions.
2Ask your agent for a plan. Approve setup and any costs before running a small isolated test.
3Check the output and changed files. Report only what actually ran; keep the source revision for reproduction.

Check the source for dependencies, API keys and third-party costs. A public repository does not mean every service is free.

Source & usage notes

IndexedStatic Checked

Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

Source repository: muend/geoai-skills
License: MIT
Version: Unknown
Last GitHub push: Sep 3, 2026
Registry updated: Sep 15, 2026
Instruction path: skills/swe-devops-standards/SKILL.md @ 096e5d4e6825

Version reported in registry metadata; check source releases before relying on it.

Quality

52/100

Needs review

Trust

55/100

Do not auto-install

Audit

68/100

Needs review

Open full audit View eval report

Dependency or permission surface needs review
Permission surface may require sandboxing
Low GitHub adoption signal
AI review approval is missing
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 22 GitHub stars
Stars/forks activity: 22 stars, 1 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

Verified installs: —
Outcomes: —

Copies are not installs. Installation counts require a reported successful installation; they are not a blanket quality guarantee.

Agent access

This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.

Open install API LLM text format Open manifest Auto-resolve Plan

More details

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      "Trust: 63/100 Manual review",
      "Audit: 68/100 Needs review",
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Creator: Muhammed Enes Duran
Source: muend/geoai-skills
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Muhammed Enes Duran

Registry indexed

swe-devops-standards

Review the source View on GitHub

Price unconfirmed★ 22 GitHub starsRegistry updated · Sep 15, 2026agent-skill

Overview

Read full documentation

Source documentation, not instructions for this website. Review permissions before running any commands.

Geospatial SWE & DevOps Standards

1. Environment realities (the top error source)

Script-first by default: no %matplotlib inline, !pip install, or display() unless the user is explicitly in a notebook. Every file runs from a terminal via python script.py behind an if __name__ == "__main__": block. (Cell markers like # %% are fine as an addition — the script must also work without them.)
Cross-platform paths: always pathlib.Path; never string-concatenate or hardcode / or \\. Ask or detect the user's OS before giving shell commands; give CMD/PowerShell syntax on Windows, POSIX elsewhere — don't mix (export vs set, venv/bin/activate vs venv\Scripts\activate).
Encodings: explicit encoding="utf-8" on every text file open — Windows still defaults to legacy code pages, and non-ASCII content corrupts silently.
Modern Python (3.11+): X | None unions, type aliases, structural pattern matching where they clarify; state the minimum version if a feature requires it.

2. Code quality defaults

Applied to every generated function/module, even when not asked:

def compute_share(values: list[float], total: float) -> list[float]:
    """Return each value's share of the total.

    Args:
        values: Values to compute shares for.
        total: Denominator; must be non-zero.

    Returns:
        Shares in the same order as values.

    Raises:
        ValueError: If total is zero.
    """
    if total == 0:
        raise ValueError("total must be non-zero — share is undefined.")
    return [v / total for v in values]

Type hints on every signature; dataclass/TypeAlias for complex types.
Google-style docstrings; one-liners suffice for trivial functions.
Never bare except:; catch specific exceptions, handle or re-raise with raise ... from e. A silent pass costs a week of debugging.
logging over print (leveled, formatted), except user-facing CLI output.
Note algorithmic complexity where it matters ("this is O(n log n), safe at n>10⁶") — especially around nested loops and pandas apply.
Magic numbers → named module-level constants.

3. Testing and verification

Offer at least a skeleton pytest for every function carrying real logic:

# test_compute.py — run: python -m pytest -q
import pytest
from compute import compute_share

def test_basic() -> None:
    assert compute_share([1, 1], 2) == [0.5, 0.5]

def test_zero_total_raises() -> None:
    with pytest.raises(ValueError):
        compute_share([1.0], 0)

Numerical code: test edge cases — empty input, NaN, negatives, single element.
Run generated code yourself when an execution environment exists; otherwise mark it explicitly "not executed" — no silent assumptions.

4. Dependencies and reproducibility

New project → virtual environment + pinned requirements.txt (package==version); never "install the latest".
Seed randomness and put the seed in config (details in ml-experiment-standards).
Note environment-difference risks where relevant (BLAS, CUDA, locale).

5. Git practices

Conventional Commits: feat(scope): ..., fix: ..., refactor: ...; the body explains why — the diff already shows what.
Commit in meaningful units; warn against 500-line single commits.
Default .gitignore: venv/, __pycache__/, *.pyc, large data files (suggest DVC/LFS), IDE folders.

6. Automation / DevOps

CI: minimal GitHub Actions for test + lint (ruff); note OS-runner differences if jobs must run on Windows too.
Docker: start from python:3.12-slim, simple single-stage until size/caching demands more; note image size and build-cache implications.
Monitoring: any long-lived service/pipeline ships three signals minimum: structured logs, failure alerting, basic metrics (duration, volume). ML services add drift checks (see ml-experiment-standards).
Scheduled jobs: match the user's platform — cron on POSIX, Task Scheduler (schtasks) on Windows.

7. Code review mode

Execution contract

Workflow: clarify the geospatial code's contract; reproduce the environment; inspect correctness and data invariants; implement the smallest safe change; test; package; document operations and rollback.
Decision rules: apply this skill to geospatial software and pipeline delivery, not generic non-spatial coding; scale CI, containers, and observability to the actual deployment risk.
Verification protocol: run focused and regression tests, lint and type checks where configured, exercise CRS/nodata/geometry edge cases, verify clean installation, and review CI artifacts.
Failure modes: block release for silent data loss, nondeterminism, mutable hidden state, unpinned critical dependencies, secrets, platform assumptions, missing rollback, or unhandled spatial edge cases.
Deliverables: reviewed code, tests, reproducible environment and lock data, CI configuration, operational notes, risk-ranked findings, observability plan, and rollback instructions.
Source freshness: consult the authoritative source registry before applying packaging, CI, testing, or supply-chain guidance.

View source ↗

File metadata

name: swe-devops-standards
description: >-
  Always invoke to review, repair, or deliver geospatial or GeoAI code,
  including contract compliance, security, error handling, transactions,
  tests, scripts, functions, notebooks, packages, CI/CD, and repository
  changes, even when deployment is not requested. Pair with the domain skill
  for ETL and other production code. Covers CRS/data invariants, dependencies,
  cross-platform reproducibility, automation, and shipping. Do not trigger for
  unrelated software or analysis requesting no code or repository artifact.
license: MIT
metadata:
  author: Muhammed Enes Duran

View original text

---
name: swe-devops-standards
description: >-
  Always invoke to review, repair, or deliver geospatial or GeoAI code,
  including contract compliance, security, error handling, transactions,
  tests, scripts, functions, notebooks, packages, CI/CD, and repository
  changes, even when deployment is not requested. Pair with the domain skill
  for ETL and other production code. Covers CRS/data invariants, dependencies,
  cross-platform reproducibility, automation, and shipping. Do not trigger for
  unrelated software or analysis requesting no code or repository artifact.
license: MIT
metadata:
  author: Muhammed Enes Duran
---

# Geospatial SWE & DevOps Standards

Purpose: code produced as part of geospatial work should run in the user's
real environment and meet peer-level engineering quality. Apply these rules
only when code or repository artifacts are in scope.

## 1. Environment realities (the top error source)

- **Script-first by default**: no `%matplotlib inline`, `!pip install`, or
  `display()` unless the user is explicitly in a notebook. Every file runs
  from a terminal via `python script.py` behind an
  `if __name__ == "__main__":` block. (Cell markers like `# %%` are fine
  as an addition — the script must also work without them.)
- **Cross-platform paths**: always `pathlib.Path`; never string-concatenate
  or hardcode `/` or `\\`. Ask or detect the user's OS before giving shell
  commands; give CMD/PowerShell syntax on Windows, POSIX elsewhere —
  don't mix (`export` vs `set`, `venv/bin/activate` vs
  `venv\Scripts\activate`).
- **Encodings**: explicit `encoding="utf-8"` on every text file open —
  Windows still defaults to legacy code pages, and non-ASCII content
  corrupts silently.
- **Modern Python (3.11+)**: `X | None` unions, `type` aliases, structural
  pattern matching where they clarify; state the minimum version if a
  feature requires it.

## 2. Code quality defaults

Applied to every generated function/module, even when not asked:

```python
def compute_share(values: list[float], total: float) -> list[float]:
    """Return each value's share of the total.

    Args:
        values: Values to compute shares for.
        total: Denominator; must be non-zero.

    Returns:
        Shares in the same order as values.

    Raises:
        ValueError: If total is zero.
    """
    if total == 0:
        raise ValueError("total must be non-zero — share is undefined.")
    return [v / total for v in values]
```

- Type hints on every signature; `dataclass`/`TypeAlias` for complex types.
- Google-style docstrings; one-liners suffice for trivial functions.
- **Never bare `except:`**; catch specific exceptions, handle or re-raise
  with `raise ... from e`. A silent `pass` costs a week of debugging.
- `logging` over `print` (leveled, formatted), except user-facing CLI
  output.
- Note algorithmic complexity where it matters ("this is O(n log n), safe
  at n>10⁶") — especially around nested loops and pandas `apply`.
- Magic numbers → named module-level constants.

## 3. Testing and verification

- Offer at least a skeleton pytest for every function carrying real logic:

```python
# test_compute.py — run: python -m pytest -q
import pytest
from compute import compute_share

def test_basic() -> None:
    assert compute_share([1, 1], 2) == [0.5, 0.5]

def test_zero_total_raises() -> None:
    with pytest.raises(ValueError):
        compute_share([1.0], 0)
```

- Numerical code: test edge cases — empty input, NaN, negatives, single
  element.
- Run generated code yourself when an execution environment exists;
  otherwise mark it explicitly "not executed" — no silent assumptions.

## 4. Dependencies and reproducibility

- New project → virtual environment + pinned `requirements.txt`
  (`package==version`); never "install the latest".
- Seed randomness and put the seed in config (details in
  `ml-experiment-standards`).
- Note environment-difference risks where relevant (BLAS, CUDA, locale).

## 5. Git practices

- Conventional Commits: `feat(scope): ...`, `fix: ...`, `refactor: ...`;
  the body explains *why* — the diff already shows *what*.
- Commit in meaningful units; warn against 500-line single commits.
- Default `.gitignore`: `venv/`, `__pycache__/`, `*.pyc`, large data files
  (suggest DVC/LFS), IDE folders.

## 6. Automation / DevOps

- **CI**: minimal GitHub Actions for test + lint (ruff); note OS-runner
  differences if jobs must run on Windows too.
- **Docker**: start from `python:3.12-slim`, simple single-stage until
  size/caching demands more; note image size and build-cache implications.
- **Monitoring**: any long-lived service/pipeline ships three signals
  minimum: structured logs, failure alerting, basic metrics (duration,
  volume). ML services add drift checks (see `ml-experiment-standards`).
- **Scheduled jobs**: match the user's platform — cron on POSIX,
  Task Scheduler (`schtasks`) on Windows.

## 7. Code review mode

Review in this order and report findings by severity: correctness (edge
cases, silent failures) → security (injection, secrets, path traversal) →
performance (N+1, needless copies, O(n²)) → readability. Every finding
ships with the suggested fix as code — never "this is bad" and nothing
else.

## Execution contract

- **Workflow:** clarify the geospatial code's contract; reproduce the environment; inspect correctness and data invariants; implement the smallest safe change; test; package; document operations and rollback.
- **Decision rules:** apply this skill to geospatial software and pipeline delivery, not generic non-spatial coding; scale CI, containers, and observability to the actual deployment risk.
- **Verification protocol:** run focused and regression tests, lint and type checks where configured, exercise CRS/nodata/geometry edge cases, verify clean installation, and review CI artifacts.
- **Failure modes:** block release for silent data loss, nondeterminism, mutable hidden state, unpinned critical dependencies, secrets, platform assumptions, missing rollback, or unhandled spatial edge cases.
- **Deliverables:** reviewed code, tests, reproducible environment and lock data, CI configuration, operational notes, risk-ranked findings, observability plan, and rollback instructions.
- **Source freshness:** consult [the authoritative source registry](references/authoritative-sources.md) before applying packaging, CI, testing, or supply-chain guidance.

Review the source

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Review before install: Avoid automatic install

License: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
Low GitHub adoption signal
AI review approval is missing
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 22 GitHub stars
Stars/forks activity: 22 stars, 1 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

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Source & usage notes

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Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

Source repository: muend/geoai-skills
License: MIT
Version: Unknown
Last GitHub push: Sep 3, 2026
Registry updated: Sep 15, 2026
Instruction path: skills/swe-devops-standards/SKILL.md @ 096e5d4e6825

Version reported in registry metadata; check source releases before relying on it.

Quality

52/100

Needs review

Trust

55/100

Do not auto-install

Audit

68/100

Needs review

Open full audit View eval report

Dependency or permission surface needs review
Permission surface may require sandboxing
Low GitHub adoption signal
AI review approval is missing
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 22 GitHub stars
Stars/forks activity: 22 stars, 1 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

Verified installs: —
Outcomes: —

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More details

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      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "GitHub adoption: 22 GitHub stars",
      "Stars/forks activity: 22 stars, 1 forks; issue activity unavailable in current metadata",
      "Dependency/runtime risk: command execution surface, credential or environment access",
      "Permission surface: secrets or environment access, shell or command execution"
    ]
  },
  "agent_proven": {
    "version": "agent-proven-v1",
    "score": 0,
    "tier": "unproven",
    "label": "Needs first agent run",
    "summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
    "metrics": {
      "totalOutcomes": 0,
      "successfulOutcomes": 0,
      "failedOutcomes": 0,
      "installAttempts": 0,
      "installSuccessRate": null,
      "successRate": null,
      "recentSuccessRate": null,
      "recentFailureRate": null,
      "riskBlocked": 0,
      "setupRequired": 0,
      "notRelevant": 0,
      "avgOutputQuality": null,
      "avgTimeToUsefulMs": null,
      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 68,
    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "Low GitHub adoption signal",
      "AI review approval is missing",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "GitHub adoption: 22 GitHub stars",
      "Stars/forks activity: 22 stars, 1 forks; issue activity unavailable in current metadata"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 52,
    "label": "Needs review"
  },
  "supply": {
    "track": "Coding and developer agents",
    "scenario": "Browser automation",
    "maintenance": "1mo since push",
    "risk": "Needs review"
  },
  "alternative_skills": [],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "production agents without a repository review",
    "Low GitHub adoption signal",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "AI review approval is missing",
    "Quality score needs review"
  ],
  "agent_contract": {
    "task_input": "Use swe-devops-standards in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 63/100 Manual review",
      "Audit: 68/100 Needs review",
      "Safety: 28/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "muend-swe-devops-standards (swe-devops-standards)",
      "install_command": "npx skills add muend/geoai-skills --skill swe-devops-standards",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "muend-swe-devops-standards",
      "task": "Use swe-devops-standards in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/muend-swe-devops-standards",
    "api": "https://www.openagentskill.com/api/agent/skills/muend-swe-devops-standards",
    "audit": "https://www.openagentskill.com/skills/muend-swe-devops-standards/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=muend-swe-devops-standards&task=Use%20swe-devops-standards%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20swe-devops-standards%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20swe-devops-standards%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/muend-swe-devops-standards/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/muend-swe-devops-standards"
  }
}

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This listing was indexed from public sources and is not marked official until a maintainer claim is approved.

Creator: Muhammed Enes Duran
Source: muend/geoai-skills
Indexed by: OpenAgentSkill community index

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Overview

Geospatial SWE & DevOps Standards

1. Environment realities (the top error source)

2. Code quality defaults

3. Testing and verification

4. Dependencies and reproducibility

5. Git practices

6. Automation / DevOps

7. Code review mode

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Review the source

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Start with one small task

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Registry indexed

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Community signal

Overview

Geospatial SWE & DevOps Standards

1. Environment realities (the top error source)

2. Code quality defaults

3. Testing and verification

4. Dependencies and reproducibility

5. Git practices

6. Automation / DevOps

7. Code review mode

Execution contract

Review the source

Price & running costs

Start with one small task

Source & usage notes

Agent access

Related skills

For the creator

Registry indexed

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Community signal