Doca Aes Gcm
NVIDIAOfficialCreated by NVIDIA
- Skill
- Tool integration
- Software development
- Apache-2.0
- Medium risk
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides technical guidance for developers integrating the DOCA AES-GCM library into applications running on BlueField DPUs or ConnectX NICs. It covers the lifecycle of AES-GCM tasks, including configuration, capability discovery, buffer management, and security-critical error handling. The skill emphasizes the AEAD (Authenticated Encryption with Associated Data) nature of AES-GCM, ensuring developers correctly handle authentication tags and avoid common pitfalls like confusing GCM with non-authenticated AES modes.
What it does
- Guides the configuration of doca_aes_gcm_task_encrypt and doca_aes_gcm_task_decrypt.
- Provides rules for querying device capabilities regarding supported key sizes (128/256-bit) and buffer limits.
- Defines security-first error handling, specifically for tag-verification-failed outcomes.
- Outlines correct mmap permission settings for source and destination buffers.
- Assists in validating implementations against standard test vectors like NIST GCMVS or RFC 5288.
- Routes users to appropriate resources for non-GCM modes or general key management.
When to use it
- When developing applications that offload AES-GCM operations to NVIDIA hardware.
- When debugging DOCA_ERROR codes related to AES-GCM task execution.
- When determining if a specific AES-GCM configuration is supported on a target device.
- When implementing decryption logic and needing to handle authentication tag failures securely.
- When choosing between hardware-accelerated DOCA AES-GCM and CPU-based OpenSSL implementations.
Advantages
- Ensures secure handling of AEAD primitives by treating tag verification as a security event.
- Provides clear boundaries between supported AES-GCM features and unsupported modes like CBC or CTR.
- Standardizes the approach to capability discovery and error taxonomy across DOCA versions.
- Promotes best practices for memory management and buffer sizing in hardware-accelerated workflows.
Usage examples
- “How do I check if my BlueField DPU supports AES-256-GCM?”
- The agent will guide you to use the doca_aes_gcm_cap_task_encrypt_is_key_type_supported query to verify support for DOCA_AES_GCM_KEY_256.
- “My decrypt task returned an IO_FAILED error, what should I do?”
- The agent will explain that this indicates an authentication tag verification failure, meaning the data is untrusted and must not be consumed.
- “Can I use this library for AES-192-GCM?”
- The agent will clarify that AES-192 is not supported by the DOCA AES-GCM library and recommend using a CPU-based library instead.
- “What permissions are required for my mmap buffers?”
- The agent will provide the required flags, such as DOCA_ACCESS_FLAG_LOCAL_READ_ONLY for source buffers and DOCA_ACCESS_FLAG_LOCAL_READ_WRITE for destination buffers.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/nvidia/doca-aes-gcm/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip doca-aes-gcm.zip -d .agents/skills
Documentation by NVIDIA
DOCA AES-GCM
Examples
"Auth tag isn't verifying on decrypt." — this is in scope. Treat tag-verification-failed as a security outcome, not a retryable size error.
"Is AES-192-GCM on this BlueField?" — no. Only key-128 and key-256 exist; do not invent AES-192 or CBC/CTR offload.
Instructions
Follow Loading order below. Open the tasks companion for configure / test / debug. Open the capabilities companion for key types and the AEAD safety note. Do not open every companion file.
Where to start: This skill assumes DOCA is already installed and
the user is doing hands-on AES-GCM-acceleration work on a
BlueField / ConnectX / host with DOCA. Open the tasks companion if
the user wants to do something (configure / build / modify / run /
test / debug); open the capabilities companion when the
question is what can DOCA AES-GCM express on this version. If the
user has not installed DOCA yet, route to
doca-setup/SKILL.md first. If the user is
asking "should I even use the accelerator for this encryption?",
the path-selection rule in
Capabilities and modes
is the first stop. If the user is treating AES-GCM as a confidentiality-only primitive (raw AES-CTR / AES-CBC style), stop and read the AEAD note in ## Safety policy first — AES-GCM is authenticated encryption, and confusing the two is the most expensive failure mode this skill exists to prevent.
Example questions this skill answers well
The CLASSES of DOCA AES-GCM questions this skill is built to answer, each with one worked example. The agent should treat the class as the load-bearing piece — the worked example is a single instance.
"Should I offload this AES-GCM encryption to DOCA AES-GCM, or just do it on the CPU with OpenSSL?" — worked example: "I am encrypting 4 KiB TLS records at line rate; is doca-aes-gcm worth the setup vs OpenSSL
EVP_aes_256_gcmon the CPU?". Answered by the path-selection table inCapabilities and modes
the "when NOT to use doca-aes-gcm" bullets in
Safety policy.
"Does my device support the AES-GCM key size I want?" — worked example: "is AES-256-GCM in the accelerator on this BlueField? And while we're here, is AES-192-GCM available?" (Answer: the library exposes only
DOCA_AES_GCM_KEY_128/DOCA_AES_GCM_KEY_256; AES-192 is not in the enum and is not supported. For the two real key types, gate ondoca_aes_gcm_cap_task_encrypt_is_key_type_supported(devinfo, key_type)and the matching_decrypt_is_key_type_supported. AES-192 is not available — route to a CPU library.) Answered by the per-key-type capability queries and the per-taskdoca_aes_gcm_cap_task_*_is_supportedqueries inCapabilities and modes
the discovery step in
configure.
"How do I correctly decrypt an AES-GCM message and verify the auth tag?" — worked example: "my
doca_aes_gcm_task_decryptcompletion reports an error — is the plaintext output safe to use?". Answered by the auth-tag verification rule inSafety policy
(do not use the plaintext if the auth tag did not verify) + the decrypt completion-handling workflow in
test and
debug.
"What permissions does the source / destination mmap need?" — worked example: "my
doca_aes_gcm_task_encryptreturnsDOCA_ERROR_NOT_PERMITTED". Answered by the permission matrix in ## Safety policythe mmap-set-permissions checklist in
test.
"Is this DOCA AES-GCM API available on my installed DOCA version?" — worked example: "is AES-192-GCM in the DOCA I have installed, on this device?". Answered by the version-compatibility overlay in
Version compatibility,
which cross-links the canonical detection chain in
doca-version/SKILL.mdand adds the AES-GCM-specific "discover key sizes via cap query" bullets."What does this
DOCA_ERROR_*from an AES-GCM call mean and which layer caused it?" — worked example: "DOCA_ERROR_NOT_PERMITTEDon my encrypt buffer". Answered by the AES-GCM overlay on the cross-library taxonomy inError taxonomy
the layered ladder in
debug that escalates to
doca-debug/SKILL.md.
IO_FAILEDon a decrypt completion is not a debugging question. It means auth tag verification failed, and that is a security event, not a code or configuration bug — there is nothing to classify by layer and nothing to escalate todoca-debug/SKILL.md. The plaintext that task produced is poisoned and must not be consumed; the decrypt must not be retried; the input ciphertext is tampered or corrupted and must be treated as untrusted; the outcome belongs to the caller's application-layer policy for tampered input. Lead with that. Only if the user is testing against known-good vectors is a configuration cause (wrong key, IV, AAD, or tag length) worth raising, and even then it comes second. Full rule inError taxonomy
and
## Safety policy.
Audience
This skill serves external developers building applications that
consume the DOCA AES-GCM library — i.e., users whose code calls
doca_aes_gcm_* (directly in C/C++, or through FFI/bindings from
another language) to offload AES-GCM authenticated encryption /
decryption onto a BlueField DPU or ConnectX accelerator. It is not
for NVIDIA developers contributing to DOCA AES-GCM itself.
Language scope. DOCA AES-GCM ships as a C library with
pkg-config module name doca-aes-gcm. The shipped samples are
written in C. C and C++ consumers are the canonical case and the
worked examples in the tasks companion assume that path. Other-language
consumers (Rust, Go, Python, …) consume the same *.so through FFI
or language-specific bindings; the skill's contribution in that case
is to keep the lifecycle, capability-discovery, permission,
error-taxonomy, AEAD-semantics, and encrypt-vs-decrypt guidance
language-neutral, and to route the agent to the public C ABI as the
authoritative surface that any wrapper will eventually call.
Key handling is out of scope. This skill teaches the agent how to use the DOCA AES-GCM library; it does not teach the user how to generate, store, rotate, or distribute AES-GCM keys. Key-management is the user's responsibility (a KMS, an HSM, a sealed file, an env var the user trusts). The skill's only key-handling rule is the operational one in
Safety policy:
do not log keys, do not commit them to source, and treat any key buffer the program holds as sensitive memory.
When to load this skill
Load this skill when the user is doing hands-on DOCA AES-GCM work, in any language. Concretely:
Initializing a
doca_aes_gcmcontext on adoca_devand configuring at least one task type (doca_aes_gcm_task_encryptand/ordoca_aes_gcm_task_decrypt) beforedoca_ctx_start().Choosing between encrypt (
doca_aes_gcm_task_encrypt— takes key + IV + AAD + plaintext, produces ciphertext + auth tag) and decrypt (doca_aes_gcm_task_decrypt— takes key + IV + AAD + ciphertext + expected auth tag, produces plaintext and verifies the tag) for the user's data shape.Setting permissions on
doca_mmapcorrectly for the source buffer (DOCA_ACCESS_FLAG_LOCAL_READ_ONLYat minimum — the plaintext on encrypt or the ciphertext on decrypt) and the destination buffer (DOCA_ACCESS_FLAG_LOCAL_READ_WRITE).Checking which AES-GCM key types (
DOCA_AES_GCM_KEY_128/DOCA_AES_GCM_KEY_256— AES-192 is not in the library) the active device's accelerator advertises viadoca_aes_gcm_cap_task_encrypt_is_key_type_supported/doca_aes_gcm_cap_task_decrypt_is_key_type_supported, and which task types viadoca_aes_gcm_cap_task_encrypt_is_supported/_task_decrypt_is_supported.Sizing the per-submission plaintext against
doca_aes_gcm_cap_task_encrypt_get_max_buf_size(devinfo).Validating an encrypt + decrypt round-trip against a published AES-GCM test vector (NIST GCMVS, or RFC 5288 examples) before pushing any user data through the accelerator.
Handling the auth-tag verification result on decrypt completions as a security-critical signal — a tag-mismatch completion means the ciphertext was tampered with or corrupted, and the plaintext output of that task is poisoned and must not be consumed.
Debugging a
DOCA_ERROR_*returned from an AES-GCM call (lifecycle vs. unsupported key size vs. permission vs. tag verification failure on decrypt) and the task-completion event on the progress engine.Designing or extending non-C bindings (Rust, Go, Python, …) that wrap the AES-GCM C ABI — for the lifecycle, permission, capability, AEAD-semantics, and encrypt-vs-decrypt rules the wrapper must honor.
Do not load this skill for general DOCA orientation, install of
DOCA itself, AES modes that are not GCM (CBC / CTR / XTS — those are
not in this library and CPU + OpenSSL is the right answer), SHA
hashing on the same accelerator family (use
doca-sha/SKILL.md), or other DOCA libraries. For
those, use
doca-public-knowledge-map/SKILL.md.
What this skill provides
This is a thin loader. The body keeps only the orientation needed to pick the right next file. The substantive AES-GCM-specific material lives in two companion files:
the capabilities companion — what DOCA AES-GCM can express on this version: the two task types (encrypt and decrypt), the AEAD output shape (ciphertext + auth tag on encrypt; verified plaintext on decrypt), the AES-GCM key-type surface (only 128-bit and 256-bit — AES-192 is not in the enum, both cap-queried), the capability-query surface (
doca_aes_gcm_cap_*for task presence, key-type support, and buffer sizing), the AES-GCM error taxonomy (mapped onto the cross-libraryDOCA_ERROR_*set, with explicit treatment of the tag-verification-failure outcome as security-critical), the observability surface (per-task completion events on the progress engine), the safety policy that gates source / destination mmap permission decisions and key-handling cautions, and the path-selection rule (when to use doca-aes-gcm versus CPU OpenSSL or a different DOCA crypto library).the tasks companion — step-by-step workflows for the six in-scope AES-GCM verbs:
configure,build,modify,run,test,debug. Plus aDeferred task verbsblock that points out-of-scope questions at the right next skill.
The skill assumes a host or BlueField where DOCA is already
installed at the standard location and the user has the privileges
their public install profile expects. It does not cover installing
DOCA — that path goes through
doca-setup/SKILL.md.
What this skill deliberately does not ship
This skill is agent guidance, not a samples or templates bundle. To keep the boundary clean, it deliberately does not contain — and pull requests should not add:
Pre-written DOCA AES-GCM application source code, in any language. The verified AES-GCM source code is the shipped C samples at
/opt/mellanox/doca/samples/doca_aes_gcm/. The agent's job is to route the user to those files and prescribe a minimum-diff modification on them via the universal modify-a-sample workflow indoca-programming-guide/SKILL.md, layered with the AES-GCM-specific overrides inmodify.
Pre-computed AES-GCM test vectors. The skill tells the agent to use a published test vector (e.g. NIST GCMVS, RFC 5288 AES-GCM examples) as the known-vector smoke; it does not ship a vector bank of its own. The agent must cite the vector source so the user can audit it.
Pre-baked AES-GCM keys, IVs, or AAD strings. A key in this repo is a key in every customer's repo — by construction, it must not be there. The skill teaches the shape of the inputs; the user supplies the actual bytes from their own key-management system.
Standalone build manifests (
meson.build,CMakeLists.txt,Cargo.toml, …) parked inside the skill. The agent constructs the build manifest in the user's project directory against the user's installed DOCA, wherepkg-config --modversion doca-aes-gcmis the source of truth.A
samples/,bindings/, orreference/subtree of any kind. A mock or incomplete artifact in this skill's tree, even one labeled "reference", is misleading: users will read it as buildable.
Loading order
Read this skill first to confirm the user's question is in scope.
For the AES-GCM capability matrix, task types, key-size surface, capability-query rules, permission matrix, AEAD semantics, error taxonomy, observability, and safety / path-selection policy, see the capabilities companion.
For step-by-step workflows — configure, build, modify, run, test, debug — see the tasks companion.
Both companion files cross-link to each other,
doca-version/SKILL.md for the canonical
version-handling rules, and
doca-public-knowledge-map/SKILL.md
whenever the right answer is "look it up in the public docs or the
installed package layout" rather than "AES-GCM-specific guidance".
Related skills
doca-public-knowledge-map/SKILL.md— the routing table for every public DOCA documentation source and the on-disk layout of an installed DOCA package. The DOCA AES-GCM page lives atdocs.nvidia.com/doca/sdk/DOCA-AES-GCM/; it is a member of the DOCA Crypto Acceleration family alongsidedoca-sha/SKILL.md.doca-setup/SKILL.md— env preparation, install verification, and the I have no install yet path with the public NGC DOCA container. This skill assumes its preconditions are satisfied.doca-version/SKILL.md— canonical DOCA version-handling rules. This skill's## Version compatibilitycross-links the four-way match rule and adds only the AES-GCM-specific "discover key sizes + task presence via cap query" overlay.doca-structured-tools-contract/SKILL.md— the bundle's structured-tools precedence rule (detect / prefer / fall back / report). The Command appendix in the tasks companion honors this contract.doca-programming-guide/SKILL.md— general DOCA programming patterns shared by every library: the canonicalpkg-config+ meson build pattern, the universal modify-a-shipped-sample first-app workflow, the universal lifecycle, the cross-libraryDOCA_ERROR_*taxonomy, and the program-side debug order. This skill layers AES-GCM specifics on top.doca-sha/SKILL.md— the sibling library in the DOCA Crypto Acceleration family for hardware-accelerated SHA hashing. Load alongside this skill when the user's flow is authenticated-encryption with a separate keyed hash (rare — AES-GCM already provides authentication via its tag) or when the user is comparing offload paths between the two.doca-debug/SKILL.md— the cross-cutting debug ladder (install / version / build / link / runtime / program / driver). AES-GCM-specific debug (key-size-not-supported, oversized input, tag-verification failure on decrypt) overlays on top of that ladder.
Files
- ATTRIBUTION.mdAdded by AutoGineer894 B
- BENCHMARK.md9.8 KiB
- CAPABILITIES.md26.8 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md16.4 KiB
- SKILLCARD.yaml2.7 KiB
- TASKS.md28.8 KiB
- evals/evals.json3.9 KiB
- skill-card.md5.1 KiB
- skill.oms.sig5.1 KiB
BENCHMARK.md
Skill Benchmark: doca-aes-gcm
✅ Overall verdict: PASS — Recommended for publication
Publication Recommendation
Recommended for publication based on the completed evaluation evidence in this report.
Evaluation Metadata
Skill:
doca-aes-gcmValidation status:
passedEvaluation date: 2026-10-28
Evaluator version:
1.6.2Agents: Claude Code (
aws/anthropic/bedrock-claude-opus-5-5), Codex (openai/openai/gpt-6.1-sol)Tasks: 4 evaluation tasks (3 positive, 1 negative)
Dataset digest:
sha256:b714f4675f43669d5298e2ed02dbee85b5b8d394ced607da4e7f4da7e23663cb(skill-evaluator-dataset-snapshot/1)Attempts per task: 1
Environment:
k8s-sandboxTier 2 evidence: required for publication
Tier 3 evidence: required for publication
Each task attempt ran in its own isolated sandbox pod.
Tier 1 LLM Provenance
SkillSpector provider, model, analyzer revision, and determinism evidence retained for this scan.
| Skill | Provider | Adapter | Analyzer | Model | Revision | Determinism |
|---|---|---|---|---|---|---|
| doca-aes-gcm | nv_inference | openai | mcp_tool_poisoning | aws/anthropic/bedrock-claude-opus-5-5 | 2.13.1 | nondeterministic / provider_defaults |
| doca-aes-gcm | nv_inference | openai | semantic_developer_intent | aws/anthropic/bedrock-claude-opus-5-5 | 2.13.1 | nondeterministic / provider_defaults |
| doca-aes-gcm | nv_inference | openai | semantic_quality_policy | aws/anthropic/bedrock-claude-opus-5-5 | 2.13.1 | nondeterministic / provider_defaults |
| doca-aes-gcm | nv_inference | openai | semantic_security_discovery | aws/anthropic/bedrock-claude-opus-5-5 | 2.13.1 | nondeterministic / provider_defaults |
| doca-aes-gcm | nv_inference | openai | meta_analyzer | aws/anthropic/bedrock-claude-opus-5-5 | 2.13.1 | nondeterministic / provider_defaults |
Controls for
doca-aes-gcm: temperature=not set (source=provider_default, forwarded=no); seed=not set (source=unset, forwarded=no); reasoning_effort=not set (source=provider_default, forwarded=no)
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
| Measure | Claude Code (Baseline → Skill Uplift) | Codex (Baseline → Skill Uplift) |
|---|---|---|
| Overall | 98.0% — baseline ran, but no comparable score was available; uplift unavailable | 89.1% — baseline ran, but no comparable score was available; uplift unavailable |
| Security | 100.0% → 100.0% (±0.0 points) | 87.5% → 100.0% (+12.5 points) |
| Correctness | 60.0% → 100.0% (+40.0 points) | 100.0% → 100.0% (±0.0 points) |
| Discoverability | 100.0% — baseline ran, but no comparable score was available; uplift unavailable | 95.0% — baseline ran, but no comparable score was available; uplift unavailable |
| Effectiveness | 39.6% → 100.0% (+60.4 points) | 87.5% → 87.5% (±0.0 points) |
| Efficiency | 89.8% — baseline ran, but no comparable score was available; uplift unavailable | 63.2% — 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.
Comparison details
Shared-source comparisons use the same signals and weights on both conditions. They do not replace the full dimension scores above or change the publication verdict.
| Agent | Dimension | Basis | Baseline → With skill (uplift) | Explanation |
|---|---|---|---|---|
| claude-code | Discoverability | Dimension comparison | N/A | Discoverability is not applicable to the no-skill baseline; there is no installed skill to discover. |
| claude-code | Efficiency | Token-only comparison | 98.2% → 96.3% (-1.9 points) | Only Token Efficiency is comparable; the full dimension scores use different evaluator sources. |
| codex | Discoverability | Dimension comparison | N/A | Discoverability is not applicable to the no-skill baseline; there is no installed skill to discover. |
| codex | Efficiency | Token-only comparison | 83.1% → 81.9% (-1.2 points) | Only Token Efficiency is comparable; the full dimension scores use different evaluator sources. |
Token Usage
Actual Tier 3 execution usage is reported for every observed agent/case pair and both conditions.
| Agent | Dataset case | With skill | Without skill | Delta | Change | Coverage |
|---|---|---|---|---|---|---|
| claude-code | All cases | 313,567 | 359,609 | -46,042 | -12.80% | skill 4/4; base 4/4 |
| claude-code | doca-aes-gcm.explicit-positive.v1 | 78,221 | 72,248 | +5,973 | +8.27% | skill 1/1; base 1/1 |
| claude-code | doca-aes-gcm.implicit-positive.v1 | 78,367 | 107,785 | -29,418 | -27.29% | skill 1/1; base 1/1 |
| claude-code | doca-aes-gcm.implicit-positive.v2 | 120,103 | 107,558 | +12,545 | +11.66% | skill 1/1; base 1/1 |
| claude-code | doca-aes-gcm.negative.v1 | 36,876 | 72,018 | -35,142 | -48.80% | skill 1/1; base 1/1 |
| codex | All cases | 376,389 | 325,826 | +50,563 | +15.52% | skill 4/4; base 4/4 |
| codex | doca-aes-gcm.explicit-positive.v1 | 117,175 | 198,840 | -81,665 | -41.07% | skill 1/1; base 1/1 |
| codex | doca-aes-gcm.implicit-positive.v1 | 90,756 | 56,900 | +33,856 | +59.50% | skill 1/1; base 1/1 |
| codex | doca-aes-gcm.implicit-positive.v2 | 120,668 | 30,437 | +90,231 | +296.45% | skill 1/1; base 1/1 |
| codex | doca-aes-gcm.negative.v1 | 47,790 | 39,649 | +8,141 | +20.53% | skill 1/1; base 1/1 |
| ALL AGENTS | Dataset aggregate | 689,956 | 685,435 | +4,521 | +0.66% | skill 8/8; base 8/8 |
Prompt tokens include cached reads, so total tokens are prompt + completion (cached is not added twice). The Efficiency score uses (prompt - cached) + completion. Change is 100 * (with - without) / without; negative means fewer tokens. N/A comparisons include their reason in the coverage column; coverage is never estimated.
Tier Status
| Tier | Purpose | Status | Evidence |
|---|---|---|---|
| Tier 1 | Static validation | PASSED WITH OBSERVATIONS | 11 validator(s); 16 finding(s) |
| Tier 2 | Semantic deduplication | PASSED | 2 validator(s); 0 finding(s) |
| Tier 3 | Live agent evaluation | PASS | 2 agent(s); 4 task(s) |
Findings and Observations
<details> <summary>Show detailed findings and successful checks</summary>
MEDIUM QUALITY/quality_discoverability: Description contains vague words (
skills/libs/doca-aes-gcm/SKILL.md)MEDIUM QUALITY/quality_discoverability: Description uses first/second person (
skills/libs/doca-aes-gcm/SKILL.md)MEDIUM SCHEMA/metadata_key_style: Metadata key 'doca_version' is not kebab-case (
skills/libs/doca-aes-gcm/SKILL.md)MEDIUM SCHEMA/folder_hierarchy: Unexpected nesting depth for general skill (
skills/libs/doca-aes-gcm)MEDIUM SECURITY/Sudo/Root Execution (PE2): Privilege Escalation: Sudo (
BENCHMARK.md:88)11 additional finding(s) are available in the full evaluation artifacts.
</details>
Scoring Methodology
<details> <summary>Show dimension definitions, source signals, and thresholds</summary>
| Dimension | Question | Scored signals |
|---|---|---|
| Security | Is it safe to use? | security (100%) |
| Correctness | Is the answer correct? | accuracy (100%) |
| Discoverability | Was the right skill loaded when needed? | skill_execution (100%) |
| Effectiveness | Did the skill help complete the task? | goal_accuracy (50%) + behavior_check (50%) |
| Efficiency | Did 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_efficiencywire 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.
The author says: Requires DOCA SDK installed at /opt/mellanox/doca on Linux (Ubuntu 22.04/24.04 or RHEL/SLES) with a BlueField DPU or ConnectX NIC attached. Reads the local install via `pkg-config doca-aes-gcm` and inspects /opt/mellanox/doca/{lib,include,samples,applications}. Only `DOCA_AES_GCM_KEY_128` / `_256` are supported (not AES-192). The validation steps require a live DOCA install.
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
- NVIDIA
- Publisher
- NVIDIA (Official)
- Source
- NVIDIA/skills · skills/doca-aes-gcm · commit fce3cca
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE-APACHE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
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Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download doca-aes-gcm.zip