Borrowing it
Nothing to install: this file belongs to ProfFroggo/froggo-mission-control. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/ProfFroggo/froggo-mission-control/dev/.claude/skills/agent-evaluation/SKILL.mdgit clone --depth 1 https://github.com/ProfFroggo/froggo-mission-controlWrote 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/proffroggo/froggo-mission-control/agent-evaluation)<a href="https://agentmods.dev/skills/proffroggo/froggo-mission-control/agent-evaluation"><img src="https://agentmods.dev/badge/skills/proffroggo/froggo-mission-control/agent-evaluation/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/proffroggo/froggo-mission-control/agent-evaluation"><img src="https://agentmods.dev/badge/skills/proffroggo/froggo-mission-control/agent-evaluation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00030 | $0.01149 |
| Opus 5.5 | $0.00012 | $0.00460 |
| Sonnet 5.5 | $0.00006 | $0.00230 |
| Haiku 4.5 | $0.00003 | $0.00115 |
Grade A, and why
agent-evaluation scanned grade A with 0 findings 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 today.
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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Evaluation
Purpose
Maintain a healthy, well-defined agent team. Catch stale definitions, identify capability gaps, surface underperforming agents, and ensure every agent has the skills and scope needed to do their job.
Trigger Conditions
Load this skill when:
- Asked to evaluate or audit a specific agent
- Reviewing the full agent roster for coverage gaps
- Onboarding a new agent (to check for overlap or missing skills)
- Reviewing agent task history for quality or performance
- Generating an HR report on team health
Procedure
Step 1 — Load Agent Context
For each agent under review:
- Read their CLAUDE.md / soul file in
.claude/agents/<name>.md - Read their memory file at
~/mission-control/agents/<name>/MEMORY.mdif it exists - Check task history via
mcp__mission-control-db__task_list { "assignedTo": "<agent-name>" } - Note: assigned skills, role description, tool access, and output paths
Step 2 — Evaluate Definition Quality
Score each agent definition across these dimensions:
| Dimension | What to Check | Score (1-3) |
|---|---|---|
| Clarity | Is the role clearly defined? Can you tell what this agent does vs doesn't do? | |
| Completeness | Does it have: character, responsibilities, workflow, memory protocol, library output? | |
| Skills Coverage | Are the right skills listed? Are there obvious gaps? | |
| Scope Boundaries | Is it clear what this agent escalates vs handles solo? | |
| Collaboration Norms | Are the key collaborators named? Are handoff rules defined? |
Scoring: 1 = needs revision, 2 = acceptable, 3 = strong
Step 3 — Review Task History
For the trailing 30 days:
- How many tasks were assigned?
- What percentage were completed vs stalled vs human-reviewed?
- Were any tasks escalated that this agent should have handled?
- Were tasks completed without logging activity? (anti-pattern — flag it)
Quality signals to check:
- Task output saved to correct library path
- Activity logged during execution (not just at end)
- Status transitions followed the correct pipeline
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.
- today First seen · 132 lines · 30 tokens per session scan A 392f1ed7ecc9
agent-evaluation is a skill published in the GitHub repository ProfFroggo/froggo-mission-control (2 stars, last pushed 6mo ago), licensed Apache-2.0. It adds 30 tokens to every session and 1,149 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-11-07.
agent-evaluation compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| agent-evaluationProfFroggo/froggo-mission-control | Skill | 2 | 6mo ago | Apache-2.0 | A | 30 | Claude Code |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 103 | Claude Code |