Finishing a Development Branch
obra/superpowers
Walks the last step of a branch: confirm tests pass, detect the git environment, ask how to integrate, carry out your choice and clean up the worktree.
A skill your agent uses when the user is doing hands-on host-side DOCA PCC work to load a CUSTOM Programmable Congestion Control algorithm onto a BlueField DPU — creating per-port docapcc contexts…
$ npx skills add NVIDIA/skills --skill doca-pcc -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install NVIDIA/skills doca-pcc --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/doca-pcc .claude/skills/doca-pcc && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "doca-pcc" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/doca-pcc into .claude/skills/doca-pcc/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "doca-pcc", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/NVIDIA/skills/tree/main/skills/doca-pccType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add NVIDIA/skills --skill doca-pcc -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install NVIDIA/skills doca-pcc --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/doca-pcc .agents/skills/doca-pcc && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "doca-pcc" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/doca-pcc into .agents/skills/doca-pcc/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "doca-pcc", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add NVIDIA/skills --skill doca-pcc -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install NVIDIA/skills doca-pcc --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/doca-pcc .cursor/skills/doca-pcc && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "doca-pcc" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/doca-pcc into .cursor/skills/doca-pcc/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "doca-pcc", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/NVIDIA/skills.git --path skills/doca-pcc--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add NVIDIA/skills --skill doca-pcc -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install NVIDIA/skills doca-pcc --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/doca-pcc .gemini/skills/doca-pcc && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "doca-pcc" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/doca-pcc into .gemini/skills/doca-pcc/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "doca-pcc", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install NVIDIA/skills doca-pccInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add NVIDIA/skills --skill doca-pcc -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/doca-pcc .github/skills/doca-pcc && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "doca-pcc" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/doca-pcc into .github/skills/doca-pcc/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "doca-pcc", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add NVIDIA/skills --skill doca-pcc -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install NVIDIA/skills doca-pcc --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/doca-pcc .opencode/skills/doca-pcc && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "doca-pcc" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/doca-pcc into .opencode/skills/doca-pcc/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "doca-pcc", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
doca-pccA skill your agent uses when the user is doing hands-on host-side DOCA PCC work to load a CUSTOM Programmable Congestion Control algorithm onto a BlueField DPU — creating per-port docapcc contexts…
Doca Pcc is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Use this skill when the user is doing hands-on host-side DOCA PCC work to load a CUSTOM Programmable Congestion Control algorithm onto a BlueField DPU — creating per-port docapcc contexts, loading a dpacc-compiled docapccapp onto the docadev for the RoCE-bearing port, parameterizing it, walking triple-axis capability discovery (DOCA cap-query + DPA-capable BlueField + firmware custom-PCC slot enabled), or debugging DOCAERROR from docapcc. Trigger even without explicit "DOCA PCC" phrasing — implicit forms include…
Its SKILL.md is about 4.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files (for example `BENCHMARK.md`, `CAPABILITIES.md` and `SKILLCARD.yaml`). Compatibility notes: Requires DOCA SDK installed at /opt/mellanox/doca on Linux (Ubuntu 22.04/24.04 or RHEL/SLES) with a BlueField DPU whose DPA processor is exposed to the host…
It sits in Development. The repository describes itself as: Agent Skills for NVIDIA products — install into Claude Code, Codex, and other coding agents to run Physical AI, robotics, simulation, CUDA, and RAG workflows end to end. The licence is Apache-2.0.
3 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 0e0d506. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Links to these hosts (documentation or services it may open):
docs.nvidia.comFrom URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Requires DOCA SDK installed at /opt/mellanox/doca on Linux (Ubuntu 22.04/24.04 or RHEL/SLES) with a BlueField DPU whose DPA processor is exposed to the host AND whose firmware has the custom-PCC slot enabled. Also requires the DPACC compiler installed at a version matched to DOCA per the DOCA Compatibility Policy. Reads the user's local install via `pkg-config doca-pcc` and inspects /opt/mellanox/doca/{lib,include,samples,applications}.
From compatibility in the SKILL.md frontmatter.
Doca Pcc loads about 4.8k tokens when it runs. Until then it costs about 249 tokens; SKILL.md has 2,246 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from NVIDIA/skills at commit 0e0d506, republished under its Apache-2.0 licence (© NVIDIA). 2,246 words, ~4,795 tokens.
.claude/skills/doca-pcc/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.Where to start: This skill assumes DOCA is already installed,
the user's BlueField has a DPA processor that the host can see
through DOCA, the BlueField firmware has the custom-PCC slot
enabled, and the user is doing hands-on custom PCC work from
the host side — i.e. using doca-pcc to load a DPA-side
congestion control algorithm onto the BlueField, attach it to a
port handling RDMA / RoCE traffic, and parameterize it from the
host. Open TASKS.md if the user wants to do
something (configure / build / modify / run / test / debug);
open CAPABILITIES.md when the question is
what can the host-side PCC API express on this version + this
BlueField generation + this firmware. If the user has not
installed DOCA yet, route to
doca-setup first; if the user is
asking how to write the DPA-side congestion-control algorithm
itself (the code that runs on the DPA processor, compiled by
dpacc), that is a different scope — route via
doca-public-knowledge-map
to the public DOCA PCC programming guide and to
doca-dpa for the host-side DPA
lifecycle this skill builds on. If the user only wants to
inspect PCC counters at runtime without writing a custom
algorithm, that is the pcc_counters CLI tool — route via
doca-public-knowledge-map ## DOCA tools;
this skill is for custom algorithms only.
The CLASSES of PCC 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.
CAPABILITIES.md ## Capabilities and modesTASKS.md ## configure.doca_pcc_cap_* against the active
doca_devinfo must agree) in
CAPABILITIES.md ## Capabilities and modesTASKS.md ## configure step 1.DOCA_ERROR_NOT_PERMITTED
even though I have doca_dev access?" — worked example:
"the BlueField firmware in this host has the custom-PCC slot
disabled". Answered by the permission matrix in
CAPABILITIES.md ## Safety policyTASKS.md ## configure step 1.doca-pcc library the right tool for what I want,
or do I want the default firmware PCC or the
pcc_counters CLI?" — worked example: "I just want to
read PCC counters without touching the algorithm". Answered
by the path-selection rule in
CAPABILITIES.md ## Capabilities and modesCAPABILITIES.md ## Deferred topic boundaries
which route to
doca-public-knowledge-map ## DOCA tools
for the counter tool.CAPABILITIES.md ## Version compatibility
which cross-links the canonical detection chain in
doca-version and adds the
PCC-specific DOCA must match DPACC overlay inherited from
doca-dpa.DOCA_ERROR_* from a doca_pcc_* call mean
and which layer caused it?" — worked example:
"DOCA_ERROR_DRIVER on the host-side algorithm-load call —
is it DOCA, the firmware-side custom-PCC slot, or the DPACC-
produced image?". Answered by the PCC overlay on the
cross-library taxonomy in
CAPABILITIES.md ## Error taxonomyTASKS.md ## debug that escalates to
doca-debug.This skill serves external developers building applications
that consume the DOCA PCC library from the host side — i.e.,
users whose code calls doca_pcc_* from host C / C++ to stand
up the per-PCC-instance context, load a DPA-side PCC algorithm
image that dpacc produced from their DPA-side source, attach
it to the BlueField port that carries the RDMA / RoCE traffic
the algorithm is meant to control, parameterize the algorithm,
start the context, and observe runtime reports back from the
algorithm. It is not for NVIDIA developers contributing to
DOCA PCC itself, nor is it the place to learn how to write
the DPA-side congestion-control algorithm itself (that path
goes through the public DOCA PCC programming guide and the
companion DOCA DPA / DPACC guides via
doca-public-knowledge-map).
Language scope. DOCA PCC ships as a host-side C library
with pkg-config module name doca-pcc. The host-side API is
C; the DPA-side congestion-control algorithm is a separate
translation unit written in the language the DPACC compiler
accepts and compiled by dpacc into a binary that the host
packages into the executable as the PCC algorithm image. The
shipped samples under /opt/mellanox/doca/samples/doca_pcc/
are written in C plus DPA-side source (NVIDIA's choice).
Other-language consumers are limited in practice — the
DPA-side algorithm has no FFI escape hatch because it must be
a translation unit dpacc accepts — but a Rust / Go / Python
host-side wrapper that drives doca_pcc_* setup and loads a
PCC algorithm image built separately is still useful, and the
skill keeps the lifecycle, capability-discovery,
env-precondition, and error-taxonomy guidance language-neutral.
Load this skill when the user is doing hands-on DOCA PCC work
from the host side for a custom congestion control
algorithm, in any host language plus a DPA-side translation
unit built by dpacc. Concretely:
doca_pcc against a doca_dev that maps to
the BlueField port carrying the RDMA / RoCE traffic to be
controlled.doca_pcc_app) that dpacc
produced from the user's DPA-side congestion-control algorithm
source, into the doca_pcc context.doca_pcc Core
context so the algorithm begins affecting RDMA / RoCE
traffic on the attached port.doca_devinfo via the doca_pcc_cap_* family — BlueField
generations and firmware revisions differ in whether they
permit a custom PCC algorithm.DOCA_ERROR_* returned from a doca_pcc_* call
— in particular disambiguating firmware-level custom-PCC
slot disabled from device does not support custom PCC at
all from algorithm image incompatible with this device
from standard doca_dev access denied.dpacc — the env-precondition and capability-discovery rules
in this skill still apply.Do not load this skill for general DOCA orientation, install
of DOCA or the DPACC compiler, the DPA-side programming model
itself (how to write the congestion-control algorithm body that
runs on the DPA), questions about the default factory PCC
algorithm shipped in ConnectX firmware (no host-side doca-pcc
code needed — that path is firmware-only configuration), or
questions about the pcc_counters diagnostic CLI (route via
doca-public-knowledge-map ## DOCA tools).
For all of those, route through
doca-public-knowledge-map
to the matching upstream guide.
This is a thin loader. The body keeps only the orientation needed to pick the right next file. The substantive PCC-specific material lives in two companion files:
CAPABILITIES.md — what the host-side PCC API can express on
this version + this BlueField generation + this firmware: the
per-PCC-instance doca_pcc context, the loaded doca_pcc_app
algorithm image produced by dpacc, the attach-to-port
semantics that bind the algorithm to the RDMA / RoCE traffic
it will control, the capability-query surface
(doca_pcc_cap_*), the PCC error taxonomy mapped onto the
cross-library DOCA_ERROR_* set, the observability surface
(host-side reports plus the public PCC counter tool reachable
via doca-public-knowledge-map), and the safety policy that
gates env preconditions (DPA-capable BlueField, firmware-level
custom-PCC slot enabled, matched DOCA + DPACC versions,
algorithm image and host-side expectations agree).TASKS.md — step-by-step workflows for the six in-scope PCC
verbs: configure, build, modify, run, test,
debug. Plus a Deferred task verbs block that points
out-of-scope questions at the right next skill.The skill assumes a host where DOCA is already installed at
the standard location, a BlueField with a DPA processor is
physically present and visible to the host, the BlueField
firmware has the custom-PCC slot enabled, the DPACC compiler
is installed at a version matched to the DOCA install per the
DOCA Compatibility Policy, and the user already knows how
(at least at a sketch level) to write the DPA-side PCC
algorithm that dpacc will compile. It does not cover
installing DOCA, installing the DPACC compiler, or flipping
firmware-level configuration — those paths go through
doca-setup.
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:
/opt/mellanox/doca/samples/doca_pcc/. 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 in
doca-programming-guide,
layered with the PCC-specific overrides in
TASKS.md ## modify.meson.build,
CMakeLists.txt, …) parked inside the skill. The agent
constructs the build manifest in the user's project
directory against the user's installed DOCA + DPACC
compiler, where pkg-config --modversion doca-pcc and the
installed dpacc are the two sources of truth.samples/, bindings/, or reference/ 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.pcc_counters tool surface. That CLI is a separate
artifact (the real tool is the pcc_counters.sh script under
tools/pcc_counters/) with its own public page; routing for it lives in
doca-public-knowledge-map ## DOCA tools.
Conflating it with the doca-pcc library is the single most
common PCC first-app design error.SKILL.md first to confirm the user's question is
in scope (host-side custom PCC work, not DPA-side algorithm
design and not the counter tool).doca_pcc per-instance context, the loaded doca_pcc_app
algorithm image, the attach-to-port semantics, the
triple-axis precondition rule, the env-precondition policy,
the error taxonomy, the observability surface, and the
safety policy, see CAPABILITIES.md.Both companion files cross-link to each other,
doca-version for the canonical
DOCA version-handling rules (with the PCC overlay that DOCA
must match the DPACC compiler), and
doca-public-knowledge-map
whenever the right answer is "look it up in the public DOCA
PCC programming guide, the public DPA / DPACC guides, the
pcc_counters tool guide, or in the on-disk install
layout" rather than "PCC host-side-specific guidance".
doca-public-knowledge-map —
the routing table for every public DOCA documentation
source and the on-disk layout of an installed DOCA package.
The PCC public guide is at
https://docs.nvidia.com/doca/sdk/DOCA-PCC/index.html; the
pcc_counters diagnostic CLI lives under the DOCA Tools
umbrella as a companion surface rather than a redefined
artifact here.doca-dpa — the host-side DPA
control library that PCC depends on conceptually: the
custom PCC algorithm is DPA-side code, compiled by the
DPACC compiler, and the host-side doca-pcc Core lifecycle
follows the same shape as the doca-dpa Core lifecycle. The
agent loads doca-dpa alongside this skill when the user
has DPA-level questions (kernel-launch model, DPA-side
libraries) that PCC is layered on top of.doca-rdma — the library whose
traffic the custom PCC algorithm is controlling. The custom
PCC algorithm affects RDMA / RoCE flows on the attached
BlueField port; if the user has not yet set up RDMA / RoCE
traffic on that port, there is nothing for the algorithm to
act on, and doca-rdma is the skill that brings that
traffic up.doca-setup — env preparation,
install verification, DPACC compiler install / verification,
BlueField firmware configuration (including the custom-PCC
slot enable), and the I have no install yet path with the
public NGC DOCA container. This skill assumes its
preconditions are satisfied AND that DPACC is installed at a
version that matches DOCA AND that the firmware-level
custom-PCC slot is enabled.doca-version — canonical
DOCA version-handling rules. This skill's ## Version compatibility cross-links the four-way match rule and adds
the PCC-specific DOCA-and-DPACC must match overlay per the
DOCA Compatibility Policy (inherited from
doca-dpa).doca-structured-tools-contract —
the bundle's structured-tools precedence rule (detect /
prefer / fall back / report). The Command appendix in
TASKS.md honors this contract.doca-programming-guide —
general DOCA programming patterns shared by every library:
the canonical pkg-config + meson build pattern, the
universal modify-a-shipped-sample first-app workflow, the
universal Core-context lifecycle, the cross-library
DOCA_ERROR_* taxonomy, and the program-side debug order.
This skill layers PCC specifics on top.doca-debug — the cross-cutting
debug ladder (install / version / build / link / runtime /
program / driver). PCC-specific debug (custom-PCC slot not
enabled in firmware, DPACC + DOCA version skew, algorithm
image rejected as incompatible with the device, traffic on
the attached port not being affected because the algorithm
body has no effect path) overlays on top of that ladder.The default factory PCC algorithms shipped inside ConnectX
firmware are not in scope for this skill — those work
without doca-pcc and are configured through firmware-level
knobs, not through any host-side library API. The
pcc_counters diagnostic CLI is also not in scope —
it is a separate artifact for inspecting runtime PCC
counters and lives under the public DOCA Tools umbrella.
Conflating either of those with the doca-pcc library is the
single most common PCC first-app design error.
© NVIDIA, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 7 other files in skills/doca-pcc of NVIDIA/skills.
Open the folder on GitHubat commit 0e0d506
Doca Pcc next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Doca Pcc this skillNVIDIA/skills | 3.5k | — | ~4.8k | Automated safety check: Pass | Apache-2.0 | |
| Finishing a Development Branchobra/superpowers | 296k | 5 repos | ~1.9k | Automated safety check: Pass | MIT | |
| Typescript Advanced Typesrolling-scopes/rsschool-app | 10k | 24 repos | ~4.2k | Automated safety check: Pass | MPL-2.0 | |
| PR Babysitteropeninterpreter/openinterpreter | 69k | 3 repos | ~4.2k | Automated safety check: Pass | Apache-2.0 | |
| Code Review ChecklistshareAI-lab/learn-claude-code | 78k | 5 repos | ~1.1k | Automated safety check: Pass | MIT | |
| Greplooponyx-dot-app/onyx | 32k | 4 repos | ~3.3k | Automated safety check: Pass | MIT |
obra/superpowers
Walks the last step of a branch: confirm tests pass, detect the git environment, ask how to integrate, carry out your choice and clean up the worktree.
rolling-scopes/rsschool-app
Master TypeScript's advanced type system including generics, conditional types, mapped types, template literals, and utility types for building type-safe applications.
openinterpreter/openinterpreter
Watches an open GitHub pull request until it merges, handling review comments, diagnosing CI failures and retrying flaky checks along the way.
shareAI-lab/learn-claude-code
Reviews code against a five-part checklist covering security, correctness, performance, maintainability and testing, and reports findings in a fixed format.
onyx-dot-app/onyx
Iteratively improves a PR (GitHub), MR (GitLab), or shelved changelist (Perforce) until Greptile gives it a 5/5 confidence score with zero unresolved comments.
akash-network/node
Behavioral guidelines to reduce common LLM coding mistakes. An agent skill from akash-network/node.
NVIDIA/skills
A skill your agent uses when the user wants to deploy, run, debug, tear down, or call the REST API of the RTVI-CV 2D detection / tracking microservice.
NVIDIA/skills
Generates, validates, compares and explains HOLOLINK_def.svh macro files for the HSB IP, using bundled Python scripts and asking before it writes anything.
NVIDIA/skills
Runs and validates an end-to-end Mission Control demo in a locally installed Isaac Sim, with a Nova Carter robot driven through a Python server.
NVIDIA/skills
Orchestrates defect image generation for PCBA, metal surface and glass inspection with NVIDIA Cosmos AnomalyGen on OSMO, from cold-start Day 0 to real-photo Day 1 labeling.
NVIDIA/skills
Orchestrates video data augmentation and auto-labeling workflows on OSMO, from flow selection and preflight checks to submission, monitoring and output download.
NVIDIA/skills
Runs NVIDIA TAO Data Services KPI analysis on object detection results, comparing predictions to ground truth and writing per-class precision, recall and AP to a CSV.
Categories
A skill your agent uses when the user is doing hands-on host-side DOCA PCC work to load a CUSTOM Programmable Congestion Control algorithm onto a BlueField DPU — creating per-port docapcc contexts…. Doca Pcc is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Use this skill when the user is doing hands-on host-side DOCA PCC work to load a CUSTOM Programmable Congestion Control algorithm onto a BlueField DPU — creating per-port docapcc contexts, loading a dpacc-compiled docapccapp onto the docadev for the RoCE-bearing port, parameterizing it, walking triple-axis capability discovery (DOCA cap-query + DPA-capable BlueField + firmware custom-PCC slot enabled), or debugging DOCAERROR from docapcc.
Doca Pcc fits situations like: loading a dpacc-compiled docapccapp onto the docadev for the RoCE-bearing port; parameterizing it; walking triple-axis capability discovery (DOCA cap-query + DPA-capable BlueField + firmware custom-PCC slot enabled); debugging DOCAERROR from docapcc.
Run `npx skills add NVIDIA/skills --skill doca-pcc -a claude-code`. Or copy the skill folder (skills/doca-pcc in NVIDIA/skills) into .claude/skills/doca-pcc in your project. Claude Code loads it when a task matches its description.
Run `npx skills add NVIDIA/skills --skill doca-pcc -a codex`. Or copy the skill folder (skills/doca-pcc in NVIDIA/skills) into .agents/skills/doca-pcc in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add NVIDIA/skills --skill doca-pcc -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/doca-pcc, .gemini/skills/doca-pcc, .github/skills/doca-pcc and .opencode/skills/doca-pcc in your project.
SKILL.md names no scripts, command-line tools or credentials: Doca Pcc is instructions for the agent only. Our summary lists: Python 3. Compatibility (from SKILL.md): Requires DOCA SDK installed at /opt/mellanox/doca on Linux (Ubuntu 22.04/24.04 or RHEL/SLES) with a BlueField DPU whose DPA processor is exposed to the host AND whose firmware has the custom-PCC slot enabled. Also requires the DPACC compiler installed at a version matched to DOCA per the DOCA Compatibility Policy. Reads the user's local install via `pkg-config doca-pcc` and inspects /opt/mellanox/doca/{lib,include,samples,applications}. .
SKILL.md names 1 domain. As links in the text: docs.nvidia.com. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Doca Pcc is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
About 4.8k tokens (SKILL.md is roughly 19k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Doca Pcc: Finishing a Development Branch (obra/superpowers, 296k stars), Typescript Advanced Types (rolling-scopes/rsschool-app, 10k stars), PR Babysitter (openinterpreter/openinterpreter, 69k stars) and Code Review Checklist (shareAI-lab/learn-claude-code, 78k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/skills, which has 3,534 GitHub stars. The repository holds 380 skills in this directory. The repository was last updated on October 7, 2026.
Source: NVIDIA/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.