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 agentmods add skills/jatingargiitk/evoagents/v1npx skills add jatingargiitk/evoagents --skill v1git clone --depth 1 https://github.com/jatingargiitk/evoagentsWrote 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/jatingargiitk/evoagents/v1)<a href="https://agentmods.dev/skills/jatingargiitk/evoagents/v1"><img src="https://agentmods.dev/badge/skills/jatingargiitk/evoagents/v1.svg" alt="Measured on agentmods" 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 | $0.00015 | $0.00591 |
| Opus 5 | $0.00008 | $0.00296 |
| Sonnet 5 | $0.00003 | $0.00118 |
| Haiku 4.5 | $0.00002 | $0.00059 |
Grade A, and why
perception 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 5d 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
89% identical to perception — 16 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 — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Perception Skill
Analyze the user's question and extract structured information for downstream planning.
When to Use
USE this skill when:
- A new user question arrives and needs to be analyzed
- Downstream skills need structured context about the query
When NOT to Use
DON'T use this skill when:
- The question has already been analyzed by a prior perception pass
Constraints
- MUST output valid JSON matching the Output Format schema
- MUST set recency_required to true if the question asks about recent, current, or time-sensitive information
- MUST identify all key entities and topics in the question
- MUST include temporal/recency keywords (e.g. "latest", "recent", "2025", "current", "today") as entities when present in the question
- NEVER include speculation about the answer — only analyze the question itself
Output Format
Respond with ONLY a JSON object:
{
"intent": "What the user wants to know (1 sentence)",
"entities": ["key entities", "topics", "concepts"],
"constraints": ["time range", "domain", "format constraints"],
"recency_required": true,
"complexity": "simple | moderate | complex"
}
Examples
Query: "What happened in AI this week?" Expected output:
{
"intent": "Find recent AI news and developments from the past week",
"entities": ["artificial intelligence", "AI news", "this week"],
"constraints": ["time range: past week"],
"recency_required": true,
"complexity": "moderate"
}
Query: "What are the latest breakthroughs in quantum computing in 2025?" Expected output:
{
"intent": "Find the most recent quantum computing advances in 2025",
"entities": ["quantum computing", "breakthroughs", "latest", "2025"],
"constraints": ["time range: 2025", "domain: quantum computing"],
"recency_required": true,
"complexity": "moderate"
}
Query: "Explain how gradient descent works" Expected output:
{
"intent": "Explain the gradient descent optimization algorithm",
"entities": ["gradient descent", "optimization", "machine learning"],
"constraints": [],
"recency_required": false,
"complexity": "moderate"
}
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.
- 5d ago First seen · 91 lines · 15 tokens per session scan A 51334aff2797
perception is a skill published in the GitHub repository jatingargiitk/evoagents (2 stars, last pushed 6mo ago), licensed MIT. It adds 15 tokens to every session and 591 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to perception, differing in 16 lines, and is treated as a copy.
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