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 ChuckSRQ/awesome-hermes-skills --skill agentic-self-improvementgit clone --depth 1 https://github.com/ChuckSRQ/awesome-hermes-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/chucksrq/awesome-hermes-skills/agentic-self-improvement)<a href="https://agentmods.dev/skills/chucksrq/awesome-hermes-skills/agentic-self-improvement"><img src="https://agentmods.dev/badge/skills/chucksrq/awesome-hermes-skills/agentic-self-improvement/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/chucksrq/awesome-hermes-skills/agentic-self-improvement"><img src="https://agentmods.dev/badge/skills/chucksrq/awesome-hermes-skills/agentic-self-improvement.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 | $0.00041 | $0.04104 |
| Opus 5 | $0.00020 | $0.02052 |
| Sonnet 5 | $0.00008 | $0.00821 |
| Haiku 4.5 | $0.00004 | $0.00410 |
Grade A, and why
agentic-self-improvement 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 12d 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 — 449 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agentic Self-Improvement Loop
Behavioral benchmarking + evolutionary self-improvement for Hermes Agent. Combines closed-loop guidance patching (from Hermes PR #6120) with the GEPA optimization framework from hermes-agent-self-evolution (NousResearch, MIT).
Core Loop
BENCHMARK → DIAGNOSE → PATCH → VERIFY → (auto-revert if regression)
↓
GEPA OPTIMIZE → EVAL DATASET → EVOLVE → DEPLOY
↓
SESSION MINE → LLM-AS-JUDGE → NEW BENCHMARKS
Two modes operate together:
- Guidance Patch Loop — fast, surgical, targets specific behavioral failures via guidance blocks
- Evolutionary Optimization Loop — principled, uses GEPA + DSPy to optimize skill text end-to-end
Part 1: Guidance Patch Loop
Core Loop
BENCHMARK → DIAGNOSE → PATCH → VERIFY → (auto-revert if regression)
Usage
# Run all benchmarks, show suggested patches (no changes made)
/self-improve
# Run all benchmarks, auto-apply if improvement, auto-revert if regression
/self-improve --mode=apply
# Run specific category only
/self-improve --benchmarks=mandatory_tool.yaml
# Run subset of categories
/self-improve --categories=mandatory_tool,act_dont_ask,no_hallucination
# Test a specific model
/self-improve --model=claude-sonnet-4
# Control parallelism (default: 4)
/self-improve --parallel=8
# View past results
/self-improve --view-results=2026-04-12_1400
# Revert last applied patch
/self-improve --revert
Options
| Flag | Default | Description |
|---|---|---|
--mode |
suggest |
suggest (show diff only) or apply (apply + verify) |
--benchmarks |
all | Benchmark YAML file(s) to run |
--categories |
all | Run only specific categories (by filename without .yaml) |
--model |
current | Model to benchmark |
--parallel |
4 | Number of parallel prompt executions |
--view-results |
none | Show a past run's results |
--revert |
false | Revert the last applied patch |
What ships with it
16 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- BENCHMARKS/act_dont_ask.yaml 2.0 KB
- BENCHMARKS/auth_state.yaml 1.6 KB
- BENCHMARKS/context_grounding.yaml 1.3 KB
- BENCHMARKS/mandatory_tool.yaml 2.2 KB
- BENCHMARKS/no_hallucination.yaml 1.7 KB
- BENCHMARKS/path_accuracy.yaml 1.6 KB
- BENCHMARKS/prerequisite.yaml 1.4 KB
- BENCHMARKS/remember_to_obsidian.yaml 1.7 KB
- BENCHMARKS/verification.yaml 1.4 KB
- README.md 3.8 KB
- REFERENCES/guidance_anatomy.md 4.5 KB
- src/__init__.py 85 B runs code
- src/apply_and_verify.py 9.5 KB runs code
- src/benchmark_runner.py 13 KB runs code
- src/failure_analyzer.py 4.7 KB runs code
- src/patch_generator.py 9.4 KB runs code
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.
- 12d ago First seen · 449 lines · 41 tokens per session scan A b7f620f3eed8
agentic-self-improvement is a skill published in the GitHub repository ChuckSRQ/awesome-hermes-skills (76 stars, last pushed 4mo ago), licensed MIT. It adds 41 tokens to every session and 4,104 once invoked, about $0.0002 per session on Opus 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 2026-08-30.
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