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AI Agent Self-Improvement Memory Auditor
Audits AI agent failures and converts recurring mistakes into durable rules, anti-patterns, regression tests, memory candidates, and improved SKILL.md sections.
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My Agensi skill was rejected because the SKILL.md file had no YAML frontmatter and the security review flagged unsafe environment access wording. Convert this failure into a durable rule, anti-pattern, regression test, instruction patch, and memory candidate review.
Your agent does
=== AI AGENT FAILURE AUDIT ===
Failure summary: An Agensi SKILL.md submission was rejected because it lacked YAML frontmatter and contained wording that triggered unsafe environment access concerns.
Original request: Create a SKILL.md ZIP file for Agensi.
Expected behavior: The ZIP should contain a valid SKILL.md file with required YAML frontmatter and safe permission wording.
Actual behavior: The file missed YAML frontmatter and included platform-sensitive wording.
Feedback/correction: The marketplace security review rejected the skill and requested proper frontmatter plus safer permission and security language.
Severity: Critical
Failure category:
- Marketplace rejection failure
- Format failure
- Safety or compliance failure
Root cause: The agent created the skill content but did not run an Agensi-specific preflight validation checklist before packaging the ZIP.
Durable lesson: For every Agensi SKILL.md ZIP submission, the agent must include YAML frontmatter and run a safety wording check before packaging.
New operating rule: Rule name: Agensi SKILL.md Safe Packaging Rule
Trigger: Whenever creating or revising a SKILL.md ZIP for Agensi.
Required behavior: Include YAML frontmatter with name, description, and tags. Use safe default permissions. Avoid wording that implies direct secret-value access, open-ended network use, unsafe command execution, or autonomous high-risk actions unless explicitly required and safely documented.
Forbidden behavior: Do not package a SKILL.md ZIP without frontmatter. Do not include risky permission language by default.
Rationale: Agensi security review may reject skills with missing metadata or unsafe patterns.
Anti-pattern: Platform-Blind Packaging
What it looks like: The skill body is detailed, but marketplace-specific metadata and security requirements are missing.
Why it fails: The marketplace rejects the submission even if the content is useful.
Replace with: Create the content and run platform-specific validation before packaging.
Regression test: Test name: Agensi Safe ZIP Validation
Test prompt: Create a DevOps SKILL.md ZIP for Agensi.
Expected behavior: The agent includes YAML frontmatter, safe permissions, no direct secret-value access, no open-ended network requirement, and exactly one SKILL.md file in the ZIP.
Failure condition: Missing frontmatter or unsafe permission wording.
Pass criteria: ZIP contains SKILL.md with valid frontmatter and safe permission model.
Instruction patch: Add under packaging rules: Before delivering any Agensi ZIP, verify that SKILL.md includes YAML frontmatter with name, description, and tags, and that permissions are minimal and justified.
Memory candidate: Yes
Memory recommendation: Store the generalized rule that future Agensi SKILL.md ZIP files should include YAML frontmatter and safe permission guidance by default.
Conflict check: No conflict. This strengthens platform compliance.
Verification plan: Recreate the rejected ZIP and resubmit after validating frontmatter and safety wording.
Final recommendation: Add this rule to the creator’s general Agensi skill-building workflow and run it before every future ZIP delivery.
What you get
About this skill
AI Agent Self-Improvement Memory Auditor helps AI-agent builders, workflow designers, founders, prompt engineers, automation consultants, and product teams turn failures into systematic improvements. It analyzes bad outputs, user corrections, rejected submissions, failed workflows, marketplace feedback, repeated mistakes, and agent quality issues, then creates root-cause audits, durable operating rules, anti-pattern libraries, regression tests, memory candidate reviews, instruction patches, quality gates, learning logs, and updated SKILL.md sections. The skill is ideal for improving Custom GPTs, ChatGPT Agents, Cursor rules, Claude Code instructions, Codex CLI workflows, agent marketplaces, prompt systems, and internal AI products that need to become more consistent over time.
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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