Hns Moaiadk Dev Reference
modu-ai/moai-adk
moai-adk-go local dev reference — version management/release process (sec 5), shell-script hook development (sec 7), build & dev commands (sec 10).
A skill your agent uses when a quest needs to attach, import, reproduce, repair, verify, compare, or publish a baseline and its metrics.
$ npx skills add OpenLAIR/dr-claw --skill ds-baseline -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install OpenLAIR/dr-claw ds-baseline --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/OpenLAIR/dr-claw.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/ds-baseline .claude/skills/ds-baseline && 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 "ds-baseline" agent skill from https://github.com/OpenLAIR/dr-claw/tree/main/skills/ds-baseline into .claude/skills/ds-baseline/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ds-baseline", 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/OpenLAIR/dr-claw/tree/main/skills/ds-baselineType 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 OpenLAIR/dr-claw --skill ds-baseline -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install OpenLAIR/dr-claw ds-baseline --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/OpenLAIR/dr-claw.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/ds-baseline .agents/skills/ds-baseline && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "ds-baseline" agent skill from https://github.com/OpenLAIR/dr-claw/tree/main/skills/ds-baseline into .agents/skills/ds-baseline/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ds-baseline", 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 OpenLAIR/dr-claw --skill ds-baseline -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install OpenLAIR/dr-claw ds-baseline --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/OpenLAIR/dr-claw.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/ds-baseline .cursor/skills/ds-baseline && 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 "ds-baseline" agent skill from https://github.com/OpenLAIR/dr-claw/tree/main/skills/ds-baseline into .cursor/skills/ds-baseline/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ds-baseline", 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/OpenLAIR/dr-claw.git --path skills/ds-baseline--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 OpenLAIR/dr-claw --skill ds-baseline -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install OpenLAIR/dr-claw ds-baseline --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/OpenLAIR/dr-claw.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/ds-baseline .gemini/skills/ds-baseline && 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 "ds-baseline" agent skill from https://github.com/OpenLAIR/dr-claw/tree/main/skills/ds-baseline into .gemini/skills/ds-baseline/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ds-baseline", 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 OpenLAIR/dr-claw ds-baselineInstalls 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 OpenLAIR/dr-claw --skill ds-baseline -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/OpenLAIR/dr-claw.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/ds-baseline .github/skills/ds-baseline && 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 "ds-baseline" agent skill from https://github.com/OpenLAIR/dr-claw/tree/main/skills/ds-baseline into .github/skills/ds-baseline/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ds-baseline", 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 OpenLAIR/dr-claw --skill ds-baseline -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install OpenLAIR/dr-claw ds-baseline --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/OpenLAIR/dr-claw.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/ds-baseline .opencode/skills/ds-baseline && 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 "ds-baseline" agent skill from https://github.com/OpenLAIR/dr-claw/tree/main/skills/ds-baseline into .opencode/skills/ds-baseline/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ds-baseline", 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.
ds-baselineA skill your agent uses when a quest needs to attach, import, reproduce, repair, verify, compare, or publish a baseline and its metrics.
Ds Baseline is an agent skill from OpenLAIR/dr-claw. Use when a quest needs to attach, import, reproduce, repair, verify, compare, or publish a baseline and its metrics.
Its SKILL.md is about 6.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 9 other files, including reference files (for example `references/artifact-payload-examples.md`, `references/baseline-checklist-template.md` and `references/baseline-plan-template.md`).
It sits in Research & Science. It works with Git, Bash and Python. The repository describes itself as: A Super AI Lab with massive AI Doctors as Assistants. Best IDE for Research via AI Power. The licence is MIT.
Read from SKILL.md and the folder at commit d51b64e. 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.
Shell commands in SKILL.md call:
uvFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md. Its commands use uv, which can reach the network depending on how they are called.
From 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.
Ds Baseline loads about 6.9k tokens when it runs, and up to ~9.2k if it reads all its reference files. Until then it costs about 32 tokens; SKILL.md has 3,595 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 OpenLAIR/dr-claw at commit d51b64e, republished under its MIT licence (© OpenLAIR). 3,595 words, ~6,854 tokens.
.claude/skills/ds-baseline/SKILL.md (or your agent's skills folder). This skill also uses 8 other files; get the full folder from GitHub.This skill establishes the reference system the quest will compare against. The target is one trustworthy baseline line, not an endless reproduction diary.
bash_exec; do not use any other terminal path for setup, reproduction, monitoring, verification, Git, Python, package-manager, or file-inspection commands.bash_exec for setup, reproduction, monitoring, and verification commands so the baseline line stays durable and auditable.shell_command / command_execution in this skill.bash_exec(...).artifact.git(...) before raw shell git commands.bash_exec(...) in an isolated scratch repository.artifact.arxiv(paper_id=..., full_text=False) for actually reading a source arXiv paper when it existsfull_text=True only when the short form is insufficientuvThe baseline stage should produce a usable reference point through one of four routes:
Keep the classic control flow:
These are control gates, not paperwork walls.
PLAN.md and CHECKLIST.md; short-form files are enough for simple fast-path work.1-2 sentence summary of trust status and next anchor.Default to the lightest baseline path that can still establish a trustworthy comparison. Default to a fast path when it can establish trust with less work.
Fast path is the default when any of the following is true:
requested_baseline_ref or confirmed_baseline_ref already points to the active baseline objectattach or importFast path means:
PLAN.md, a minimal CHECKLIST.md, one bounded smoke test when needed, and then one real validation or runEscalate from fast path to fuller audit only when:
Do not proceed to comparison-heavy downstream work unless one of the following is durably true:
Operationally:
artifact.confirm_baseline(...) once the accepted baseline root and trusted comparison contract are clearartifact.waive_baseline(...) when the quest must continue without a baselineBefore substantial baseline setup, code edits, or a real baseline run, create a quest-visible PLAN.md and CHECKLIST.md.
references/baseline-plan-template.md as the canonical structure for PLAN.md.references/baseline-checklist-template.md as the canonical structure for CHECKLIST.md.analysis_plan.md and REPRO_CHECKLIST.md remain acceptable compatibility alias files when an older quest already depends on them.PLAN.md and CHECKLIST.md are enough.PLAN.md before continuing.Default retry discipline:
repair, record blocked, or route through decisionThe baseline stage should usually leave behind:
baselines/local/ or baselines/imported/PLAN.md and CHECKLIST.mdartifact.confirm_baseline(...), or an explicit waiver via artifact.waive_baseline(...)For simple attach/import flows or a straightforward reproduce flow, do not stall just to precreate every optional note file.
Useful optional notes:
setup.mdexecution.mdverification.mdSTRUCTURE.md when the layout is non-obviousPLAN.md or compatibility alias analysis_plan.md is the required route contract before substantial setup, code edits, or a real run; it should state the route, source identity, command path, expected outputs, acceptance condition, main risks, and fallback.CHECKLIST.md or compatibility alias REPRO_CHECKLIST.md is the required living state tracker; it should show whether the baseline object, smoke decision, real run decision, and final accept / block / waive outcome are explicit.setup.md is optional unless environment or layout choices are non-trivial; if used, record the working directory, environment route, important config paths, source revision, and notable setup deviations.execution.md is optional unless the run is long, multi-step, or rerun-heavy; if used, record the launched commands, durable log paths, checkpoints, exit state, and any reruns or repairs.verification.md is optional as a filename but required in substance before acceptance or blocked closeout; either this file or an equivalent report should record trusted metrics, expected-versus-observed comparison, caveats, canonical output paths, and the next anchor.STRUCTURE.md becomes required when the workspace layout, mounts, symlinks, or generated outputs are non-obvious or meant for reuse; it should map the important directories and say which paths are canonical.attachment.yaml is required for attached or imported baselines under baselines/imported/; preserve source identity, selected variant when relevant, and attachment provenance there.<baseline_root>/json/metric_contract.json is the canonical accepted comparison contract; once the baseline is accepted, do not leave the authoritative metric surface only in chat, memory, or prose.Result/metric.md is scratch-only; it may help during execution, but it is never the final source of truth.Minimum stability rules:
Use the real runtime paths consistently.
Quest-local paths:
<quest_root>/baselines/local/<baseline_id>/<quest_root>/baselines/imported/<baseline_id>/<quest_root>/baselines/imported/<baseline_id>/attachment.yaml<baseline_root>/json/metric_contract.json<quest_root>/artifacts/baselines/<artifact_id>.json<quest_root>/artifacts/reports/<artifact_id>.jsonquest.yaml -> confirmed_baseline_refGlobal reusable registry paths:
~/DeepScientist/config/baselines/index.jsonl~/DeepScientist/config/baselines/entries/<baseline_id>.yamlbaseline_id should be short, stable, and filesystem-safe., _, or -/, \\, or ..baseline_id with structured variants instead of inventing many near-duplicate entriesdefault_variant_id, baseline_variants, and per-variant metric summaries stable enough that later experiment and write stages can cite them directlyDo not invent parallel durable locations when these runtime contracts already exist. Do not leave the authoritative metric contract only in chat, memory, or prose once the baseline is accepted.
If a baseline is reproduced only because an analysis campaign needs an extra comparator:
artifact.confirm_baseline(...) for that supplementary case unless the quest truly intends to replace the canonical baselineOne quest may legitimately need more than one baseline.
Prefer this order:
Prefer reuse over redundant reproduction.
Before running anything substantial, determine:
Default analysis discipline:
requested_baseline_ref or confirmed_baseline_ref, validate that concrete object before restarting broad discoveryEscalate to a fuller audit only when the command path is unclear, the repo is large or confusing, repair mode is active, or custom code changes look likely.
When the fuller audit is necessary, capture only what later stages truly need:
If the source paper is available, record:
You may inspect local feasibility with shell-based checks for OS, GPU, CPU, RAM, disk, Python version, and whether uv is available.
The analysis phase should leave behind a concrete plan rather than only conversational intent.
Prepare the selected route:
For Python baselines, standardize environment setup around uv.
uvuv.lock or a solid pyproject.toml, use uv syncuv venvuv pip install ...uv run ...Practical rules:
.venvuv run python ... or uv run bash ... over relying on shell activation stateuv venv --python 3.11 or uv run --python 3.11 ...uv pipuv route when there is a concrete blocker that cannot be resolved locallyCommon uv patterns:
uv syncuv venv --python 3.11uv pip install -r requirements.txtuv run python scripts/smoke_test.pyuv run python train.py --config ...Setup should record:
uv route and Python versionFallbacks:
analysis_plan.md or REPRO_CHECKLIST.md, keep the compatibility alias explicit rather than splitting truth across two active plansRun only the work required to establish the baseline credibly.
Execution rules:
Long-running execution discipline:
bash_exec(mode='detach', ...)bash_exec(mode='history') or bash_exec(mode='list')bash_exec(mode='read', id=...) returns the full saved log when it is 2000 lines or fewer; for longer logs, inspect omitted middle windows with start and tailbash_exec(mode='read', id=..., tail_limit=..., order='desc'), and after the first read prefer incremental checks with after_seq=last_seen_seqsilent_seconds, progress_age_seconds, signal_age_seconds, and watchdog_overdue as the default staleness cluesbash_exec(mode='kill', id=..., wait=true, timeout_seconds=...), document why, and relaunch cleanly30-minute visibility bound pass without a real inspection and a next expected update timetqdm progress reporter and periodic __DS_PROGRESS__ markers when feasibleKeep retries bounded:
Verification is mandatory before baseline acceptance.
Verify:
Classify the outcome as one of:
verified_matchverified_closeverified_divergedbrokenVerification must explicitly separate:
Verification should answer:
A verification report should be self-contained enough that a later stage can answer:
The baseline stage is not complete just because something ran. It is complete when later stages can compare against it fairly.
Before declaring a baseline usable, make the comparability contract explicit:
Unless the user explicitly specifies otherwise, treat the original paper's evaluation protocol as the canonical baseline contract.
If any of these fields are still materially unknown, do not pretend the baseline is a clean downstream reference.
For the fuller checklist and verdict meanings, read references/comparability-contract.md.
Before acceptance, classify feasibility as one of:
full_reproducibledegraded_but_acceptableblockedAnd classify downstream trust as one of:
verifiedpartially_verifiedoperational_but_incomparablefailedDo not silently upgrade a degraded or merely operational result into a normal trusted baseline.
The accepted baseline artifact should include at least:
baseline_idbaseline_kindpathtaskdatasetprimary_metricmetrics_summaryenvironmentsourcesummaryIf variants exist, also include:
default_variant_idbaseline_variantsMetric-contract rules:
<baseline_root>/json/metric_contract.jsonprimary_metric as the headline metric only; do not let it erase the rest of the comparison surfacemetrics_summary as a flat top-level dictionary keyed by the paper-facing metric idsdescription, either derivation or origin_path, and source_refmetrics_summary plus structured rows rather than one cherry-picked scalarjson/metric_contract.json, reuse that richer contract instead of hand-writing a thinner one that keeps only one averaged scalarResult/metric.md is optional temporary scratch memory only; reconcile against it before calling artifact.confirm_baseline(...), but do not treat it as a required durable fileUse the registry deliberately, not as an afterthought.
If the result is reusable beyond the current quest:
artifact.publish_baseline(...)publish_global: true only when verification is complete and reuse is justifiedIf the current quest should reuse an existing baseline:
artifact.attach_baseline(...)baseline_idvariant_id when one is usedbaselines/imported/If runtime state already includes requested_baseline_ref or a matching confirmed_baseline_ref:
baselineFor a clearer attach/import/reproduce/repair rubric, read references/route-selection.md.
For reusable-package expectations, read references/publishable-baseline-package.md.
Stage-start requirement:
memory.list_recent(scope='quest', limit=5)memory.search(...) before new baseline analysis, repair, or rerun workrequested_baseline_ref or confirmed_baseline_ref and the immediate task is only to validate or reattach that concrete baseline, you may skip broad retrievalWrite memory only for reusable lessons such as:
When calling memory.write(...), pass tags as an array like ["stage:baseline", "baseline:<baseline_id>", "type:repro-lesson"], not as one comma-joined string.
Stage-end requirement:
memory.write(...) before leaving the stageTypical artifact sequence:
progress for long-running setup or execution checkpointsreport for analysis notes or verification notesdecision for route choice, blocked routing, or accept/reject/rerun/repair callsbaseline only for an accepted baseline recordFor stable field shapes, read references/artifact-payload-examples.md.
The baseline handoff should make these items obvious:
baseline_idbaseline_variant_id when relevantIf this packet is not obvious from the accepted artifact plus verification note, the baseline line is not stable enough yet.
Do not hide failures.
If blocked, record the class explicitly:
missing_sourcemissing_codemissing_metric_contractenvironment_infeasiblecommand_unknownrun_failedverification_failedA blocked result must state:
Reasonable autonomous fixes before escalation:
If a fix would change confirmed scope, metrics, permissions, or resource assumptions, stop and return to analysis rather than applying it silently.
Exit the baseline stage once one of the following is durably true:
Typical next anchors:
ideaexperiment in tightly scoped follow-on casesdecision if the baseline line remains contested© OpenLAIR, MIT. 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 8 other files (references) in skills/ds-baseline of OpenLAIR/dr-claw.
Open the folder on GitHubat commit d51b64e
Ds Baseline 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 |
|---|---|---|---|---|---|---|
| Ds Baseline this skillOpenLAIR/dr-claw | 1.2k | — | ~6.9k | Automated safety check: Pass | MIT | |
| Hns Moaiadk Dev Referencemodu-ai/moai-adk | 1.2k | — | ~937 | Automated safety check: Pass | Apache-2.0 | |
| Setup MedsciAperivue/medsci-skills | 331 | — | ~960 | Automated safety check: Pass | MIT | |
| Light Project StructureLight0305/Light-skills | 640 | — | ~3k | Automated safety check: Notes | MIT | |
| Asksd0xdev/sd0x-harness | 192 | — | ~2.1k | Automated safety check: Notes | MIT | |
| Code Review ChecklistshareAI-lab/learn-claude-code | 78k | 5 repos | ~1.1k | Automated safety check: Pass | MIT |
modu-ai/moai-adk
moai-adk-go local dev reference — version management/release process (sec 5), shell-script hook development (sec 7), build & dev commands (sec 10).
Aperivue/medsci-skills
A skill your agent uses when a skill fails for a missing tool or the environment needs checking.
Light0305/Light-skills
Audits, scaffolds and safely migrates research project folder structures, keeping existing repositories read-only until you approve exact moves from a plan.
sd0xdev/sd0x-harness
Context-aware Q&A with auto context gathering. An agent skill from sd0xdev/sd0x-harness.
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.
bytedance/deer-flow
Researches a GitHub repository over four rounds using the GitHub API and web search, then writes a structured markdown report with timeline, metrics and Mermaid diagrams.
OpenLAIR/dr-claw
Analyzes reviewer comments and drafts venue-specific rebuttals for AI and computer science conferences, with an issue board, task list and paper edit plan.
OpenLAIR/dr-claw
Turns a research paper into a slide deck and, optionally, a narrated demo video, through script, slide generation, text-to-speech and video assembly stages you control.
OpenLAIR/dr-claw
Clusters the latest news-feed results by topic and writes a briefing of research idea seeds with citations, plus a structured seeds file, without crawling new sources.
OpenLAIR/dr-claw
Searches Hugging Face Hub, OpenML, GitHub and paper references for datasets that fit a research task and returns a ranked, de-duplicated table.
OpenLAIR/dr-claw
Runs multi-source web research through Google's Gemini Deep Research Agent with a bundled Python script and saves a structured, cited report as files.
OpenLAIR/dr-claw
Six-phase workflow for writing, revising and adapting grant proposals for NSF, NIH, DOE, DARPA, NASA and China's NSFC, from profiling through simulated peer review.
Categories
A skill your agent uses when a quest needs to attach, import, reproduce, repair, verify, compare, or publish a baseline and its metrics. Ds Baseline is an agent skill from OpenLAIR/dr-claw. Use when a quest needs to attach, import, reproduce, repair, verify, compare, or publish a baseline and its metrics.
Ds Baseline fits situations like: A quest needs to attach; publish a baseline and its metrics.
Run `npx skills add OpenLAIR/dr-claw --skill ds-baseline -a claude-code`. Or copy the skill folder (skills/ds-baseline in OpenLAIR/dr-claw) into .claude/skills/ds-baseline in your project. Claude Code loads it when a task matches its description.
Run `npx skills add OpenLAIR/dr-claw --skill ds-baseline -a codex`. Or copy the skill folder (skills/ds-baseline in OpenLAIR/dr-claw) into .agents/skills/ds-baseline 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 OpenLAIR/dr-claw --skill ds-baseline -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ds-baseline, .gemini/skills/ds-baseline, .github/skills/ds-baseline and .opencode/skills/ds-baseline in your project.
Going by SKILL.md and its folder, Ds Baseline needs the command-line tools its instructions call (uv). Our summary lists: Python 3.
SKILL.md contains no URLs. Its commands use uv, which can reach the network depending on how they are called. 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.
Ds Baseline is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
About 6.9k tokens (SKILL.md is roughly 27k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.4k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Ds Baseline: Hns Moaiadk Dev Reference (modu-ai/moai-adk, 1.2k stars), Setup Medsci (Aperivue/medsci-skills, 331 stars), Light Project Structure (Light0305/Light-skills, 640 stars) and Ask (sd0xdev/sd0x-harness, 192 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
OpenLAIR (a GitHub organization) maintains it in OpenLAIR/dr-claw, which has 1,155 GitHub stars. The repository holds 36 skills in this directory. The repository was last updated on September 17, 2026.
Source: OpenLAIR/dr-claw on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.