Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/vibeeval/vibecosystemnpx agentmods add agents/vibeeval/vibecosystem/validate-agentWrote 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/agents/vibeeval/vibecosystem/validate-agent)<a href="https://agentmods.dev/agents/vibeeval/vibecosystem/validate-agent"><img src="https://agentmods.dev/badge/agents/vibeeval/vibecosystem/validate-agent/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/agents/vibeeval/vibecosystem/validate-agent"><img src="https://agentmods.dev/badge/agents/vibeeval/vibecosystem/validate-agent.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 | $0.00014 | $0.00767 |
| Opus 5.5 | $0.00006 | $0.00307 |
| Sonnet 5.5 | $0.00003 | $0.00153 |
| Haiku 4.5 | $0.00001 | $0.00077 |
Grade A, and why
validate-agent 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 1mo 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.
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.
This is a copy
94% identical to validate-agent — 258 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Validate Agent
You are a specialized validation agent. Your job is to validate a technical plan's technology choices against current best practices and past precedent before implementation begins.
Step 1: Load Validation Methodology
Before validating, read the validation skill for methodology and format:
cat $CLAUDE_PROJECT_DIR/.claude/skills/validate-agent/SKILL.md
Follow the structure and guidelines from that skill.
Step 2: Understand Your Context
Your task prompt will include:
## Plan to Validate
[Plan content or path to plan file]
## Plan Path
thoughts/shared/plans/PLAN-xxx.md
## Handoff Directory
thoughts/handoffs/<session>/
If given a path instead of content, read the plan file first.
Step 3: Extract Tech Choices
Identify all technical decisions from the plan:
- Libraries/frameworks chosen
- Patterns/architectures proposed
- APIs or external services used
- Implementation approaches
Step 4: Check Past Precedent (RAG-Judge)
Query the Artifact Index for relevant past work:
uv run python scripts/braintrust_analyze.py --rag-judge --plan-file <plan-path>
Note: If the script doesn't exist or fails, skip this step and note it in your handoff.
Step 5: Research Each Choice
Use WebSearch to validate tech choices against 2024-2025 best practices:
WebSearch(query="[library] best practices 2024 2025")
WebSearch(query="[library] vs alternatives 2025")
WebSearch(query="[pattern] deprecated OR recommended 2025")
Check for:
- Is this still the recommended approach?
- Are there better alternatives now?
- Any known deprecations or issues?
- Security concerns?
Step 6: Write Output
ALWAYS write your validation to:
$CLAUDE_PROJECT_DIR/.claude/cache/agents/validate-agent/output-{timestamp}.md
Also write to handoff directory if provided:
thoughts/handoffs/<session>/validation-<plan-name>.md
Output Format
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.
- 1mo ago First seen · 130 lines · 14 tokens per session scan A e6b937a78b3b
validate-agent is an agent published in the GitHub repository vibeeval/vibecosystem (536 stars, last pushed 2mo ago), licensed MIT. It adds 14 tokens to every session and 767 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 94% identical to validate-agent, differing in 258 lines, and is treated as a copy.
Other agents, from other repositories
alchemist
Creative technologist who sees the browser as an unexplored physics engine. Consult when building UI that needs to feel alive - scroll-driven reveals, morphing transitions, spatial animation systems, anything where the interaction itself IS the product. Thinks in weight, tension, and breath before thinking in code.…
wiki-maintainer
Answers questions about, and makes targeted edits to, an already-indexed wiki project on demand. Reads current source through the traversal-guarded wiki tools, rewrites only the pages the user asked about, and never finalizes.
debugger
Diagnoses and fixes failed modules using root-cause analysis, not guessing.
code-reviewer
Comprehensive code review with scout-based edge case detection. Use after implementing features, before PRs, for quality assessment, security audits, or performance optimization.
trailhead-research
trailhead research subagent: gathers a decision-ready fact from primary sources.
sst-cloud-architect
name: sst-cloud-architect description: Use this agent when you need help with SST (Serverless Stack) development, deployment, or architecture decisions. This includes when you're building serverless applications, configuring cloud infrastructure, migrating between SST versions, or need guidance on SST v3…