>- Run a full 6-dimension health check of this Claude Code skills marketplace repo — code/script safety, documentation/SSOT consistency, security/PII leaks, open-PR triage, open-issue triage, and marketplace-manifest integrity — via a parallel fan-out Dynamic Workflow, then verify the serious findings and report them by priority. Use this whenever the user asks to check the repo, run a health check, do a full sweep/audit before a release, 全面体检, 检查仓库状态, 看看仓库健康吗, 审计一下仓库, or asks whether the PRs / issues / docs / versions / PII are in good shape across the board — even if they never say the word "workflow". Reach for it for any broad "is this whole repo OK" request, not just one-file checks.
npx skills add https://github.com/daymade/claude-code-skills --skill marketplace-health-check
Run a comprehensive, evidence-based health check of this Claude Code skills marketplace repo using a parallel fan-out Dynamic Workflow. Six independent inspectors cover, in parallel:
except, injection, missing shebangs.security-scan-passed marker gap, case-file auditscheck_marketplace.sh + check_doc_skill_lists.py, orphans, suite registrationThen YOU verify the serious findings and report by priority. The bundled script (scripts/repo-health-check.workflow.js) is the proven, ready-to-run workflow; this file is how to run and interpret it.
The six dimensions are independent, so fanning them out across six parallel agents is far faster than one agent sweeping serially, and each inspector stays focused on one concern with its own structured output.
This skill must run inline (no context: fork). It orchestrates parallel agents through the Workflow tool, and a forked subagent cannot spawn subagents or launch a workflow — running it forked would silently break the fan-out. The Workflow tool also requires explicit user opt-in; a user asking to "run the health check" IS that opt-in, so proceed.
The workflow script takes an args object so all agents share one accurate snapshot instead of each re-discovering it. Gather:
gh repo view --json nameWithOwner,stargazerCount,isPrivate | jq -c .
echo "skills: $(find . -name SKILL.md -not -path '*-workspace/*' | wc -l | tr -d ' ')"
echo "open PRs: $(gh pr list --state open --json number | jq length)"
echo "open issues: $(gh issue list --state open --json number | jq length)"
grep -A1 '"metadata"' .claude-plugin/marketplace.json | grep -oE '"version": "[^"]*"' | head -1
git rev-parse --short HEAD; gh release view --json tagName -q .tagName 2>/dev/null
Confirm isPrivate: false before treating PII as a publishing risk — the whole point is that this is a PUBLIC repo.
Read the bundled script and launch it inline via the script parameter (pass its contents, so there's no dependency on where the skill is installed):
Workflow({
script: <full contents of scripts/repo-health-check.workflow.js>,
args: { repo: "<owner/name>", scale: "<one-line summary from Step 1>" }
})
It runs the six inspectors in parallel (~15-20 min, ~400-500k output tokens — tell the user the cost up front) and returns { checks: [...] }, one structured result per dimension: health + summary + findings[] (each with severity / title / detail / location / recommendation) + stats.
While it runs you can do other useful prep, but don't start editing files the inspectors are reading.
Agent findings are HYPOTHESES, not conclusions. Never relay them verbatim. For every high/critical finding, verify it yourself with a quick command — grep the leaked value, sed -n the broken line, gh repo view the claimed state — confirming it's (a) real, (b) located where the agent says, and (c) not over-reach. This catches false alarms AND, just as important, agent *recommendations* that are actively wrong. (In the session this skill was distilled from, a security inspector recommended adding the real private domains into the public .gitleaks.toml — an anti-target move that had to be rejected; see the methodology reference.)
Filter every finding through four questions: probability (does it really happen?), cost (fix vs ignore), real scenario (does it bite in practice?), verifiable (can a 1-line command confirm or refute it?).
Lead with the table, then layer by priority. Classify — don't dump:
Tag each surfaced item ✅ real / ⚠️ partly / ❌ false-alarm. Most raw agent output is noise; your job is to surface the real risks the owner didn't already know, not to forward 25 findings for them to sift.
Apply these when interpreting findings and proposing fixes. Full reasoning + the real failure cases behind each are in references/health-check-methodology.md — read it before acting on PII or PR/issue findings.
.gitleaks.toml) — a public list enumerating real assets is itself a leak. Sanitize the value in place; detection rules for real private values belong in the owner's private global guard, not in this public repo..security-scan-passed marker means "no known-format secret was found", NOT "sanitized". It is blind to keyword-free leaks, so pair it with a human/semantic read of any skill shipping real-data examples.version in marketplace.json (and a CHANGELOG entry). External-contributor PRs almost always miss this — flag it, don't merge without it.scripts/repo-health-check.workflow.js — the six-inspector Dynamic Workflow. Run it via the Workflow tool's script param (Step 2). Edit it when you add/retire an inspector dimension.references/health-check-methodology.md — the Counter-Review filter, reporting discipline, and the anti-target / history / scan-marker / decline rules, each with the real failure case that motivated it.After delivering the report, the typical follow-ups are owner decisions, not automated actions — fixing the verified HIGHs (sanitize PII, correct broken commands), or triaging the PR/issue backlog. Surface them as options; don't auto-fix or auto-comment on PRs/issues without the user's go-ahead, since those are outward-facing and affect external contributors.
> FHIR REST endpoints (Patient, Observation, Encounter, Condition, MedicationRequest), (2) Validating FHIR resources and returning proper HTTP status codes and error responses, (3) Implementing SMART on FHIR authorization and OAuth scopes, (4) Working with Bundles, transactions, batch operations, or search pagination. Covers FHIR R4 resource structures, required fields, value sets (status codes, gender, intent), coding systems (LOINC, SNOMED, RxNorm, ICD-10), and OperationOutcome error handling.
Interact with ClawDirect, a directory of social web experiences for AI agents. Use this skill to browse the directory, like entries, or add new sites. Requires ATXP authentication for MCP tool calls. Triggers: browsing agent-oriented websites, discovering social platforms for agents, liking/voting on directory entries, or submitting new agent-facing sites to ClawDirect.
Shared audit integrity framework for all AppSec agents — enforces output quality, intellectual honesty, and continuous improvement through anti-rationalization guards, self-critique loops, retry protocols, non-negotiable behaviors, self-reflection quality gates (1-10 scoring, ≥8 threshold), and a self-learning system with lesson/memory governance for security analysis agents.
Opt out of the OneCLI gateway and supply Anthropic credentials from .env instead. For users who want simple .env-based credential management without the OneCLI agent vault. Reads the API key or OAuth token from .env and injects it into the container's API requests.
Cross-product Zoom reference skill. Use after the workflow is clear when you need shared platform guidance, app-model comparisons, authentication context, scopes, marketplace considerations, or API-vs-MCP routing.
>- Static source-code vulnerability scan. Reads a target directory (and THREAT_MODEL.md if present), spawns parallel review subagents per focus area, and writes VULN-FINDINGS.json + .md for /triage to consume. Read-only — no building, running, or network. For execution-verified crashes, use vuln-pipeline instead. Use when asked to "scan for vulns", "review this code for security issues", "find bugs in <dir>", or as the step between /threat-model and /triage.
Hunt Session Management vulnerabilities — session fixation (no regeneration on login), insufficient invalidation on logout / password-change / email-change, predictable or low-entropy session IDs, JWT-as-session with no exp/revocation, refresh-token rotation/reuse-detection gaps, OAuth/SSO session linkage, device-bound-session (DBSC) downgrade, and cookie attribute issues (Secure/HttpOnly/SameSite/__Host-). Validate with TWO real sessions (attacker A + victim B), body-diff every 200, and OOB confirmation for theft chains. Medium to Critical (fixation→admin hijack, no-invalidation→persistent ATO).
> Use this skill when the user is doing hands-on DOCA AES-GCM work on a BlueField DPU or ConnectX NIC — configuring `doca_aes_gcm_task_encrypt` / `_task_decrypt`, querying `doca_aes_gcm_cap_*` for per-key-type (only `DOCA_AES_GCM_KEY_128` / `_256` — AES-192 not supported) and per-task support, sizing plaintext against the max-buf cap, setting source / destination mmap permissions, validating with a NIST GCMVS or RFC 5288 vector, or debugging DOCA_ERROR_* including the security-critical tag-verification-failed outcome on decrypt. Trigger even when the user does not explicitly mention "DOCA AES-GCM" or IO_FAILED", "auth tag isn't verifying", "NOT_PERMITTED on my encrypt buffer", "is AES-192-GCM on this BlueField" (no), or "encrypted record came back tampered". Refuse and route elsewhere for non-GCM AES modes (CBC / CTR / XTS — CPU OpenSSL), key management (KMS / HSM / rotation), SHA (doca-sha), or general AEAD background.
Take daymade/marketplace-health-check from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.