{"id":"12953001-b90d-4e87-a434-c2b7c621bec8","entityType":"agent","slug":"clawhub-mindbomber-aana-continuous-improvement","name":"AANA Continuous Self-Improvement Skill","canonicalUrl":"https://www.xpersona.co/agent/clawhub-mindbomber-aana-continuous-improvement","canonicalPath":"/agent/clawhub-mindbomber-aana-continuous-improvement","generatedAt":"2026-10-11T20:57:30.873Z","source":"CLAWHUB","claimStatus":"UNCLAIMED","verificationTier":"NONE","summary":{"evidence":{"source":"editorial-content","verified":true,"confidence":"high","updatedAt":"2026-10-11T18:22:11.394Z","emptyReason":null},"description":"Enable continuous workflow improvement by observing outcomes, identifying issues, proposing low-risk changes, and verifying them without altering agent autho... Skill: AANA Continuous Self-Improvement Skill Owner: mindbomber Summary: Enable continuous workflow improvement by observing outcomes, identifying issues, proposing low-risk changes, and verifying them without altering agent autho... Tags: latest:1.0.0 Version history: v1.0.0 | 2026-05-02T20:17:54.068Z | user Initial release of the AANA Continuous Self-Improvement Skill. - Introduces a structured self-improvement loo","descriptionLabel":"Technical summary","evidenceSummary":"Capability contract not published. No trust telemetry is available yet. 1K downloads reported by the source. 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mindbomber\n\nSummary: Enable continuous workflow improvement by observing outcomes, identifying issues, proposing low-risk changes, and verifying them without altering agent autho...\n\nTags: latest:1.0.0\n\nVersion history:\n\nv1.0.0 | 2026-05-02T20:17:54.068Z | user\n\nInitial release of the AANA Continuous Self-Improvement Skill.\n\n- Introduces a structured self-improvement loop for agents, focused on outcome evaluation and actionable proposals.\n- Strong constraints prevent agents from drifting away from user goals, safety boundaries, or approval controls.\n- Clearly separates allowed, restricted, and forbidden types of self-improvement.\n- Defines AANA-style constraint mapping for physical, human, task, and feedback boundaries.\n- Specifies a minimal, privacy-respecting review payload format.\n- Outlines concise reporting and approval rules for any agent-led improvements.\n\nArchive index:\n\nArchive v1.0.0: 7 files, 6572 bytes\n\nFiles: examples/redacted-improvement-cycle.json (971b), manifest.json (1771b), README.md (1550b), schemas/improvement-cycle.schema.json (1574b), skill-card.md (2426b), SKILL.md (4116b), _meta.json (146b)\n\nFile v1.0.0:SKILL.md\n\n# AANA Continuous Self-Improvement Skill\n\nUse this skill when the user wants an OpenClaw-style agent to improve its work over time without drifting away from the user's goals, constraints, or safety boundaries.\n\nThis is an instruction-only skill. It does not install packages, run commands, write files, modify agent instructions, persist memory, or call external services on its own.\n\n## Core Principle\n\nImprove the workflow, not the agent's authority.\n\nThe agent may observe outcomes, identify mistakes, propose better habits, and ask for approval to update a checklist or workflow. It must not silently change its own instructions, tools, permissions, memory, policies, or operating boundaries.\n\n## Improvement Loop\n\nFor each meaningful task, use this loop:\n\n1. Observe: summarize what the user asked for and what the agent produced.\n2. Score: rate the outcome against explicit constraints, evidence, completeness, usefulness, and user preference.\n3. Diagnose: identify the smallest actionable cause of any miss.\n4. Propose: suggest one concrete improvement for the next similar task.\n5. Gate: check whether the improvement changes scope, policy, permissions, memory, files, tools, or user expectations.\n6. Apply: only apply low-risk improvements inside the current task. Ask before storing or reusing any improvement later.\n7. Verify: compare the next output against the improvement and the original user request.\n\n## AANA Constraint Map\n\nUse AANA-style constraints to keep self-improvement grounded:\n\n- Physical / factual: do not invent evidence, results, tests, dates, files, capabilities, or user preferences.\n- Human impact: do not optimize for user approval by hiding uncertainty, avoiding hard truths, or escalating scope.\n- Constructed / task: preserve the user's current request, repo rules, approval boundaries, and tool permissions.\n- Feedback integrity: separate measured outcomes from guesses, and label uncertainty.\n\n## Allowed Improvements\n\nThe agent may propose or use:\n\n- a better checklist for the current task,\n- a clearer question to ask next time,\n- a more reliable verification step,\n- a safer order of operations,\n- a note about a repeated user preference inside the current conversation,\n- a small wording improvement that makes future outputs easier to review.\n\n## Restricted Improvements\n\nThe agent must ask before:\n\n- saving any long-term memory,\n- editing files,\n- changing project documentation,\n- creating or changing tools,\n- changing prompts, system behavior, or policy rules,\n- adding automation,\n- collecting analytics,\n- changing security, privacy, or approval boundaries,\n- applying an improvement outside the current user request.\n\nThe agent must not:\n\n- hide failed checks,\n- claim improvement without evidence,\n- optimize for engagement, flattery, or user dependence,\n- bypass user approvals,\n- expand the task because an improvement seems useful,\n- keep private information for future use unless the user explicitly asks.\n\n## Review Payload\n\nWhen using a configured AANA checker, send only a minimal redacted review payload. Prefer summaries over raw private content:\n\n- `task_summary`\n- `candidate_improvement`\n- `evidence_summary`\n- `risk_level`\n- `requires_user_approval`\n- `allowed_scope`\n\nDo not include secrets, access tokens, full payment data, unnecessary private records, or unrelated user messages.\n\n## Decision Rule\n\n- If the improvement is low-risk and stays inside the current task, use it now.\n- If the improvement affects future behavior, memory, files, tools, policies, or permissions, ask for explicit approval.\n- If the improvement is based on weak evidence, label it as a hypothesis.\n- If the user rejects an improvement, do not repeat it unless new evidence appears.\n- If an AANA checker is unavailable or untrusted, use manual review.\n\n## Output Format\n\nWhen reporting improvement work, keep it short:\n\n```text\nWhat I noticed: ...\nNext improvement: ...\nRisk: low / needs approval / do not apply\nEvidence: observed / inferred / uncertain\n```\n\nDo not include this report unless the user asks, the task failed, or the improvement affects future behavior.\n\nFile v1.0.0:README.md\n\n# AANA Continuous Self-Improvement Skill\n\nThis OpenClaw-style skill helps agents improve across repeated work without silently changing their authority, memory, tools, or safety boundaries.\n\n## Marketplace Slug\n\nRecommended slug:\n\n```text\naana-continuous-improvement\n```\n\n## Contents\n\n- `SKILL.md`: agent-facing instructions.\n- `manifest.json`: review metadata and safety boundaries.\n- `schemas/improvement-cycle.schema.json`: optional review-payload shape.\n- `examples/redacted-improvement-cycle.json`: safe example payload.\n\n## What It Does\n\nThe skill gives the agent a disciplined improvement loop:\n\n1. Observe the task and result.\n2. Score against explicit constraints.\n3. Diagnose the smallest useful improvement.\n4. Propose a future improvement.\n5. Gate the improvement against scope, memory, files, tools, and policy boundaries.\n6. Apply only low-risk current-task improvements.\n7. Ask before persisting or reusing improvements later.\n\n## What It Does Not Do\n\nThis package does not:\n\n- install dependencies,\n- execute code,\n- call remote services,\n- write files,\n- persist memory,\n- change agent instructions,\n- alter tool permissions,\n- create automations.\n\n## Safety Model\n\nSelf-improvement is useful only when it stays accountable. The skill requires explicit user approval before improvements affect future behavior, stored memory, files, tools, policies, or permissions.\n\nUse redacted summaries for review payloads. Do not include secrets, tokens, passwords, full payment numbers, unnecessary private records, or unrelated user messages.\n\nFile v1.0.0:_meta.json\n\n{\n  \"ownerId\": \"kn7cgkmd5zmvhysnw2553a3gk185za2x\",\n  \"slug\": \"aana-continuous-improvement\",\n  \"version\": \"1.0.0\",\n  \"publishedAt\": 1777753074068\n}\n\nFile v1.0.0:skill-card.md\n\n## Description:\n\nAANA-grounded continuous self-improvement instructions for OpenClaw-style agents, with explicit approval, memory, and scope boundaries.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[mindbomber](https://clawhub.ai/user/mindbomber)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nDevelopers and agent builders use this instruction-only skill to help agents review outcomes, identify small workflow improvements, and gate any future-facing changes through explicit user approval.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Self-improvement suggestions could be applied beyond the current task or alter memory, files, tools, policies, permissions, or user expectations without approval.\n\nMitigation: Keep improvements inside the current task unless the user explicitly approves future behavior, persistence, files, tools, policy, permission, or scope changes.\n\nRisk: Review payloads could expose secrets or unnecessary private content.\n\nMitigation: Use minimal redacted summaries and exclude access tokens, passwords, full payment data, unnecessary private records, and unrelated user messages.\n\nRisk: Weak evidence could lead to misleading claims that an agent improved.\n\nMitigation: Separate observed outcomes from guesses, label uncertain improvements as hypotheses, and verify the next output against the original user request.\n\n## Reference(s):\n\n- [ClawHub Skill Page](https://clawhub.ai/mindbomber/skills/aana-continuous-improvement)\n- [README](artifact/README.md)\n- [Improvement Cycle Schema](artifact/schemas/improvement-cycle.schema.json)\n- [Redacted Improvement Cycle Example](artifact/examples/redacted-improvement-cycle.json)\n\n## Skill Output:\n\n**Output Type(s):** [guidance, markdown, configuration]\n\n**Output Format:** [Markdown or short text reports with optional JSON review payloads]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Instruction-only; optional review payloads should use redacted summaries and follow the bundled JSON schema.]\n\n## Skill Version(s):\n\n1.0.0 (source: server release metadata; artifact manifest reports 0.1.0)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.\n\nFile v1.0.0:examples/redacted-improvement-cycle.json\n\n{\n  \"task_summary\": \"Drafted a source-grounded answer for a user question.\",\n  \"outcome_summary\": \"The answer was useful but should have labeled uncertainty sooner.\",\n  \"constraints_checked\": [\n    \"Use only provided evidence.\",\n    \"Do not overstate confidence.\",\n    \"Keep answer concise.\"\n  ],\n  \"misses_or_uncertainties\": [\n    \"Uncertainty label appeared after the main claim.\"\n  ],\n  \"candidate_improvement\": \"For future source-grounded answers, place uncertainty and source limits before the main takeaway when evidence is incomplete.\",\n  \"evidence_summary\": [\n    \"The task had incomplete evidence.\",\n    \"The answer included an uncertainty caveat, but late.\"\n  ],\n  \"risk_level\": \"low\",\n  \"requires_user_approval\": false,\n  \"allowed_scope\": \"current_task_only\",\n  \"forbidden_changes\": [\n    \"system_instructions\",\n    \"tool_permissions\",\n    \"long_term_memory\",\n    \"files\",\n    \"automations\",\n    \"security_policy\",\n    \"privacy_policy\",\n    \"task_scope\"\n  ]\n}\n\nFile v1.0.0:manifest.json\n\n{\n  \"name\": \"aana-continuous-improvement\",\n  \"slug\": \"aana-continuous-improvement\",\n  \"version\": \"0.1.0\",\n  \"type\": \"instruction_only_skill\",\n  \"description\": \"AANA-grounded continuous self-improvement instructions for OpenClaw-style agents, with explicit approval, memory, and scope boundaries.\",\n  \"entrypoint\": \"SKILL.md\",\n  \"bundled_code\": false,\n  \"installs_dependencies\": false,\n  \"executes_commands\": false,\n  \"writes_files\": false,\n  \"writes_event_files\": false,\n  \"persists_memory\": false,\n  \"requires_user_approval_for_persistence\": true,\n  \"requires_user_approval_for_policy_changes\": true,\n  \"requires_user_approval_for_tool_changes\": true,\n  \"data_handling\": {\n    \"prefer_redacted_summaries\": true,\n    \"store_payloads_by_default\": false,\n    \"requires_redaction\": true,\n    \"forbidden_payload_content\": [\n      \"API keys\",\n      \"bearer tokens\",\n      \"passwords\",\n      \"full payment numbers\",\n      \"unnecessary private records\",\n      \"unrelated user messages\"\n    ]\n  },\n  \"self_improvement_boundary\": {\n    \"may_reflect_on_current_task\": true,\n    \"may_propose_future_improvements\": true,\n    \"may_not_modify_system_instructions\": true,\n    \"may_not_change_tools_without_approval\": true,\n    \"may_not_save_memory_without_approval\": true,\n    \"may_not_expand_task_scope\": true,\n    \"must_label_uncertainty\": true\n  },\n  \"aana_boundary\": {\n    \"checker_bundled\": false,\n    \"checker_required\": false,\n    \"allowed_checker_interfaces\": [\n      \"approved host tool configured by the user or administrator\",\n      \"approved in-memory API connector configured by the user or administrator\",\n      \"manual review\"\n    ]\n  },\n  \"schemas\": [\n    \"schemas/improvement-cycle.schema.json\"\n  ],\n  \"examples\": [\n    \"examples/redacted-improvement-cycle.json\"\n  ]\n}\n\nFile v1.0.0:schemas/improvement-cycle.schema.json\n\n{\n  \"$schema\": \"https://json-schema.org/draft/2020-12/schema\",\n  \"title\": \"AANA Continuous Improvement Cycle\",\n  \"type\": \"object\",\n  \"required\": [\n    \"task_summary\",\n    \"candidate_improvement\",\n    \"risk_level\",\n    \"requires_user_approval\"\n  ],\n  \"properties\": {\n    \"task_summary\": {\n      \"type\": \"string\",\n      \"minLength\": 1\n    },\n    \"outcome_summary\": {\n      \"type\": \"string\"\n    },\n    \"constraints_checked\": {\n      \"type\": \"array\",\n      \"items\": {\n        \"type\": \"string\"\n      }\n    },\n    \"misses_or_uncertainties\": {\n      \"type\": \"array\",\n      \"items\": {\n        \"type\": \"string\"\n      }\n    },\n    \"candidate_improvement\": {\n      \"type\": \"string\",\n      \"minLength\": 1\n    },\n    \"evidence_summary\": {\n      \"type\": \"array\",\n      \"items\": {\n        \"type\": \"string\"\n      }\n    },\n    \"risk_level\": {\n      \"type\": \"string\",\n      \"enum\": [\n        \"low\",\n        \"needs_approval\",\n        \"do_not_apply\"\n      ]\n    },\n    \"requires_user_approval\": {\n      \"type\": \"boolean\"\n    },\n    \"allowed_scope\": {\n      \"type\": \"string\",\n      \"enum\": [\n        \"current_task_only\",\n        \"future_tasks_after_approval\",\n        \"manual_review_only\"\n      ]\n    },\n    \"forbidden_changes\": {\n      \"type\": \"array\",\n      \"items\": {\n        \"type\": \"string\",\n        \"enum\": [\n          \"system_instructions\",\n          \"tool_permissions\",\n          \"long_term_memory\",\n          \"files\",\n          \"automations\",\n          \"security_policy\",\n          \"privacy_policy\",\n          \"task_scope\"\n        ]\n      }\n    }\n  },\n  \"additionalProperties\": false\n}","readmeExcerpt":"Skill: AANA Continuous Self-Improvement Skill Owner: mindbomber Summary: Enable continuous workflow improvement by observing outcomes, identifying issues, proposing low-risk changes, and verifying them without altering agent autho... Tags: latest:1.0.0 Version history: v1.0.0 | 2026-05-02T20:17:54.068Z | user Initial release of the AANA Continuous Self-Improvement Skill. - Introduces a structured self-improvement loo","codeSnippets":[],"executableExamples":[{"language":"text","snippet":"What I noticed: ...\nNext improvement: ...\nRisk: low / needs approval / do not apply\nEvidence: observed / inferred / uncertain"},{"language":"text","snippet":"aana-continuous-improvement"}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"# AANA Continuous Self-Improvement Skill\n\nUse this skill when the user wants an OpenClaw-style agent to improve its work over time without drifting away from the user's goals, constraints, or safety boundaries.\n\nThis is an instruction-only skill. It does not install packages, run commands, write files, modify agent instructions, persist memory, or call external services on its own.\n\n## Core Principle\n\nImprove the workflow, not the agent's authority.\n\nThe agent may observe outcomes, identify mistakes, propose better habits, and ask for approval to update a checklist or workflow. It must not silently change its own instructions, tools, permissions, memory, policies, or operating boundaries.\n\n## Improvement Loop\n\nFor each meaningful task, use this loop:\n\n1. Observe: summarize what the user asked for and what the agent produced.\n2. Score: rate the outcome against explicit constraints, evidence, completeness, usefulness, and user preference.\n3. Diagnose: identify the smallest actionable cause of any miss.\n4. Propose: suggest one concrete improvement for the next similar task.\n5. Gate: check whether the improvement changes scope, policy, permissions, memory, files, tools, or user expectations.\n6. Apply: only apply low-risk improvements inside the current task. Ask before storing or reusing any improvement later.\n7. Verify: compare the next output against the improvement and the original user request.\n\n## AANA Constraint Map\n\nUse AANA-style constraints to keep self-improvement grounded:\n\n- Physical / factual: do not invent evidence, results, tests, dates, files, capabilities, or user preferences.\n- Human impact: do not optimize for user approval by hiding uncertainty, avoiding hard truths, or escalating scope.\n- Constructed / task: preserve the user's current request, repo rules, approval boundaries, and tool permissions.\n- Feedback integrity: separate measured outcomes from guesses, and label uncertainty.\n\n## Allowed Improvements\n\nThe agent may propose or use:\n\n- a better checklist for the current task,\n- a clearer question to ask next time,\n- a more reliable verification step,\n- a safer order of operations,\n- a note about a repeated user preference inside the current conversation,\n- a small wording improvement that makes future outputs easier to review.\n\n## Restricted Improvements\n\nThe agent must ask before:\n\n- saving any long-term memory,\n- editing files,\n- changing project documentation,\n- creating or changing tools,\n- changing prompts, system behavior, or policy rules,\n- adding automation,\n- collecting analytics,\n- changing security, privacy, or approval boundaries,\n- applying an improvement outside the current user request.\n\nThe agent must not:\n\n- hide failed checks,\n- claim improvement without evidence,\n- optimize for engagement, flattery, or user dependence,\n- bypass user approvals,\n- expand the task because an improvement seems useful,\n- keep private information for future use unless the user explicitly asks.\n\n## Review Payload\n\nWhen using a co"},{"path":"README.md","content":"# AANA Continuous Self-Improvement Skill\n\nThis OpenClaw-style skill helps agents improve across repeated work without silently changing their authority, memory, tools, or safety boundaries.\n\n## Marketplace Slug\n\nRecommended slug:\n\n```text\naana-continuous-improvement\n```\n\n## Contents\n\n- `SKILL.md`: agent-facing instructions.\n- `manifest.json`: review metadata and safety boundaries.\n- `schemas/improvement-cycle.schema.json`: optional review-payload shape.\n- `examples/redacted-improvement-cycle.json`: safe example payload.\n\n## What It Does\n\nThe skill gives the agent a disciplined improvement loop:\n\n1. Observe the task and result.\n2. Score against explicit constraints.\n3. Diagnose the smallest useful improvement.\n4. Propose a future improvement.\n5. Gate the improvement against scope, memory, files, tools, and policy boundaries.\n6. Apply only low-risk current-task improvements.\n7. Ask before persisting or reusing improvements later.\n\n## What It Does Not Do\n\nThis package does not:\n\n- install dependencies,\n- execute code,\n- call remote services,\n- write files,\n- persist memory,\n- change agent instructions,\n- alter tool permissions,\n- create automations.\n\n## Safety Model\n\nSelf-improvement is useful only when it stays accountable. The skill requires explicit user approval before improvements affect future behavior, stored memory, files, tools, policies, or permissions.\n\nUse redacted summaries for review payloads. Do not include secrets, tokens, passwords, full payment numbers, unnecessary private records, or unrelated user messages."},{"path":"_meta.json","content":"{\n  \"ownerId\": \"kn7cgkmd5zmvhysnw2553a3gk185za2x\",\n  \"slug\": \"aana-continuous-improvement\",\n  \"version\": \"1.0.0\",\n  \"publishedAt\": 1777753074068\n}"},{"path":"skill-card.md","content":"## Description:\n\nAANA-grounded continuous self-improvement instructions for OpenClaw-style agents, with explicit approval, memory, and scope boundaries.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[mindbomber](https://clawhub.ai/user/mindbomber)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nDevelopers and agent builders use this instruction-only skill to help agents review outcomes, identify small workflow improvements, and gate any future-facing changes through explicit user approval.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Self-improvement suggestions could be applied beyond the current task or alter memory, files, tools, policies, permissions, or user expectations without approval.\n\nMitigation: Keep improvements inside the current task unless the user explicitly approves future behavior, persistence, files, tools, policy, permission, or scope changes.\n\nRisk: Review payloads could expose secrets or unnecessary private content.\n\nMitigation: Use minimal redacted summaries and exclude access tokens, passwords, full payment data, unnecessary private records, and unrelated user messages.\n\nRisk: Weak evidence could lead to misleading claims that an agent improved.\n\nMitigation: Separate observed outcomes from guesses, label uncertain improvements as hypotheses, and verify the next output against the original user request.\n\n## Reference(s):\n\n- [ClawHub Skill Page](https://clawhub.ai/mindbomber/skills/aana-continuous-improvement)\n- [README](artifact/README.md)\n- [Improvement Cycle Schema](artifact/schemas/improvement-cycle.schema.json)\n- [Redacted Improvement Cycle Example](artifact/examples/redacted-improvement-cycle.json)\n\n## Skill Output:\n\n**Output Type(s):** [guidance, markdown, configuration]\n\n**Output Format:** [Markdown or short text reports with optional JSON review payloads]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Instruction-only; optional review payloads should use redacted summaries and follow the bundled JSON schema.]\n\n## Skill Version(s):\n\n1.0.0 (source: server release metadata; artifact manifest reports 0.1.0)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment."},{"path":"examples/redacted-improvement-cycle.json","content":"{\n  \"task_summary\": \"Drafted a source-grounded answer for a user question.\",\n  \"outcome_summary\": \"The answer was useful but should have labeled uncertainty sooner.\",\n  \"constraints_checked\": [\n    \"Use only provided evidence.\",\n    \"Do not overstate confidence.\",\n    \"Keep answer concise.\"\n  ],\n  \"misses_or_uncertainties\": [\n    \"Uncertainty label appeared after the main claim.\"\n  ],\n  \"candidate_improvement\": \"For future source-grounded answers, place uncertainty and source limits before the main takeaway when evidence is incomplete.\",\n  \"evidence_summary\": [\n    \"The task had incomplete evidence.\",\n    \"The answer included an uncertainty caveat, but late.\"\n  ],\n  \"risk_level\": \"low\",\n  \"requires_user_approval\": false,\n  \"allowed_scope\": \"current_task_only\",\n  \"forbidden_changes\": [\n    \"system_instructions\",\n    \"tool_permissions\",\n    \"long_term_memory\",\n    \"files\",\n    \"automations\",\n    \"security_policy\",\n    \"privacy_policy\",\n    \"task_scope\"\n  ]\n}"}],"languages":[],"docsSourceLabel":"CLAWHUB","editorialOverview":"Enable continuous workflow improvement by observing outcomes, identifying issues, proposing low-risk changes, and verifying them without altering agent autho... Skill: AANA Continuous Self-Improvement Skill Owner: mindbomber Summary: Enable continuous workflow improvement by observing outcomes, identifying issues, proposing low-risk changes, and verifying them without altering agent autho... 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