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SKILL verified MIT Self-run

Peer Review

skill-nutdnuy-self-driving-portfolio-skill-peer-review · by nutdnuy

This skill should be used when the user asks to "review portfolio proposals", "rank strategies with Borda voting", "filter IPS violations", or test a candidate portfolio against an adversarial diversifier.

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Install

$ agentstack add skill-nutdnuy-self-driving-portfolio-skill-peer-review

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • ✓ Prompt-injection patterns
  • ✓ Secret / credential exfiltration
  • ✓ Dangerous shell & filesystem operations
  • ✓ Untrusted network calls
  • ✓ Known-malicious package signatures

What it can access

  • ✓ Network access No
  • ✓ Filesystem access No
  • ✓ Shell / process execution No
  • ✓ Environment & secrets No
  • ✓ Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

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Reliability & compatibility

✓ Security review passed
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● 26d ago

Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

Preview Execution monitoring

We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.

How agent discovery & health will work →
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About

peer-review

Implements the multi-agent strategy review protocol from Ang/Azimbayev/Kim (2026) §3.1 step 5.

Steps

  1. Hard-constraint filter (drop infeasible proposals).
  2. Risk filter (drop proposals with vol > IPS hard cap).
  3. Three-axis review: a deterministic rubric rates every surviving

proposal 1–5 on risk-adjusted return, diversification, robustness.

  1. Borda count: per-axis rankings are converted to tie-aware Borda points;

points are summed across the three axes.

  1. Adversarial diversifier: if the Borda winner has effective N 50%, generate an IPS-projected equal-weight challenger. Mark

it as winning the concentration challenge only when effective N improves by more than 20%.

  1. Output the top-K (default 5) survivors plus the breakdown.

CLI

python skills/peer-review/scripts/peer_review.py \
       --proposals outputs/demo01/pc_proposals.json \
       --ips ips/ips_template.md \
       --top-k 5 \
       --vol-cap 0.18 \
       --out outputs/demo01/peer_review.json

Run through pipeline/orchestrator.py for schema gating and governed output.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.

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