MCP Server Builder
anthropics/skills
Guides the design and implementation of Model Context Protocol servers in TypeScript or Python, from tool naming and error messages to evaluation.
Operates or analyzes a LoopX-managed benchmark experiment: launching runs, maintaining the experiment board, qualifying integrity, and writing case insights.
$ npx skills add loopx-project/loopx --skill loopx-benchmark -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install loopx-project/loopx loopx-benchmark --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/loopx-project/loopx.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/loopx-benchmark .claude/skills/loopx-benchmark && 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 "loopx-benchmark" agent skill from https://github.com/loopx-project/loopx/tree/main/skills/loopx-benchmark into .claude/skills/loopx-benchmark/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "loopx-benchmark", 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/loopx-project/loopx/tree/main/skills/loopx-benchmarkType 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 loopx-project/loopx --skill loopx-benchmark -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install loopx-project/loopx loopx-benchmark --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/loopx-project/loopx.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/loopx-benchmark .agents/skills/loopx-benchmark && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "loopx-benchmark" agent skill from https://github.com/loopx-project/loopx/tree/main/skills/loopx-benchmark into .agents/skills/loopx-benchmark/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "loopx-benchmark", 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 loopx-project/loopx --skill loopx-benchmark -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install loopx-project/loopx loopx-benchmark --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/loopx-project/loopx.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/loopx-benchmark .cursor/skills/loopx-benchmark && 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 "loopx-benchmark" agent skill from https://github.com/loopx-project/loopx/tree/main/skills/loopx-benchmark into .cursor/skills/loopx-benchmark/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "loopx-benchmark", 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/loopx-project/loopx.git --path skills/loopx-benchmark--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 loopx-project/loopx --skill loopx-benchmark -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install loopx-project/loopx loopx-benchmark --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/loopx-project/loopx.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/loopx-benchmark .gemini/skills/loopx-benchmark && 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 "loopx-benchmark" agent skill from https://github.com/loopx-project/loopx/tree/main/skills/loopx-benchmark into .gemini/skills/loopx-benchmark/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "loopx-benchmark", 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 loopx-project/loopx loopx-benchmarkInstalls 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 loopx-project/loopx --skill loopx-benchmark -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/loopx-project/loopx.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/loopx-benchmark .github/skills/loopx-benchmark && 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 "loopx-benchmark" agent skill from https://github.com/loopx-project/loopx/tree/main/skills/loopx-benchmark into .github/skills/loopx-benchmark/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "loopx-benchmark", 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 loopx-project/loopx --skill loopx-benchmark -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install loopx-project/loopx loopx-benchmark --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/loopx-project/loopx.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/loopx-benchmark .opencode/skills/loopx-benchmark && 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 "loopx-benchmark" agent skill from https://github.com/loopx-project/loopx/tree/main/skills/loopx-benchmark into .opencode/skills/loopx-benchmark/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "loopx-benchmark", 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.
loopx-benchmarkOperates or analyzes a LoopX-managed benchmark experiment: launching runs, maintaining the experiment board, qualifying integrity, and writing case insights.
This skill only applies to actual run management or post-run analysis, not to a solver simply completing an assigned benchmark task under its own execution contract, even if that task mentions benchmark or evaluation. Installing the skill alone grants no runner, shell, network, credential, or Goal-mutation authority beyond what the current todo and host already allow.
It exposes its capability surface through a JSON catalog entry and a set of benchmark subcommands for showing or updating the experiment board, fencing a source revision, qualifying integrity, and classifying artifacts, and shares study data with other benchmark developers through a typed, validated manifest and upload-envelope flow rather than handing over a raw runner-specific ledger.
5 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 8205c8b. 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 (its code samples are bash).
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
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.
LoopX Benchmark Operator loads about 3.6k tokens when it runs. Until then it costs about 108 tokens; SKILL.md has 1,637 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 loopx-project/loopx at commit 8205c8b, republished under its Apache-2.0 licence (© loopx-project). 1,637 words, ~3,645 tokens.
.claude/skills/loopx-benchmark/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.Use this skill to operate or analyze a LoopX-managed benchmark experiment. The builtin
benchmark-toolkit capability owns provider-neutral experiment state and
integrity boundaries. This packaged skill is its task-triggered Agent playbook.
An assigned solver follows its task instructions and current execution contract. The words “benchmark”, “evaluation”, or “submission” in that task do not grant the operator role or require experiment-board discovery. Use this workflow when the requested work actually includes run management or post-run analysis; an explicit request to use the skill still applies. Keep the solver's task-local validation and authorized submission path distinct from experiment management.
The capability is catalog-ready without a per-Goal enable switch. Installing this skill does not grant runner, shell, network, credential, private-evidence, or Goal mutation authority. Respect the selected todo's required capabilities, any external provider binding, host permissions, and user gates.
loopx capability show benchmark-toolkit --format json — catalog entry with
usage hints, role boundaries, and the post-run case-insight template.loopx benchmark --help — subcommands (experiment-board-show,
experiment-board-upsert, source-revision-fence, integrity-qualification,
classify-artifacts).When another benchmark developer needs portable study data, use the capability's typed study flow rather than sharing a runner-specific ledger or raw evidence:
benchmark_study_manifest_v0 with benchmark study-validate.benchmark upload-envelope.benchmark upload-local without --execute first, then explicitly execute
against a caller-selected local JSONL store.benchmark upload-readback.benchmark study-dashboard; pass a
compact four-arm contract only when the study preregistered that design.For case_insight_projection, first upload the same run's active terminal
experiment-board row with insight.status=complete. The case, run, and outcome
must match; the run row remains the only arm, score, countability, integrity, and
treatment-fidelity authority. Reduce private post-run evidence to bounded prose
and public-safe handles or digests before building the envelope.
The local provider is a no-network simulation. It does not grant remote upload, publication, credentials, retention, or benchmark submission authority. Adapters keep their native metric names and reduce private post-run evidence before envelope construction.
When the owner authorizes selected behavioral observations but not a complete
study release, use behavior_finding records and benchmark behavior-report.
See docs/reference/benchmark-behavior-findings.md for the contract. These records
require selection rules, sample denominators, observations, interpretations,
limitations, counterevidence, and evidence digests; they require neither a run-row
upload nor a full study manifest and have no score authority.
Freeze the authorized disclosure projection before rendering. Review the same scope in visible text, foldouts, embedded data, downloads, and PR attachments. Permission to share duration does not grant permission to share outcome totals or deltas. Schema validity and a producer redaction attestation are not publication approval or verification of unshared evidence. Keep selected-case observations explicitly exploratory and retain the relevant limitations and counterexamples.
capability show and benchmark --help; remain
read-only. Do not create an experiment-board row merely because the user asks
what the toolkit does.For a generic library microbenchmark or an eval with no LoopX Goal/board, use the task's normal tools instead of imposing this workflow.
Read the experiment board before launching or selecting a case.
loopx benchmark experiment-board-show --goal-id <GOAL_ID> --format jsonInspect baseline, treatment, explore, countability, effort, and insight rows before choosing the next arm.
Qualify the source revision before each new run admission.
loopx benchmark source-revision-fence \
--source-checkout <clean-source> \
--expected-revision <PIN> \
--observed-reference-revision <OBSERVED_HEAD> \
--require-admitted --format jsonThe fence fails closed unless the clean pinned source matches the observed reference head.
Preview, then preregister or mark the run row when it starts.
loopx benchmark experiment-board-upsert --goal-id <GOAL_ID> \
--row-json <running-row.json> --format json
loopx benchmark experiment-board-upsert --goal-id <GOAL_ID> \
--row-json <running-row.json> --execute --format jsonThe running row uses status=running, empty metrics, and
countability={integrity_qualified:false, official_result_present:false, score_countable:false}. Keep the same stable run_id for every transition.
Preview and upsert terminal score, countability, effort, and insight.
First run integrity qualification. An automated restricted-access match is a
countable suspicion, not a cheating verdict. After solver and scoring are
terminal, inspect the real solver trajectory, tool results, and final
workspace. Pass a compact
benchmark_restricted_access_adjudication_v0 only after that review; confirm
cheating only when restricted material was actually disclosed and causally
entered a solving or validation decision.
loopx benchmark experiment-board-upsert --goal-id <GOAL_ID> \
--row-json <terminal-row.json> --execute --format jsonThe terminal row sets status=completed, fills metrics (primary metric plus
guardrails), and updates countability. Only mark score_countable=true when
integrity_qualified=true and official_result_present=true. Fill effort
and set insight.status to complete after the post-run analysis.
For non-baseline arms, also reduce the reviewed mechanism facts separately:
loopx benchmark treatment-continuation-receipt \
--observation-json <compact-post-run-observation.json> --format jsonThis receipt distinguishes qualified startup from post-start semantic control persistence. It is analysis-only and must not change score countability, integrity qualification, treatment fidelity, or matched-pair eligibility.
Read matched comparisons before selecting the next arm.
loopx benchmark experiment-board-show --goal-id <GOAL_ID> --format jsonOnly claim paired results from matched_pair_countable comparisons. Keep
diagnostic-only explore rows in a separate evidence lane.
benchmark_id, study_id, case_id, run_id, arm_id, arm_role,
attempt, status, observed_at, model_id, protocol_id,
comparison_protocol_id, claim_scope, primary_metric,
guardrail_metrics, metrics, countability, treatment_fidelity,
effort, insight are the canonical row fields (schema_version =
benchmark_experiment_board_row_v0).treatment_fidelity=not_applicable and cannot name a
comparison_anchor_run_id. Non-baseline rows must name a
comparison_anchor_run_id.{"name": {"value": <number>, "unit": <str>, "higher_is_better": <bool>}}; at most 16 entries. The primary_metric must
not also be a guardrail metric.score_countable requires status=completed, integrity_qualified=true,
and official_result_present=true. score=0 is a valid completed result.source-revision-fence is read-only and caller-observed: it performs no
fetch, install, or launch. It blocks new admissions only.integrity-qualification reduces private trajectory and runner isolation
evidence to a compact public-safe receipt (hashes, counts, reason codes).restricted_access_review=suspected while keeping the run score-eligible. Use
--restricted-access-adjudication-json for the post-run agent decision; only
confirmed disclosure plus causal use disqualifies the score.classify-artifacts classifies benchmark artifact paths without reading them;
use it before reading or publishing any candidate artifact.capability bind selects an external provider implementation for a Goal; it
is not the activation mechanism for this builtin capability. Todo
required_capability fields remain runtime prerequisites, not product
capability switches.When the user wants a persistent experiment overview, keep a stable per-task entry point backed by an explicit maintained selection, rather than sending a new long run-query URL after every restart. Keep task switching and full history one interaction away. Use the existing board/runtime projections for run state and the provider's authorized score projection; a display selection must not become a second source of score, integrity, or countability truth.
continuous_monitor todo. Refresh aggregate score/coverage and write
benchmark_case_insight_v0 on material scored-case transitions, with bounded
periodic reviews while the campaign remains active.quota monitor-poll --material-change --next-agent-todo with explicit
--next-action-kind, repository, and required capabilities so it creates an
independent runnable advancement_task. An unchanged poll creates no successor
and spends no delivery quota.open and pair resume_when=monitor_changed:<monitor-todo-id> with an
already-created independent runnable successor. Do not mark the wait blocked,
and do not treat the monitor itself as delivery work.benchmark_case_insight_v0 explaining the decisive evidence, why the outcome
happened, and what LoopX should test next.startup_only
only when a complete authorized post-run review observed no such transition;
otherwise absence is unknown. Keep terminal settlement separate.© loopx-project, 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 2 other files in skills/loopx-benchmark of loopx-project/loopx.
Open the folder on GitHubat commit 8205c8b
LoopX Benchmark Operator 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 |
|---|---|---|---|---|---|---|
| LoopX Benchmark Operator this skillloopx-project/loopx | 6.2k | — | ~3.6k | Automated safety check: Pass | Apache-2.0 | |
| MCP Server Builderanthropics/skills | 180k | 62 repos | ~2.3k | Automated safety check: Pass | Apache-2.0 | |
| Diagnosing Superpowers Sessionsobra/superpowers | 296k | 3 repos | ~1.7k | Automated safety check: Pass | MIT | |
| Darwin Skill Optimizeralchaincyf/darwin-skill | 6.2k | 1 repos | ~4.7k | Automated safety check: Pass | MIT | |
| Skill Release Gaterohitg00/ai-engineering-from-scratch | 65k | — | ~1k | Automated safety check: Pass | MIT | |
| CodeGraph Agent Evalcolbymchenry/codegraph | 73k | — | ~950 | Automated safety check: Pass | MIT |
anthropics/skills
Guides the design and implementation of Model Context Protocol servers in TypeScript or Python, from tool naming and error messages to evaluation.
obra/superpowers
Investigates a session where Superpowers went wrong, reads the transcripts on disk and produces an evidence-cited report, optionally prepared as a bug report for the maintainers.
alchaincyf/darwin-skill
Scores SKILL.md files on a nine-dimension rubric, then improves them in a keep-or-revert loop with independent judge agents, test prompts, git history and human checkpoints.
rohitg00/ai-engineering-from-scratch
Evaluates an Agent Skill bundle before release for structure, trigger quality, artifact improvement, script correctness, safety, installed-tree integrity and host portability.
colbymchenry/codegraph
Benchmarks how much CodeGraph helps a coding agent on a real repository, comparing runs with and without it for a chosen local or published version.
dotnet/maui
Mines local Copilot CLI session logs for dotnet/maui to rank costly or failing runs, tag recurring failure modes, propose repo edits and emit guard evals.
loopx-project/loopx
Tracks a group of pull or merge requests across repositories as durable LoopX state: inventory, reconcile changes, keep priorities and a roadmap, and monitor over time.
loopx-project/loopx
Diagnoses surprising LoopX behavior, such as stale recommendations or tiny progress, assigns it to the responsible layer and repairs it at the lowest durable level.
loopx-project/loopx
Role playbook for a LoopX worker running an auto-research lane, with execution checklists, artifact contracts and stop conditions.
loopx-project/loopx
Registers durable project materials such as design docs, SOPs and research notes in a LoopX project's own registry so future agents can find them without raw URLs or private content.
loopx-project/loopx
Runs an evidence-backed pull request review through the loopx CLI and posts bilingual reviews: a full Chinese review plus one concise English verdict.
loopx-project/loopx
Connects and configures LoopX projects and Goals, repairs project-local state and stale status, syncs registry entries and diagnoses CLI routing.
Categories
Operates or analyzes a LoopX-managed benchmark experiment: launching runs, maintaining the experiment board, qualifying integrity, and writing case insights. This skill only applies to actual run management or post-run analysis, not to a solver simply completing an assigned benchmark task under its own execution contract, even if that task mentions benchmark or evaluation. Installing the skill alone grants no runner, shell, network, credential, or Goal-mutation authority beyond what the current todo and host already allow.
LoopX Benchmark Operator fits situations like: launching or selecting a run inside a LoopX-managed benchmark; maintaining experiment-board rows for a benchmark study; writing a post-run case insight from a completed benchmark run.
Run `npx skills add loopx-project/loopx --skill loopx-benchmark -a claude-code`. Or copy the skill folder (skills/loopx-benchmark in loopx-project/loopx) into .claude/skills/loopx-benchmark in your project. Claude Code loads it when a task matches its description.
Run `npx skills add loopx-project/loopx --skill loopx-benchmark -a codex`. Or copy the skill folder (skills/loopx-benchmark in loopx-project/loopx) into .agents/skills/loopx-benchmark 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 loopx-project/loopx --skill loopx-benchmark -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/loopx-benchmark, .gemini/skills/loopx-benchmark, .github/skills/loopx-benchmark and .opencode/skills/loopx-benchmark in your project.
SKILL.md names no scripts, command-line tools or credentials: LoopX Benchmark Operator is instructions for the agent only. Our summary lists: The benchmark-toolkit capability installed in LoopX.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. 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.
LoopX Benchmark Operator is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 3.6k tokens (SKILL.md is roughly 15k 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 LoopX Benchmark Operator: MCP Server Builder (anthropics/skills, 180k stars), Diagnosing Superpowers Sessions (obra/superpowers, 296k stars), Darwin Skill Optimizer (alchaincyf/darwin-skill, 6.2k stars) and Skill Release Gate (rohitg00/ai-engineering-from-scratch, 65k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
loopx-project (a GitHub organization) maintains it in loopx-project/loopx, which has 6,167 GitHub stars. The repository holds 12 skills in this directory. The repository was last updated on October 7, 2026.
Source: loopx-project/loopx on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.