agentdb-advanced
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems…
Run `@metaharness/darwin security bench` (upstream "Darwin Shield" / ADR-155) — evolves a champion security-detection harness against a 10-vuln / 9-decoy corpus and grades it on TPR/FPR/patch-pass/repro/unsafe vs four baselines (B0 static, B1 LLM-single-pass, B2 fixed-agent, B3
$ npx -y skills add ruvnet/claude-flow --skill harness-security-bench --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/harness-security-benchContext preview
The summary Claude sees to decide when to auto-load this skill.
Run `@metaharness/darwin security bench` (upstream "Darwin Shield" / ADR-155) — evolves a champion security-detection harness against a 10-vuln / 9-decoy corpus and grades it on TPR/FPR/patch-pass/repro/unsafe vs four baselines (B0 static, B1 LLM-single-pass, B2 fixed-agent, B3
name: harness-security-bench description: Run `@metaharness/darwin security bench` (upstream "Darwin Shield" / ADR-155) — evolves a champion security-detection harness against a 10-vuln / 9-decoy corpus and grades it on TPR/FPR/patch-pass/repro/unsafe vs four baselines (B0 static, B1 LLM-single-pass, B2 fixed-agent, B3 Darwin-champion). Closest reference implementation for ruflo's own ADR-155 nightly self-learning security harness (PR #2417). Degrades gracefully when @metaharness/darwin is absent. argument-hint: "[--population 2] [--cycles 1] [--seed N] [--alert-on-fail]" allowed-tools: Bash
Surfaces the upstream `metaharness-darwin security bench` command. **This is the upstream's own ADR-155 — Darwin Shield — and is the closest reference implementation for ruflo's nightly self-learning security harness ([#2417](https://github.com/ruvnet/ruflo/pull/2417)).**
ruflo's ADR-155 proposes three learning loops (per-dimension confidence, severity calibration, auto-fix bid). Loop A trains on accumulated `(finding, dimension, human_outcome)` tuples — but the gradient signal is only sound if the underlying detection mechanism converges on a known-good corpus. Darwin Shield evolves exactly that mechanism on a 10-vuln/9-decoy ground-truth set. Running this nightly gives us:
TPR=1/FPR=0 on the bench corpus, our Loop A's reward signal is noise.
when the security landscape (or our mutator policy) shifts.
points to weight per-dimension confidence against.
Implementation: [`scripts/security-bench.mjs`](../../scripts/security-bench.mjs).
1. Shell to `npx -y @metaharness/darwin@~0.8.0 metaharness-darwin security bench --population N --cycles N [--seed S]`. 2. Default timeout = `3s × 19 evaluations × population × cycles + 30s overhead`. At default `--population 2 --cycles 1` ≈ 144s; at `--population 4 --cycles 3` ≈ 12 min. 3. Parse the markdown report — overall PASS/FAIL plus per-gate pass/fail rows (gate examples: "TPR improvement ≥ 25% vs fixed", "FPR reduction ≥ 40%", "Patch-test pass rate ≥ 80%", "Reproduction success ≥ 90%", "Unsafe outputs = 0", "Cost increase ≤ 2× fixed", "Beyond SOTA: champion statistically beats previous champion", "Compounding: false-positive repeat-rate drop ≥ 35%"). 4. Parse the baselines-vs-champion table (4 rows: fitness/TPR/FPR/patchPass/ repro/unsafe/cost per harness). 5. Emit structured JSON. With `--alert-on-fail`, exit 1 when overall = FAIL.
{
"success": true,
"data": {
"overall": { "ok": true, "icon": "✅" },
"gates": {
"total": 11,
"passed": 11,
"failed": 0,
"details": [{ "ok": true, "criterion": "TPR improvement ≥ 25% vs fixed harness", "measured": "+150% (B2 0.4 → B3 1)" }, ...]
},
"baselines": [
{ "harness": "static-only", "fitness": 0.5665, "tpr": 0.3, "fpr": 1, "unsafe": 0, ... },
{ "harness": "LLM single-pass", "fitness": 0.1365, ... },
{ "harness": "fixed agent", "fitness": 0.598, ... },
{ "harness": "Darwin champion", "fitness": 0.93275, "tpr": 1, "fpr": 0, ... }
],
"rawMarkdown": "...",
"shape": { "population": 2, "cycles": 1, "seed": null },
"durationMs": 142000
}
}The ADR-155 nightly workflow (per #2418 task `W1.5`) will spawn this as one of the active-pentest dimension's calls — its results become a trajectory record:
{
"dimension": "mcp-pentest",
"subdimension": "darwin-shield-bench",
"champion_fitness": 0.93275,
"champion_tpr": 1, "champion_fpr": 0,
"gates_passed": 11, "gates_failed": 0,
"shape": { "population": 4, "cycles": 3 }
}Loop A learns: if `darwin-shield-bench` consistently passes on the seeded corpus, weight findings caught only by `mcp-pentest` higher.
| Code | Meaning | |---|---| | 0 | Bench ran (overall PASS or FAIL — distinguish via JSON `overall.ok`), or degraded | | 1 | `--alert-on-fail` and `overall.ok === false` | | 2 | Config error or upstream infrastructure failure |
When `@metaharness/darwin` is absent, emits `{degraded: true, reason: 'metaharness-darwin-not-available'}` and exits 0.
An agent meta-harness for Claude Code and Codex. 📖 RuFlo Explained — Build an AI Team That Plans, Remembers, Tests, and Improves A 14-chapter guide: from the basic idea to a first useful task, then memory, agent teams, plugins, cost and verification.
Repo: ruvnet/claude-flow
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