Overview Benchmark Contents Activity Versions 2 Ratings 0
See it in action One of 7 test cases the skill fixed. Same model, same prompt, with and without the skill.
Prompt After updating prompt memory files following a failure fix, what final step completes the self-editing cycle?
What was checked, without and with the skill Checked Without skill With skill Requires committing the memory change with an explanatory message Missed Passed
Show both answers
What's inside The instructions your model reads when the skill loads.
SKILL.md 49 lines single file
Self-Improvement Skill
"The software that writes itself."
1. Evolutionary Prompts (The Genome)
Treat the system_prompt and skill.md files as DNA .
Genes: Individual instructions (e.g., "Always use TDD").
Phenotype: The agent's actual behavior in a session.
Fitness Function: Did the user accept the code? Did the tests pass?
2. The Mutation Cycle
When the agent encounters a novel failure or success:
Selection: Identify the "Gene" (Prompt Instruction) responsible.
Mutation: Rewrite the instruction.
Example: Change "Write clean code" -> "Write clean code adhering to AIRBNB style guiding."
Crossover: Combine two successful skills into a new hybrid skill.
3. Recursive Self-Editing
The agent has permission to edit its own skill files.
Protocol:
Trigger: "I keep failing to import this library correctly."
Analysis: "My knowledge base is outdated."
Action: calling replace_file_content on my-language.skill.md.
Commit: "Updated skill memory with correct import syntax."
4. Safety Guardrails (Homeostasis)
To prevent "Cancer" (Runaway bad mutations):
Version Control: All skill edits must be git-committed.
Revert: If success_rate drops after a mutation, auto-revert.
Core Immutable: The "Prime Directives" (Safety, Obedience) cannot be mutated.
5. Implementation
def optimize_prompt(task_history):
# 1. Analyze Failure
failure_pattern = find_pattern(task_history, status="failed")
# 2. Propose Mutation
When it activates Automatically, when a task matches.
Use when: (1) A command or operation fails unexpectedly, (2) User corrects the agent ('No, that's wrong...', 'Actually...'), (3) User requests a capability that doesn't exist, (4) An external API or tool fails, (5) The agent realizes its knowledge is outdated or incorrect, (6) A better approach is discovered for a recurring task. Also review learnings before major tasks.
Track record Benchmark 18 / 21
test cases passed, vs 11 / 21 without
Adoption
Users 0
Times used 0 View activity → Safety and source details
SkillSafety ✓ 3 of 3 passed ✓ Code scan 15 of 15 checks clean
0 critical · 0 warning · 0 info — scanner v5 — 8/17/2026
View 15 scanner checks live_secret remote_code_execution reverse_shell data_exfiltration ssrf_metadata agent_snooping anti_refusal destructive_commands obfuscated_unicode unicode_homoglyph opaque_blob suspicious_url tool_overreach trigger_abuse prompt_injection_phrasing
✓ Intent Safe
LLM judge · does the skill do what it claims
✓ Content Clean
LLM judge · policy & harmful-content review
Source Imported majiayu000 / claude-skill-registry
skills/agent/self-improving-agent-ederheisler-agent-skills/SKILL.md
View on GitHub ↗ License MITImported 8/17/2026Version v2