
AI Agent Self-Improvement Memory Auditor
by Shandra
Audits AI agent failures and converts recurring mistakes into durable rules, anti-patterns, regression tests, memory candidates, and improved SKILL.md sections.
- Convert user 'from now on' corrections into durable instruction patches.
- Generate regression test suites to ensure agents don't repeat old mistakes.
- Audit rejected marketplace skills to fix formatting and security flags.
$50
· or 250 creditsSecure checkout via Stripe
Included in download
- Convert user 'from now on' corrections into durable instruction patches.
- Generate regression test suites to ensure agents don't repeat old mistakes.
- file_write, file_read, browser automation included
- Ready for Compatible with ChatGPT Custom GPTs
Sample input
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.
Sample output
=== 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.
Audits AI agent failures and converts recurring mistakes into durable rules, anti-patterns, regression tests, memory candidates, and improved SKILL.md sections.
$50
· or 250 creditsSecure checkout via Stripe
Included in download
- Convert user 'from now on' corrections into durable instruction patches.
- Generate regression test suites to ensure agents don't repeat old mistakes.
- file_write, file_read, browser automation included
- Ready for Compatible with ChatGPT Custom GPTs
- Instant install
Sample input
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.
Sample output
=== 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.
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.
Use Cases
- Convert user 'from now on' corrections into durable instruction patches.
- Generate regression test suites to ensure agents don't repeat old mistakes.
- Audit rejected marketplace skills to fix formatting and security flags.
- Build an anti-pattern library to prevent common agent hallucinations.
- Review agent memory candidates to filter out sensitive or one-off data.
Known Limitations
This skill creates structured improvement recommendations, but it does not automatically modify live agent memory, production prompts, hidden system instructions, marketplace listings, or deployed agents unless the user applies the changes. It cannot guarantee that an agent will never repeat mistakes. Improvement quality depends on the clarity of failure examples, correction feedback, review cadence, regression tests, and human approval. Sensitive data should be summarized or removed before being turned into rules or memory candidates.
How to Install
mkdir -p ~/.claude/skills && curl -sL https://www.agensi.io/api/install/ai-agent-self-improvement-memory-auditor -o /tmp/ai-agent-self-improvement-memory-auditor.zip && unzip -o /tmp/ai-agent-self-improvement-memory-auditor.zip -d ~/.claude/skills && rm /tmp/ai-agent-self-improvement-memory-auditor.zipFree skills install directly. Paid skills require purchase - use the download button above after buying.
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Security Scanned
Passed automated security review
Permissions
File Scopes
This skill uses file access to read user-provided agent failures, bad outputs, correction notes, prompt drafts, rejected skill files, marketplace feedback, QA reports, support tickets, logs, and previous instruction versions. It uses write access to create structured Markdown/text outputs such as failure audits, durable operating rules, anti-pattern libraries, regression tests, memory candidate reviews, instruction patches, quality gates, learning logs, updated SKILL.md sections, and SKILL.md files. Browser access is optional and should only be used for public documentation or best-practice research when explicitly requested. The default safe setup does not require network access, shell access, or environment variable access.
Tags
Compatible with ChatGPT Custom GPTs, ChatGPT Agents, Claude-style workflows, Cursor, Claude Code, Codex CLI, OpenCode, Replit, Agensi-style skill marketplaces, PromptBase-style agents, internal AI governance workflows, and other AI systems that support structured Markdown instruction files such as SKILL.md. It can also be used manually in any AI chat by pasting the instructions.
Creator
Shandra is a top-ranked AI prompt creator and premium agent skill builder with an established track record in the AI marketplace. She is recognized as a #1 Top Seller on PromptBase, where she has built a trusted catalog of specialized AI prompts and agent skills for creators, entrepreneurs, educators, marketers, digital product sellers, and business professionals. With over 3,000 AI products published, more than 3,000 sales, and 1,000+ five-star reviews, Shandra has become known for creating practical, polished, and commercially useful AI resources that help users save time, organize complex ideas, generate high-quality content, build digital products, and transform creative concepts into actionable workflows. Her Agensi store focuses on premium, ready-to-use agent skills designed for real-world productivity. Each skill is developed with clear instructions, structured workflows, professional formatting, practical use cases, setup guidance, examples, edge-case handling, and a strong emphasis on usability. Her work combines creative strategy, prompt engineering, documentation design, business thinking, and practical automation into reliable tools that users can apply immediately. Shandra’s mission is to create AI skills that feel professional, useful, and complete from the first use — not generic templates, but carefully built workflow systems that help users think better, work faster, and produce stronger results.
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