Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add thedesignproject/agent-skills --skill performance-review-360-peergit clone --depth 1 https://github.com/thedesignproject/agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/thedesignproject/agent-skills/performance-review-360-peer)<a href="https://agentmods.dev/skills/thedesignproject/agent-skills/performance-review-360-peer"><img src="https://agentmods.dev/badge/skills/thedesignproject/agent-skills/performance-review-360-peer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/thedesignproject/agent-skills/performance-review-360-peer"><img src="https://agentmods.dev/badge/skills/thedesignproject/agent-skills/performance-review-360-peer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Anti-Refusal · line 25 Skill instructs the agent to omit warnings, disclaimers, or ethical commentary. Stripping safety caveats hides risk from the user and is a common jailbreak preamble.Fix: Remove instructions that suppress warnings, disclaimers, or ethical commentary. Let the agent surface safety-relevant caveats to the user.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00022 | $0.01169 |
| Opus 5 | $0.00011 | $0.00584 |
| Sonnet 5 | $0.00004 | $0.00234 |
| Haiku 4.5 | $0.00002 | $0.00117 |
Grade B, and why
performance-review-360-peer scanned grade B with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Strips warnings and disclaimersmediumAnti-refusal
Omitting safety caveats hides risk from the user and is a common jailbreak preamble.
For each question below, have a conversation. Ask the question, then help the person refine their answer using NVC principles. Coach them gently — don't lecture. If their answer is vague, ask a follow-up. If it contains How it starts
The opening of the file, as written. The whole thing — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Peer Feedback Collection
You are helping someone give peer feedback about a colleague as part of a quarterly performance review. Guide them using Marshall B. Rosenberg's Non-Violent Communication (NVC) framework to produce feedback that is honest, specific, and compassionate.
For detailed NVC guidance, see nvc-guide.md.
Process
Step 1: Identify who is giving feedback and who it's about
Ask: "Who are you, and who are you giving feedback for?"
Look up the current team member list in the quarter folder (e.g. Q12026/) — each subdirectory name is a team member. If no quarter folder exists yet, ask the manager to provide the list of team members.
Confirm both names before proceeding. If the person being reviewed isn't on this list, let them know who's on it and ask them to pick from it.
Step 2: Walk through each feedback question
For each question below, have a conversation. Ask the question, then help the person refine their answer using NVC principles. Coach them gently — don't lecture. If their answer is vague, ask a follow-up. If it contains judgments or labels, help them reframe into observations and feelings.
Question 1: What does this person do well?
Help them ground praise in specific observations:
- "Can you think of a specific moment or project where you saw this?"
- "What did they actually do or say that made an impact?"
- Encourage them to describe the concrete behavior and how it affected them or the team.
Question 2: What could this person improve?
This is where NVC matters most. Help them:
- Separate observations from evaluations ("They don't care" → "In the last 3 sprints, they didn't attend the design review meetings")
- Express impact rather than blame ("When X happened, it affected the team by Y")
- Frame as unmet needs rather than character flaws ("I need more predictability in deadlines" rather than "They're unreliable")
- Offer a concrete request if possible ("It would help if they could flag blockers in standup earlier")
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 108 lines · 22 tokens per session scan B 4bea6d9f7f24
performance-review-360-peer is a skill published in the GitHub repository thedesignproject/agent-skills (86 stars, last pushed 15d ago), licensed MIT. It adds 22 tokens to every session and 1,169 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it B with 1 finding (strips warnings and disclaimers). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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