mongodb/skill-gate

記録された最新のソース活動: 2026年6月26日 02:35

SkillsMP カタログ更新: 2026年10月26日 11:37

収集済み skills: 13

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AI アシスタントでインストール

このプロンプトをコピーして、使用中の AI アシスタントに送ってください。

Follow https://skillsmp.com/skill-install/prompt.md to install Agent Skills from https://github.com/mongodb/skill-gate.

リポジトリの README

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# skill-gate A CLI that gates Agent Skill PRs by evaluating the **prose and guidance** of a skill bundle — not its code — for content that could steer an agent toward unsafe behavior. It runs a skill's markdown through a YAML rule pack and produces a single verdict: **AUTO-PASS**, **WARN**, or **ESCALATE**. > **Status: both stages implemented.** The CLI (`scan`, `version`, `rules list`, > `rules lint`), the static-pattern engine, the LLM-as-judge stage, the `core` + > `mongodb` rule packs, and the full output surface needed to gate PRs in CI > (`text`, `json`, `markdown`, and GitHub Actions `--emit-annotations`) are working > today. ## Why it exists Most skill scanners target *untrusted-author maliciousness* (prompt injection, code execution, exfiltration). skill-gate sits in a different lane: **trusted-author content evaluation** — skills written in good faith whose guidance could still direct an agent toward unsafe actions (logging credentials, over-broad permissions, unguarded destructive commands, ambiguous scope). It evaluates instructional content, not code logic. ## How it works A two-stage pipeline over every markdown file in a skill bundle: 1. **Static patterns** *(implemented)* — regex rules for criteria with clear lexical signatures (credential references, hardcoded secrets in examples, destructive verbs, external URLs), with a heuristic that *downgrades* matches reading as cautionary documentation examples ("never log credentials") to an advisory WARN rather than silently dropping them — so a real instruction disguised with cautionary phrasing can never slip through to AUTO-PASS. 2. **LLM-as-judge** *(implemented)* — rubric-based evaluation for criteria that need semantic judgment (unsanitized user input, PII handling, over-broad permissions, admin-command scope, ambiguous scope, missing guardrails). Each `llm_judge` rule carries a rubric and a JSON-schema'd finding contract; the judge fails closed — a refusal, truncation, or invalid response is a tool error, never a silent pass — and caches results per `(model, rule, file)` so unchanged skills aren't re-evaluated. Each rule declares a severity tier. The verdict is the highest tier any rule triggers: | Verdict | Exit code | Meaning | |---|---|---| | AUTO-PASS | `0` | No rules fired. | | WARN | `1` | Advisory findings; author resolves before opening a PR. | | ESCALATE | `2` | Blocking findings; requires security review. | Symlinks are unsupported by the security scanner. A bundle root, recursive entry, or explicitly supplied scan file that is a symlink is rejected rather than following the link. Scanned files are opened without following final-component symlinks where the platform supports it and are required to be regular files. ## Install ```sh go install github.com/mongodb/skill-gate/cmd/skill-gate@latest ``` ## Usage ```sh # Scan a skill bundle (text output; exit code 0/1/2 = AUTO-PASS/WARN/ESCALATE) # Stage 2 needs an LLM client (see below); add --static-only to run stage 1 alone. skill-gate scan ./my-skill/ # Machine-readable output / PR comment skill-gate scan ./my-skill/ -o json skill-gate scan ./my-skill/ -o markdown # CI: pin findings to lines in the PR diff via GitHub Actions annotations skill-gate scan ./my-skill/ --emit-annotations # Treat WARN as a blocking exit code (per-repo CI policy) skill-gate scan ./my-skill/ --strict # Filter low-confidence static findings: below the floor, ESCALATE→WARN or WARN drops # (static stage only; judge confidence is uncalibrated, so the floor doesn't gate it) skill-gate scan ./my-skill/ --min-confidence 0.6 # Choose which built-in packs run (default: all) skill-gate scan ./my-skill/ --packs core # core only skill-gate scan ./my-skill/ --packs none --rules-dir ./rules.d/ # only your packs # Overlay your own rule packs (added on top of the selected built-ins) skill-gate scan ./my-skill/ --rules-dir ./rules.d/ # Run only the static stage (no LLM client required) skill-gate scan ./my-skill/ --static-only # Inspect and validate rule packs skill-gate rules list skill-gate rules lint --rules-dir ./rules.d/ ``` `--emit-annotations` writes GitHub Actions workflow commands (`::error` / `::warning`) to stdout in addition to the chosen `-o` format, so a CI job can both produce a report artifact and pin each finding to its line in the diff. The annotation paths are resolved relative to the directory the scan is launched from. The LLM-as-judge stage uses a client configured by environment — `ANTHROPIC_API_KEY` (required), `ANTHROPIC_BASE_URL` (default `https://api.anthropic.com`), and `ANTHROPIC_AUTH_HEADER` (default `x-api-key`; set `api-key` for an Azure-fronted gateway) — with the model set by `--llm-model` (default `claude-sonnet-4-6`). When the selected packs contain `llm_judge` rules but no client is configured, the scan **fails closed** (a tool error) rather than skipping them; pass `--static-only` to run stage 1 alone. The static stage needs no configuration. The judge result cache is **opt-in** and off by default (`--cache-dir <path>` enables it): its validity is keyed only on public inputs and the author's own content, so a committed cache could be forged to force a pass. In CI, point `--cache-dir` at an ephemeral, never-committed location (or leave caching off); locally, treat it as scratch. Skill content sent to the judge is fenced as untrusted input, so prose that tries to talk the judge out of a finding does not change the verdict. ## Extending it skill-gate is built to be reusable in other projects: - **Bring your own rules** *(available now)* — rule packs are plain YAML loaded via `--rules-dir`; no Go required. Validate them with `skill-gate rules lint`. - **Choose your packs** *(available now)* — `--packs` selects which built-in packs run (e.g. `--packs core` for a non-MongoDB repo, or `--packs none` to run only your own overlays). The default runs every built-in pack. - **Embed the scanner** *(available now)* — call `scanner.Scan` and read the returned `Report`. The `verdict` package is the stable vocabulary for tiers and exit codes. - **Bring your own LLM client** *(available now)* — implement the `llm.Client` interface (one `Judge` method) and pass it to `scanner.Scan` via `Config.Client`, e.g. for Bedrock or a custom gateway. See [`examples/custom-client`](examples/custom-client). See [`docs/architecture.md`](docs/architecture.md) for the package layout and the public API surface. ## Development ```sh make fmt # format (gofumpt via golangci-lint) make lint # static analysis + license-header check make test # race tests make build # build the binary make install-hooks # install pre-commit hooks (requires the pre-commit framework) ``` `golangci-lint` is the single source of truth for lint and format, shared by the pre-commit hook and CI. ## Releasing skill-gate ships as a Go module — a release is just a pushed semver tag, with no build pipeline: installing at a tag compiles from source, and the binary reads its version from build info. See [RELEASING.md](RELEASING.md). ## License Licensed under the Apache License 2.0 — see [LICENSE](LICENSE).

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