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Holoscan Install Conda

NVIDIAOfficialCreated by Holoscan TeamDistributed by NVIDIA

  • Skill
  • Tool integration
  • Software development
  • Apache-2.0
  • Medium risk

From the author

Handle Holoscan SDK 5.x Conda installation requests: explain 5.0 package unavailability, check later releases, and select a supported installation method.
— the description by Holoscan Team

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill manages installation requests for Holoscan SDK 5.x, specifically addressing the lack of Conda support for version 5.0. It provides a structured process to verify Conda availability for specific releases and redirects users to supported installation methods such as containers, Debian packages, Python wheels, or source builds when Conda is not a viable option. The skill ensures that the user's target SDK version and language preferences (C++ or Python) are maintained throughout the selection process.

What it does

  • Verifies Conda package availability for specific Holoscan SDK 5.x releases.
  • Explains the unavailability of Conda packages for Holoscan SDK 5.0.
  • Prevents incorrect installation attempts by distinguishing between 4.x and 5.x release lines.
  • Recommends alternative installation methods including containers, Debian, wheels, or source.
  • Maintains the user's requested SDK version and programming language goals during redirection.
  • Integrates with other Holoscan setup skills to assess host environments when necessary.

When to use it

  • When a user requests a Conda-based installation for Holoscan SDK 5.x.
  • When you need to clarify which installation methods are supported for a specific Holoscan release.
  • When a user is unsure whether their target Holoscan version supports Conda.
  • When you need to transition a user from an unsupported Conda request to a valid installation path.

Advantages

  • Prevents failed installation attempts by clarifying Conda support status.
  • Ensures users follow official, verified installation paths for the SDK.
  • Maintains consistency in SDK versioning and language requirements.
  • Reduces ambiguity by providing clear alternatives based on the user's specific goals.

Usage examples

“Install Holoscan SDK 5.0 using Conda.”
The agent explains that Conda is not supported for version 5.0 and provides a list of alternative installation methods like containers or Debian packages.
“How do I set up Holoscan 5.x with Conda?”
The agent checks the specific release version, confirms if Conda is supported, and either provides instructions or suggests the appropriate alternative installation skill.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/nvidia/holoscan-install-conda/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip holoscan-install-conda.zip -d .agents/skills

Documentation by Holoscan Team

Holoscan 5.0 and Conda

This skill checks Conda availability for the requested Holoscan SDK 5.x release. SDK 5.0 has no Conda package. For later releases, it follows the release's Conda instructions when support is documented and matching packages are published. Otherwise, it offers container, Debian, wheel, or source alternatives while preserving the requested SDK version and C++ or Python goal.

Conda-forge Holoscan 4.x packages are a different release line and cannot satisfy a 5.x installation request.

  1. Establish the requested SDK release. For 5.0, explain that Conda installation is unavailable; do not install Miniforge, create an environment, or run a Holoscan Conda solve. For a later release, follow its documented Conda method only if that release explicitly supports it and the matching packages are published. If availability is unknown, report the uncertainty and offer the verified alternatives below rather than inventing an installation command.

  2. When Conda is unavailable or unconfirmed, preserve the requested SDK version and the user's C++ or Python goal. Offer the matching skill when available, or use the release's installation guide directly:

    GoalMethod
    Ready-to-use C++ and Python environmentholoscan-install-container
    Native C++ headers and librariesholoscan-install-debian
    Python environmentholoscan-install-wheel
    Early Access binaries unavailable, or SDK developmentholoscan-install-source
  3. Confirm which alternative the user wants if they have not already chosen one. If the host or goal is unclear, use holoscan-setup to assess it.

  4. If the user explicitly needs 4.x, consult that release's Conda instructions and report the version change before proceeding. Keep an existing Conda installation intact; its presence does not establish availability for the requested release.

Use the selected release notes and installation guide to establish current support. Do not treat a 4.x search result as 5.x support.

Files

BENCHMARK.md

Skill Benchmark: holoscan-install-conda

✅ Overall verdict: PASS — Recommended for publication

Publication Recommendation

Recommended for publication based on the completed evaluation evidence in this report.

Evaluation Metadata

  • Skill: holoscan-install-conda

  • Evaluation date: 2026-10-15

  • Evaluator version: 1.5.6

  • Agents: Claude Code (aws/anthropic/bedrock-claude-opus-4-8), Codex (openai/openai/gpt-5.5)

  • Tasks: 6 evaluation tasks (5 positive, 1 negative)

  • Dataset digest: sha256:e87c52afd05fad3a0b48ec1070a137338aa69d392aa189bee08d8193030935c2 (skill-evaluator-dataset-snapshot/1)

  • Attempts per task: 1

  • Environment: k8s-sandbox

  • Tier 2 evidence: required for publication

  • Tier 3 evidence: required for publication

Each task attempt ran in its own isolated sandbox pod.

What This Report Answers

The three-tier evaluation checks whether the skill:

  • is safe to use;

  • produces correct answers;

  • is discovered and activated when needed;

  • helps the agent complete the user's goal and expected workflow; and

  • avoids wasted skill and tool usage.

Results at a Glance

MeasureClaude Code (Baseline → Skill Uplift)Codex (Baseline → Skill Uplift)
Overall96.3% — baseline ran, but no comparable score was available; uplift unavailable94.7% — baseline ran, but no comparable score was available; uplift unavailable
Security100.0% → 100.0% (±0.0 points)100.0% → 83.3% (-16.7 points)
Correctness86.7% → 96.7% (+10.0 points)86.7% → 100.0% (+13.3 points)
Discoverability100.0% — baseline ran, but no comparable score was available; uplift unavailable95.0% — baseline ran, but no comparable score was available; uplift unavailable
Effectiveness87.1% → 85.8% (-1.3 points)74.6% → 97.1% (+22.5 points)
Efficiency99.0% — baseline ran, but no comparable score was available; uplift unavailable97.9% — baseline ran, but no comparable score was available; uplift unavailable

How to read this table: baseline is the same task attempted without the target skill. Scores are rounded to one decimal; threshold-adjacent values use additional precision so their displayed band matches the verdict. Uplift is derived from those displayed scores and shown in percentage points.

Example: 47.0% → 92.0% (+45.0 points) means the skill-assisted run scored 92.0%, 45.0 percentage points above its 47.0% no-skill baseline.

A partial dimension was calculated from only the available configured signals; review the detailed report before relying on it.

Token Usage

Actual Tier 3 execution usage is reported for every observed agent/case pair and both conditions.

AgentDataset caseWith skillWithout skillDeltaChangeCoverage
claude-codeAll cases522,7061,429,614-906,908-63.44%skill 6/6; base 6/6
claude-codeholoscan-install-conda-5x-00160,310185,707-125,397-67.52%skill 1/1; base 1/1
claude-codeholoscan-install-conda-5x-00260,563248,485-187,922-75.63%skill 1/1; base 1/1
claude-codeholoscan-install-conda-5x-00360,765220,599-159,834-72.45%skill 1/1; base 1/1
claude-codeholoscan-install-conda-5x-004219,087272,488-53,401-19.60%skill 1/1; base 1/1
claude-codeholoscan-install-conda-5x-00561,292217,237-155,945-71.79%skill 1/1; base 1/1
claude-codeholoscan-install-conda-5x-00660,689285,098-224,409-78.71%skill 1/1; base 1/1
codexAll cases267,185266,219+966+0.36%skill 6/6; base 6/6
codexholoscan-install-conda-5x-00128,30614,138+14,168+100.21%skill 1/1; base 1/1
codexholoscan-install-conda-5x-00228,21514,069+14,146+100.55%skill 1/1; base 1/1
codexholoscan-install-conda-5x-00328,37214,543+13,829+95.09%skill 1/1; base 1/1
codexholoscan-install-conda-5x-004125,775195,359-69,584-35.62%skill 1/1; base 1/1
codexholoscan-install-conda-5x-00528,30514,081+14,224+101.02%skill 1/1; base 1/1
codexholoscan-install-conda-5x-00628,21214,029+14,183+101.10%skill 1/1; base 1/1
ALL AGENTSDataset aggregate789,8911,695,833-905,942-53.42%skill 12/12; base 12/12

Prompt tokens include cached reads, so total tokens are prompt + completion (cached is not added twice). The Efficiency score uses (prompt - cached) + completion. N/A means the relevant trajectory counters were not available; coverage is never estimated.

Tier Status

TierPurposeStatusEvidence
Tier 1Static validationPASSED WITH OBSERVATIONS11 validator(s); 10 finding(s)
Tier 2Semantic deduplicationPASSED2 validator(s); 0 finding(s)
Tier 3Live agent evaluationPASS2 agent(s); 6 task(s)

Findings and Observations

<details> <summary>Show detailed findings and successful checks</summary>

  • MEDIUM SCHEMA/body_recommended_section: Missing recommended section: '## Instructions' (team-skills/holoscan/holoscan-sdk/holoscan-install-conda/SKILL.md)

  • MEDIUM SCHEMA/body_recommended_section: Missing recommended section: '## Examples' (team-skills/holoscan/holoscan-sdk/holoscan-install-conda/SKILL.md)

  • LOW QUALITY/quality_correctness: No examples provided (team-skills/holoscan/holoscan-sdk/holoscan-install-conda/SKILL.md)

  • LOW QUALITY/quality_discoverability: Description doesn't mention WHEN to use this skill (team-skills/holoscan/holoscan-sdk/holoscan-install-conda/SKILL.md)

  • LOW QUALITY/quality_discoverability: Broad description without negative triggers may cause over-triggering (team-skills/holoscan/holoscan-sdk/holoscan-install-conda/SKILL.md)

  • 5 additional finding(s) are available in the full evaluation artifacts.

</details>

Scoring Methodology

<details> <summary>Show dimension definitions, source signals, and thresholds</summary>

DimensionQuestionScored signals
SecurityIs it safe to use?security (100%)
CorrectnessIs the answer correct?accuracy (100%)
DiscoverabilityWas the right skill loaded when needed?skill_execution (100%)
EffectivenessDid the skill help complete the task?goal_accuracy (50%) + behavior_check (50%)
EfficiencyDid it avoid wasted tool calls and token usage?skill_efficiency (50%) + token_efficiency (50%)
  • Dimension bands: PASS at 50% or above; NEUTRAL from 40% to below 50%; FAIL below 40%.

  • Overall Tier 3 lift: PASS at +5 points or more; FAIL at -10 points or less; values between those bands are NEUTRAL.

  • Overall verdict: PASS only when every configured dimension passes for at least one supported agent. Lift is reported as diagnostic evidence and does not override this gate.

  • The 50% attempt pass threshold is a separate per-task gate; it is not the dimension pass threshold.

  • Effectiveness is the equal-weight mean of goal completion (goal_accuracy) and expected workflow adherence (behavior_check).

  • Efficiency is 50% tool-call productivity (the backward-compatible skill_efficiency wire id) and 50% token_efficiency. Positive-case skill routing is scored under Discoverability, not Efficiency; a negative case without a routing target is N/A. N/A sources are omitted, remaining weights are renormalized, and the dimension is marked partial.

Signals present in this run:

  • security (Security): unsafe operations, secret leakage, and unauthorized access.

  • skill_execution (Skill Execution): whether the expected skill was selected, decoys were avoided, and the workflow executed.

  • skill_efficiency (Tool Productivity): tool-call productivity (legacy wire id; routing is scored under Discoverability).

  • accuracy (Accuracy): final-answer correctness against the reference answer.

  • goal_accuracy (Goal Accuracy): whether the user's goal was achieved.

  • behavior_check (Behavior Check): whether the expected workflow behavior was followed.

  • token_efficiency (Token Efficiency): actual uncached prompt plus completion usage (50% of Efficiency).

</details>

Freshness

Regenerate this benchmark when the skill, evaluation dataset, target agent/model, evaluator version, environment, or scoring policy changes.

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Security

What our scanner noticed· Medium risk

  • Large base64-encoded blobs

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
Holoscan Team
Publisher
NVIDIA (Official)
Distributed by
NVIDIA
Source
NVIDIA/skills · skills/holoscan-install-conda · commit fce3cca
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE-APACHE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by Holoscan Team or NVIDIA.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download holoscan-install-conda.zip