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.
Use only when the user explicitly invokes $onboard-repository.
$ npx skills add hoangnb24/repository-harness --skill onboard-repository -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install hoangnb24/repository-harness onboard-repository --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/hoangnb24/repository-harness.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/onboard-repository .claude/skills/onboard-repository && 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 "onboard-repository" agent skill from https://github.com/hoangnb24/repository-harness/tree/main/.agents/skills/onboard-repository into .claude/skills/onboard-repository/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "onboard-repository", 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/hoangnb24/repository-harness/tree/main/.agents/skills/onboard-repositoryType 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 hoangnb24/repository-harness --skill onboard-repository -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install hoangnb24/repository-harness onboard-repository --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/hoangnb24/repository-harness.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.agents/skills/onboard-repository .agents/skills/onboard-repository && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "onboard-repository" agent skill from https://github.com/hoangnb24/repository-harness/tree/main/.agents/skills/onboard-repository into .agents/skills/onboard-repository/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "onboard-repository", 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 hoangnb24/repository-harness --skill onboard-repository -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install hoangnb24/repository-harness onboard-repository --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/hoangnb24/repository-harness.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.agents/skills/onboard-repository .cursor/skills/onboard-repository && 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 "onboard-repository" agent skill from https://github.com/hoangnb24/repository-harness/tree/main/.agents/skills/onboard-repository into .cursor/skills/onboard-repository/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "onboard-repository", 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/hoangnb24/repository-harness.git --path .agents/skills/onboard-repository--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 hoangnb24/repository-harness --skill onboard-repository -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install hoangnb24/repository-harness onboard-repository --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/hoangnb24/repository-harness.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.agents/skills/onboard-repository .gemini/skills/onboard-repository && 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 "onboard-repository" agent skill from https://github.com/hoangnb24/repository-harness/tree/main/.agents/skills/onboard-repository into .gemini/skills/onboard-repository/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "onboard-repository", 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 hoangnb24/repository-harness onboard-repositoryInstalls 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 hoangnb24/repository-harness --skill onboard-repository -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/hoangnb24/repository-harness.git skills-src && mkdir -p .github/skills && cp -r skills-src/.agents/skills/onboard-repository .github/skills/onboard-repository && 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 "onboard-repository" agent skill from https://github.com/hoangnb24/repository-harness/tree/main/.agents/skills/onboard-repository into .github/skills/onboard-repository/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "onboard-repository", 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 hoangnb24/repository-harness --skill onboard-repository -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install hoangnb24/repository-harness onboard-repository --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/hoangnb24/repository-harness.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.agents/skills/onboard-repository .opencode/skills/onboard-repository && 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 "onboard-repository" agent skill from https://github.com/hoangnb24/repository-harness/tree/main/.agents/skills/onboard-repository into .opencode/skills/onboard-repository/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "onboard-repository", 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.
onboard-repositoryUse only when the user explicitly invokes $onboard-repository.
Onboard Repository is an agent skill from hoangnb24/repository-harness. Use only when the user explicitly invokes $onboard-repository. Inspect an unfamiliar repository, trace one operational path, identify missing capabilities, and propose evidence-backed knowledge improvements. The first pass is read-only; apply exact approved items only within the requested scope.
Its SKILL.md is about 6.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including scripts and reference files (for example `agents/openai.yaml`, `references/evidence-capsule-v1.md` and `references/evidence-capsule-v2.md`).
It sits in Agent Workflows. The repository describes itself as: Turn any repo into an agent-ready workspace for Claude Code, Codex, Cursor, and other coding agents. The licence is MIT.
6 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 485fd40. 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.
Ships 2 files in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
python3nodepnpmFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md. Its commands use pnpm, 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.
Onboard Repository loads about 6.8k tokens when it runs, and up to ~10k if it reads all its reference files. Until then it costs about 79 tokens; SKILL.md has 3,600 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 noted patterns worth knowing about, such as sudo or a known installer.
`.env.local`, dependency directories, and managed repository state. NeverAutomated 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); the scripts in this folder are not scanned.
The full file from hoangnb24/repository-harness at commit 485fd40, republished under its MIT licence (© hoangnb24). 3,600 words, ~6,760 tokens.
.claude/skills/onboard-repository/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.Turn an unfamiliar repository into a verified map for future work. Treat the repository as the system of record. Separate facts from gaps and suggestions.
The first pass is always inspection and proposal only, even when the worktree is writable.
AGENTS.md before inspecting deeper files..env.local, dependency directories, and managed repository state. Never
print secret contents. If an initial baseline was missed, report pre/post
equivalence as Unknown; a later sample cannot reconstruct it.path, status, pipestatus, commands, or their uppercase system
counterparts; corrupting the inspection shell invalidates later baselines.python3 -c/node -e programs, stdin pipes, or
ordinary read-only commands instead.An explicit later approval may authorize documentation changes. It never authorizes application-code changes, invented product policy, hooks, databases, or background automation unless the user separately requests them.
Read applicable instructions and the smallest repository map available. Record pre-existing dirt before doing anything else. If instructions conflict, follow the narrower instruction and report the conflict.
When .harness-core/manifest.json exists and an installed managed file
conflicts with .harness-core/base/<path>, treat the installed file as active
instructions for the current run. For a correction proposal, verify the base
file against its manifest checksum and show the conflict. Propose replacing
only content inside managed markers and preserve all consumer-owned content
outside them. Do not treat the managed base as permission to edit.
A checksum-verified conflict in an active mandatory instruction that caused a failed, unavailable, or unsafe command is the first proposal priority. Preview that correction before proposing additive documentation elsewhere.
Inspect only material needed to understand the requested path, normally:
For every important claim, cite an exact repository path and classify it:
Never silently promote Observed, Derived, Decision required, or Unknown to Authoritative.
Treat operational authority as context-specific. A command or flag documented for CI, a container build, release automation, or another runbook proves only that context. Do not transplant it into a local developer procedure unless the local owning document authorizes it or the user chooses it. When two viable commands differ, do not select one as a Derived rule; classify the choice as Decision required.
When evidence comes from a type, schema, or serializer, distinguish required from optional fields. Say that optional fields appear only when present; a field's existence in a schema does not prove that every emitted record has it.
Verify every clause and qualifier in a proposed sentence independently. Do not generalize configurability, defaults, optionality, ownership, or lifecycle from one field or resource to an adjacent one merely because they appear together.
For environment-derived behavior, trace each claimed key from every source to its final consumer. Distinguish same-key merge precedence, fallback between different keys, checked-in values that make later fallbacks unreachable on the default path, and assignments performed after a merge. Never summarize this as "the environment overrides the files" unless that is true for every named key.
List related identifiers separately and label each one fixed, defaulted, or configurable, with its own source. Apply the same rule to ports, paths, and resource names: state both configurability and fallback when either exists. Assign each write to the stage where it actually occurs rather than grouping later interface writes into setup or readiness.
The current task or frozen evaluation prompt defines run scope, not durable repository authority. A new rule supported only by that prompt is Decision required unless the user explicitly adopts it as repository policy.
During the read-only proposal pass, compare accepted architecture, reliability, security, and quality invariants with the repository's checked-in validation. This is an inventory, not authority to edit or enforce.
Use one row per invariant or check:
| Documented invariant or executable check | Authority and source | Validation owner and command | Local coverage | CI discovery | Finding |
|---|
Classify findings precisely:
Inspect the native validation owner and checked-in CI invocation separately. A local command, an optional hook, CI configuration, and external branch protection are different enforcement levels; do not infer one from another.
Do not add, edit, delete, enable, or execute a guard during onboarding. Do not install hooks or mutate CI, merge, or branch-protection settings. Report the mismatch with evidence and propose the smallest next step: encode an accepted rule, obtain a missing decision, or investigate an unknown. Any later edit or enforcement still requires exact user approval and the repository authority gate.
Prefer one already-documented local happy path over a broad architecture summary. Trace cause and effect through:
prerequisites
-> start
-> readiness
-> deterministic setup
-> real interface exercise
-> evidence and correlation boundary
-> stop and cleanupUse this exact operational-path table schema; do not merge columns:
| Stage or branch | Command/interface and expected result | Classification and source | Write at this stage | Process/container owner | Host and container ports | Evidence/log boundary and correlation | Cleanup at this stage | Unknowns |
|---|
Include a value or N/A/Unknown in every cell. An omitted or implicit cell fails the operational-path gate; prose elsewhere does not replace it.
Trace lifecycle flags as separate table rows. At minimum, include default
startup, each no-start mode, cleanup requested after success, and failure after
startup. For a no-start mode, continue tracing later schema, probe, interface,
and write behavior rather than assuming the flag makes the whole run read-only.
Verify whether cleanup is unconditional, after assertions, or in a
finally/trap path. "No teardown command is invoked" does not prove that every
service remains running, and a cleanup flag must not be described as guaranteed
when earlier failure bypasses it.
Classify cleanup mechanics separately from cleanup obligations. Existing code or an authorized command may prove how cleanup can be performed; it does not create a new instruction that an operator must perform cleanup after a specific failure. Put that obligation in Decision required unless repository authority already states it.
If a read-only inspection contacts a runtime manager such as Docker, capture the relevant project/container identifiers and pre/post state. Never describe logs as instance-local merely because they are container logs; identify the actual project/container boundary and correlation identifiers.
Before the path table, add a resource-and-identifier ledger:
| Item | Kind | Exact behavior or value | Classification and source |
|---|
Use one row per identifier, port, project, service, volume, state path, and log
boundary. Kind must distinguish fixed, defaulted, configurable,
generated, logical configuration name, and observed runtime name.
For port mappings, list host and container sides separately. Never report a
logical Compose volume key as an observed engine-level volume name.
One row means one item: do not combine two identifiers or resources in a single row even when their classification is identical. Record checked-in values that make later fallbacks unreachable on the documented default path. For logs, state which fields are guaranteed and which are optional; optional identity or warning fields make evidence request-correlatable only when present. Keep process-wide metrics separate from request- or instance-correlated evidence.
Source the entire causal chain for each effect. An HTTP controller does not by itself prove persistence, and a runner call does not by itself prove provider, logging, database, or runtime-manager consequences. Mark direct implementation facts Observed and consequences of a called tool or protocol Derived. Pre-existing resources under a no-start mode have Unknown creator/owner unless the repository or runtime observation identifies it.
Do not execute the path during the first pass. The goal is to learn whether a fresh agent could execute it without undocumented human help.
Propose only missing knowledge or navigation; do not write it. When existing code, interfaces, commands, tests, and examples already explain the inspected path, retain references in the map and return no proposals. Do not create summary documentation merely to produce a patch. Each proposed item must include:
Show all six headings for every proposal, including Unknowns: none when no
unresolved factual claim affects that proposal. Do not infer completion of a
field from prose in another section.
Prepare an exact patch preview. Classify and cite every proposed sentence, not merely the proposal containing it. The approval unit is the machine-emitted hunk ID, destination, and patch digest, not a proposal number or an ambiguous alternative.
Never handwrite unified-diff headers, line ranges, context, or patch hashes. Construct the complete proposed destination image in memory from the pinned destination, preserving every byte outside the intended edit. Pipe those complete bytes to the bundled renderer:
<non-materializing command that prints the complete after image> |
python3 .agents/skills/onboard-repository/scripts/render_patch.py \
--repository <tested-root> \
--revision <full-tested-revision> \
--destination <repository-relative-path> \
--hunk-id H1Use the standalone renderer only as a preflight when needed. Do not copy its output into the final answer or manually repair it. Correct the complete after image and rerun it. The final bundle emitter invokes the same renderer and produces the canonical marked diff and digests. A renderer failure excludes that proposal from the approval bundle. Rendering is read-only and creates no draft file.
Give every emitted diff hunk a stable H1, H2, ... identifier. The emitter
wraps it exactly as follows:
<!-- ONBOARDING_PATCH:H1:BEGIN -->
```diff
<one complete diff hunk>
```
<!-- ONBOARDING_PATCH:H1:END -->The hunk digest is SHA-256 over the exact UTF-8 bytes inside the diff fence,
including one final newline and excluding the fence lines. Do not reuse an
identifier or place two hunks inside one marker pair.
For control-flow wording, cite every branch needed to prove the whole clause: flag parsing, startup, checks, failure handling, and teardown as applicable. A single line that initializes a flag or collection does not prove later lifecycle behavior.
Treat temporal words as causal claims. Before writing before, after,
successful, complete, or finally, locate the exact success/failure signal
and every awaited operation around it. If teardown runs before the success
signal and can itself fail, say after all preceding checks succeed, the runner attempts teardown; do not call it teardown after a successful run.
Do not promote a sentinel or partial probe into an aggregate claim. Wording
such as the schema, the configuration, all required resources, or the system is ready requires an enumerated membership boundary and evidence for
every member. Otherwise name the exact object checked, such as checks whether processed_webhook_events exists. One table probe does not prove that a
multi-table schema is present.
Phrase negative lifecycle evidence narrowly. Skipping or bypassing a teardown
call proves only that this runner does not invoke that teardown; it does not
prove resources remain running. Prefer the runner does not invoke Compose down over the runner leaves the stack running unless runtime evidence proves
the stronger state.
Before displaying a command as new runbook guidance, require authority from the same operational context. Code may prove that dependencies are required, but a CI install command does not determine the approved local install command or flags.
Order proposals by causal leverage:
Use the existing file that already owns the operational procedure. Do not add current runbook guidance to a generic, historical, or future-contract document when a maintained operations guide exists. Never put an Unknown sentence in a patch preview; keep it in the gap report until authority or a user decision exists.
When no maintained operational guide exists, use
docs/templates/application-runbook.md only to structure a proposed
consumer-owned guide. The template supplies headings, not commands or authority:
omit unsupported instructions from the patch and retain them as unknowns.
Prefer a correction to an existing repository-owned document over a new framework. Do not propose generic adapters, hooks, state markers, databases, or generated architecture unless the observed failure requires them.
Read references/evidence-capsule-v2.md completely before constructing the
capsule. Follow its exact field names, vocabularies, and hash definitions.
Do not manually copy source, patch, producer, or destination hashes into the
final answer. Build one compact JSON spec in memory and pipe it to
scripts/emit_evidence_bundle.py. The spec contains boundary rows, atomic
claims with unhashed source ranges, complete proposed destination images,
unknowns, and limitations. The emitter reads every pinned blob, computes every
digest, renders each patch, and writes one authenticated bundle to stdout
without creating a draft file.
For no backfill, supply empty claims and hunks arrays. Keep all four boundary
rows and limitations; authentication and no-mutation reporting still apply.
There are no patch IDs or edits to approve in this outcome.
<non-materializing command that prints the JSON spec> |
python3 .agents/skills/onboard-repository/scripts/emit_evidence_bundle.py \
--repository <tested-root> \
--revision <full-tested-revision> \
--branch <tested-branch>Run the emitter as the final tool call with an output budget large enough to retain the complete result. Do not rerun it unless it fails. Do not reproduce its patch or capsule bytes in the assistant message. The raw tool result is the auditable artifact; the final answer reports its bundle digest, hunk IDs, destinations, classifications, gate result, and exact approval instruction.
Use schema onboarding-evidence-capsule/v2. The emitted capsule is an
authenticated index, not authority. It contains exactly:
tested_repository: absolute root, full 40-character revision, and branch;producer_skill: repository-relative skill path and file SHA-256;boundary: separate rows covering git, ignored_or_managed, runtime,
and temporary_paths, with normalized initial/final evidence hashes and a
Pass, Fail, or Unknown result;claims: one atomic, single-line proposed clause per ID, its hunk ID,
Authoritative/Observed/Derived classification, and every exact source;hunks: destination, whole-file destination hashes before and after
in-memory patch application, displayed patch hash, claim IDs, and unresolved
unknowns;limitations: remaining audit or environment limitations.For each source spec record the repository-relative path, inclusive start/end
lines, and role. Set revision only when it differs from the tested revision.
The emitter adds the pinned revision and SHA-256 of the exact LF-terminated
range. Never cite a working-tree line without a pinned revision. Split
conjunctions into atomic claim records even when they remain one
natural-language sentence.
Repeat each claim's complete causal chain inside its capsule sources; prose
citations elsewhere do not satisfy the capsule. For a command-controlled
effect, include the package/entry command mapping, flag-to-branch mapping,
branch ordering, and the called implementation that performs the effect.
Make each capsule claim one subject-condition-effect fact. Split sentinel lookup, branch selection, named path, file read, subprocess call, failure propagation, and cleanup absence into separate claims even when the patch uses one natural-language sentence. When a claim names an exact constant, path, or fallback, cite both its definition and its use. When a claim says a failure bypasses later work, cite the awaited call, the callee or assertion that rejects/throws, and the top-level handler; the callsite alone does not prove propagation.
For a boundary row, Pass requires equal non-null initial and final hashes;
Fail requires two different hashes; use Unknown when either observation is
missing or cannot prove equivalence. Hash stable normalized observation output,
not terminal decoration or timestamps. The capsule must reflect Unknowns from
the prose gate rather than silently upgrading them.
When the sibling audit skill is installed, its read-only validator is:
python3 .agents/skills/audit-onboarding-proposal/scripts/validate_evidence_capsule.py --transcript <raw-session.jsonl> --expected-transcript-sha256 <sha256> --repository <tested-worktree>The validator can run only after the transcript exists. For new runs it extracts the last complete machine bundle emitted before task completion and verifies its bundle digest, pinned producer and source blobs, and whole-file patch application. Legacy transcripts with capsules in the completed assistant message remain readable. Do not create a local draft merely to prevalidate the final answer.
End the first pass with:
The gate passes only when repository authority was read, the complete path table contains every required field, every patch sentence passes clause-level evidence review, the run stopped at suggestions, and all required no-mutation comparisons are proven. Git cleanliness alone cannot satisfy the last gate.
Compute the no-mutation gate conjunctively. Git state, every relevant ignored or managed path, runtime ownership, and every task-owned prohibited temporary path must each have proven pre/post equivalence; any required Unknown makes this gate fail. Do not convert an unknown into a pass because no mutating command was observed. A discovered repository gap does not by itself fail a methodological gate; score the five gate conditions, not repository readiness.
Before assigning scores, perform a contradiction scan:
If the final state differs from the initial state, disclose every difference and treat the pass as failed. Do not repair or erase user-owned changes.
Apply only when the user approves the exact displayed patch wording.
When new choices appear during application, stop and return them for approval.
Suppose a repository documents pnpm local:e2e, its test runner observes
/health and /chat, and the root onboarding guide points to a nonexistent
binary. A verified managed base contains newer instructions without that
binary.
The first pass follows the active root instructions, reports the unavailable binary, verifies the managed-base checksum, and leaves Git unchanged. Its patch preview replaces only the stale managed block and links the existing E2E procedure. Any new cleanup rule is classified Decision required. It must not claim a new chat contract or add a universal startup script. After the user approves exact patch wording, the later pass applies it and produces a skill-free documentation patch for a fresh replay. Success is fewer undocumented hints and safer command ordering, not merely more documentation.
© hoangnb24, 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 5 other files (scripts, references) in .agents/skills/onboard-repository of hoangnb24/repository-harness.
Open the folder on GitHubat commit 485fd40
Onboard Repository 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 |
|---|---|---|---|---|---|---|
| Onboard Repository this skillhoangnb24/repository-harness | 1.2k | — | ~6.8k | Automated safety check: Notes | MIT | |
| MCP Server Builderanthropics/skills | 180k | 63 repos | ~2.3k | Automated safety check: Pass | Apache-2.0 | |
| Hook Development for Claude Code Pluginsanthropics/claude-plugins-official | 38k | 10 repos | ~4.1k | Automated safety check: Notes | Apache-2.0 | |
| Using Superpowersfarm-fe/farm | 5.6k | 36 repos | ~1.4k | Automated safety check: Pass | MIT | |
| Executing Plans Inlineobra/superpowers | 297k | 2 repos | ~5.1k | Automated safety check: Pass | MIT | |
| Skill CreatorAzure/azqr | 796 | 89 repos | ~8.2k | Automated safety check: Pass | Apache-2.0 |
anthropics/skills
Guides the design and implementation of Model Context Protocol servers in TypeScript or Python, from tool naming and error messages to evaluation.
anthropics/claude-plugins-official
Explains how to write Claude Code plugin hooks, both prompt-based checks and bash commands, for events such as PreToolUse, Stop and SessionStart.
farm-fe/farm
A skill your agent uses when starting any conversation - establishes how to find and use skills, requiring Skill tool invocation before ANY response including clarifying questions
obra/superpowers
Has the agent carry out an implementation plan itself, task by task in the current session, keeping a ledger, proving each step with a test and ending with one whole-branch review.
Azure/azqr
Create new skills, modify and improve existing skills, and measure skill performance.
anthropics/claude-plugins-official
Explains how to write agents for Claude Code plugins: the markdown file with YAML frontmatter, trigger descriptions, model and color settings, and system prompt design.
hoangnb24/repository-harness
Use only when the user explicitly invokes $audit-onboarding-proposal.
hoangnb24/repository-harness
Use only when the user explicitly invokes $engineering-wisdom.
hoangnb24/repository-harness
Use only when the user explicitly invokes $improve-harness. An agent skill from hoangnb24/repository-harness.
hoangnb24/repository-harness
Use only when the user explicitly invokes $encode-invariant.
Categories
Use only when the user explicitly invokes $onboard-repository. Onboard Repository is an agent skill from hoangnb24/repository-harness. Use only when the user explicitly invokes $onboard-repository.
Onboard Repository fits situations like: explicitly invokes $onboard-repository.
Run `npx skills add hoangnb24/repository-harness --skill onboard-repository -a claude-code`. Or copy the skill folder (.agents/skills/onboard-repository in hoangnb24/repository-harness) into .claude/skills/onboard-repository in your project. Claude Code loads it when a task matches its description.
Run `npx skills add hoangnb24/repository-harness --skill onboard-repository -a codex`. Or copy the skill folder (.agents/skills/onboard-repository in hoangnb24/repository-harness) into .agents/skills/onboard-repository 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 hoangnb24/repository-harness --skill onboard-repository -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/onboard-repository, .gemini/skills/onboard-repository, .github/skills/onboard-repository and .opencode/skills/onboard-repository in your project.
Going by SKILL.md and its folder, Onboard Repository needs Python for the scripts in its folder and the command-line tools its instructions call (python3, node and pnpm). Our summary lists: Python 3; Docker.
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 notes only (mentions a .env file), nothing it rates as a warning. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
Onboard Repository is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 6.8k 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 3.3k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Onboard Repository: MCP Server Builder (anthropics/skills, 180k stars), Hook Development for Claude Code Plugins (anthropics/claude-plugins-official, 38k stars), Using Superpowers (farm-fe/farm, 5.6k stars) and Executing Plans Inline (obra/superpowers, 297k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
hoangnb24 (a GitHub user) maintains it in hoangnb24/repository-harness, which has 1,243 GitHub stars. The repository holds 5 skills in this directory. The repository was last updated on October 4, 2026.
Source: hoangnb24/repository-harness on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.