stellarlinkco-skills — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited stellarlinkco-skills (Plugin) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
<p> <a href="http://stellarlink.co/"> <img src="./assets/stellarlink-logo.svg" alt="Stellarlink" width="369"> </a> </p>
Agent skills for work that needs more control than a single prompt: long-running execution, high-recall code review, and measurable self-improvement loops.
These are not vibe-coding macros. They are small operating protocols for agents: explicit state, hard gates, deterministic rollback, and proofs before claims. Use them directly, fork them, or steal the patterns.
Install the collection with the skills.sh installer:
npx skills@latest add stellarlinkco/skillsThen call the skill you need from your agent:
/harness implement prd.md; loop verify, fix, retestFor manual installs, copy the skill directory you need from skills/ into your agent's skills directory. harness also needs hook registration; see skills/harness/README.md.
AI agents fail in boring, repeatable ways. They stop too early. They review too narrowly. They make a prompt or skill better once, then lose the path that made it better. This repo turns those failure modes into protocols.
The problem: long tasks die at session boundaries. Context windows reset, partial state disappears, and the agent starts summarizing instead of finishing.
The fix: harness gives the agent a durable task ledger, append-only progress log, hook-driven stop blocking, dependency checks, leases, and recovery rules. Progress files become the context.
Use it when a task has many subtasks, must survive sleep/resume cycles, or needs automatic recovery after a failed attempt.
The problem: most agent reviews optimize for precision too early. They produce a tidy list, but miss the dangerous bug hiding in a changed contract, deleted branch, or wrapper boundary.
The fix: code-review uses a max-recall pipeline: gather the real diff, generate candidates from independent angles, verify them, run a final gap sweep, then return a capped JSON findings list.
Use it for PRs, branch diffs, local working-tree diffs, security-sensitive changes, or any review where a missed P1 costs more than an extra candidate.
The problem: “make this better” is not a loop. Without a measurable oracle, each mutation is just taste with confidence.
The fix: self-evolution turns prompts, skills, documents, configs, code, and experiments into evaluate-gate loops. It supports GT case suites and scalar scoreboard metrics, keeps a ledger, and reverts mutations that do not pass.
Use it when the artifact can be measured and you want repeated improvement without guessing.
SKILL.md is the agent-facing protocol.~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.