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 gitstq/awesome-ai-agent-skills --skill self-evolver-progit clone --depth 1 https://github.com/gitstq/awesome-ai-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/gitstq/awesome-ai-agent-skills/self-evolver-pro)<a href="https://agentmods.dev/skills/gitstq/awesome-ai-agent-skills/self-evolver-pro"><img src="https://agentmods.dev/badge/skills/gitstq/awesome-ai-agent-skills/self-evolver-pro/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/gitstq/awesome-ai-agent-skills/self-evolver-pro"><img src="https://agentmods.dev/badge/skills/gitstq/awesome-ai-agent-skills/self-evolver-pro.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 | $0.00148 | $0.02574 |
| Opus 5.5 | $0.00059 | $0.01030 |
| Sonnet 5.5 | $0.00030 | $0.00515 |
| Haiku 4.5 | $0.00015 | $0.00257 |
Grade A, and why
self-evolver-pro 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.
How it starts
The opening of the file, as written. The whole thing — 316 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Self Evolver Pro - Intelligent Agent Evolution Engine
Don't just use AI. Make AI learn from every mistake and get better over time.
What Problem Does This Solve?
Every AI coding agent starts fresh each session. You correct the same mistakes, repeat the same preferences, and explain the same conventions over and over. Self Evolver Pro solves this by giving your agent a persistent, intelligent memory that:
- Captures experiences automatically (corrections, errors, preferences)
- Deduplicates intelligently using semantic similarity (not just string matching)
- Promotes high-value patterns into reusable knowledge
- Compacts old knowledge to prevent context bloat
- Transfers learnings across projects
- Reviews itself periodically to stay relevant
How It Works
Architecture Overview
User Interaction
|
v
[Signal Capture Layer] -- captures LRN/ERR/FEAT/PREF signals
|
v
[Deduplication Engine] -- semantic similarity check
|
v
[Knowledge Store] -- .evolver/ directory structure
| ├── signals/ (raw captured signals)
| ├── patterns/ (deduplicated, promoted patterns)
| ├── context/ (active context for current session)
| └── review/ (periodic review queue)
|
v
[Context Manager] -- loads relevant knowledge at session start
|
v
[Compaction Engine] -- prevents knowledge bloat over time
|
v
[Promotion Engine] -- promotes patterns to skills when ready
Signal Types
| Signal | Trigger | Example | Action |
|---|---|---|---|
| LRN (Learning) | User corrects the agent | "Don't use var, use const/let" | Store as learning pattern |
| ERR (Error) | Command fails or exception | npm run build returns exit code 1 |
Store with error context and fix |
| FEAT (Feature Request) | User asks for something new | "Can you also check TypeScript types?" | Queue as capability request |
| PREF (Preference) | User expresses a preference | "I prefer functional over OOP" | Store as project preference |
| PATTERN (Pattern) | Recurring successful solution | Same fix applied 3+ times | Promote to reusable pattern |
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 · 316 lines · 148 tokens per session scan A b39c813f5b0a
self-evolver-pro is a skill published in the GitHub repository gitstq/awesome-ai-agent-skills (1 stars, last pushed 7mo ago), licensed MIT. It adds 148 tokens to every session and 2,574 once invoked, about $0.0006 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 2026-08-31.
Other skills, from other repositories
knowledge-ingest
Ingest documents, articles, conversations, or supplied text into Knowledge with retained originals, sourced claims, entity updates, timelines, and cross-links; also learn durable lessons from a set of conversations, transcripts, or past sessions (/learn), including the Learn from conversations and Learn from Memory…
mem0-oss-to-platform
Plan, then execute, a migration of a project from the mem0 open-source / self-hosted SDK (local Memory class) to the mem0 Platform / hosted SDK (MemoryClient). TRIGGER when: the developer wants to move, switch, or migrate mem0 off OSS/self-hosted to the hosted API, e.g. "migrate my mem0 setup to the platform", "switch…
forget
Remove a personal fact, preference, relationship detail, topic, or prior event from Muse's active memory and stop existing copies or automations from bringing it back. Use for explicit requests such as 'forget that', 'don't remember this about me', or 'remove that from your memory'. Do not use when 'forget it' merely…
Cortex
LifeOS memory, prior-session recall, and learning capture. USE WHEN capture this learning, document this learning, what did we learn recently, recent learnings, cortex, knowledge, knowledge base, search knowledge, what do we know about, archive, harvest, knowledge status, develop note, add to knowledge, add a people…
memory
Use when the user asks to remember, recall, forget, update, search, or inspect durable OpenSquilla memory, including profile facts in USER.md and long-term notes in MEMORY.md or memory//.md.
ha-data-stores
Map of Hope Agent's local data stores and safe read-only query workflow. Use when the user asks where Hope Agent stores data, wants to inspect sessions/messages/memory/logs/background jobs/knowledge indexes/settings, asks the model to query local app data, or debugging requires checking persisted state. Trigger…