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 Bilal140202/the-lord-of-the-skills --skill emagi6395__skillsgit clone --depth 1 https://github.com/Bilal140202/the-lord-of-the-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/bilal140202/the-lord-of-the-skills/emagi6395__skills)<a href="https://agentmods.dev/skills/bilal140202/the-lord-of-the-skills/emagi6395__skills"><img src="https://agentmods.dev/badge/skills/bilal140202/the-lord-of-the-skills/emagi6395__skills/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/bilal140202/the-lord-of-the-skills/emagi6395__skills"><img src="https://agentmods.dev/badge/skills/bilal140202/the-lord-of-the-skills/emagi6395__skills.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 | $0.00176 | $0.01807 |
| Opus 5.5 | $0.00070 | $0.00723 |
| Sonnet 5.5 | $0.00035 | $0.00361 |
| Haiku 4.5 | $0.00018 | $0.00181 |
Grade A, and why
evo-search 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
100% identical to evo-search — 0 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 — 189 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Evo-Search
Runs a genetic-algorithm-style loop over candidate responses: generate a diverse initial population, score each against a rubric, then repeatedly select, crossover, and mutate to improve quality across generations. Output the top 3 final solutions.
Step 0: Problem Intake & Rubric
Identify problem type, constraints, audience, and scope. If ambiguous, ask one question.
Rubric Construction
Detect which mode applies:
| Mode | Trigger | Action |
|---|---|---|
| A: Auto | User gave only the problem | Generate 4–6 domain-appropriate criteria |
| B: Guided | User hinted at priorities | Generate rubric, weight toward stated priorities |
| C: Manual | User gave explicit criteria | Convert each into a scored rubric entry with anchors |
In all modes: augment vague criteria into scorable definitions, and always add:
Overall Fitness (30%): "Would a knowledgeable expert prefer this over a competent but unremarkable response?" Scored holistically. Prevents narrow-criteria gaming.
Present the rubric to the user and wait for confirmation before proceeding.
Rubric format:
| Criterion | Description | Weight | Max |
|----------------|--------------------------|--------|-----|
| [Name] | [Definition + anchors] | X% | 10 |
| Overall Fitness| Expert holistic score | 30% | 10 |
Weighted Total = Σ(score × weight) [max = 10.00]
Step 1: Initial Population
Generate 6 candidates (default) using these diversity frames, one per candidate:
| # | Frame |
|---|---|
| 1 | Conventional / mainstream |
| 2 | Contrarian / challenges assumptions |
| 3 | First-principles / bottom-up |
| 4 | Analogy-led / draws from another domain |
| 5 | Risk-focused / emphasizes what could go wrong |
| 6 | Synthesis / combines multiple angles |
Generate all candidates before scoring any. Then score each and display:
GENERATION 0
| # | Frame | Fitness | Strength | Weakness |
|---|--------------|---------|----------------|---------------|
| 1 | Conventional | X.X | [one phrase] | [one phrase] |
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 · 189 lines · 176 tokens per session scan A 7da690a68b90
evo-search is a skill published in the GitHub repository Bilal140202/the-lord-of-the-skills (6 stars, last pushed 1mo ago), licensed MIT. It adds 176 tokens to every session and 1,807 once invoked, about $0.0007 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to evo-search, differing in 0 lines, and is treated as a copy.
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