Skill: mindbomber/aana-continuous-improvement (version 1.0.0)

# AANA Continuous Self-Improvement Skill Use 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. This 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. ## Core Principle Improve the workflow, not the agent's authority. The 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. ## Improvement Loop For each meaningful task, use this loop: 1. Observe: summarize what the user asked for and what the agent produced. 2. Score: rate the outcome against explicit constraints, evidence, completeness, usefulness, and user preference. 3. Diagnose: identify the smallest actionable cause of any miss. 4. Propose: suggest one concrete improvement for the next similar task. 5. Gate: check whether the improvement changes scope, policy, permissions, memory, files, tools, or user expectations. 6. Apply: only apply low-risk improvements inside the current task. Ask before storing or reusing any improvement later. 7. Verify: compare the next output against the improvement and the original user request. ## AANA Constraint Map Use AANA-style constraints to keep self-improvement grounded: - Physical / factual: do not invent evidence, results, tests, dates, files, capabilities, or user preferences. - Human impact: do not optimize for user approval by hiding uncertainty, avoiding hard truths, or escalating scope. - Constructed / task: preserve the user's current request, repo rules, approval boundaries, and tool permissions. - Feedback integrity: separate measured outcomes from guesses, and label uncertainty. ## Allowed Improvements The agent may propose or use: - a better checklist for the current task, - a clearer question to ask next time, - a more reliable verification step, - a safer order of operations, - a note about a repeated user preference inside the current conversation, - a small wording improvement that makes future outputs easier to review. ## Restricted Improvements The agent must ask before: - saving any long-term memory, - editing files, - changing project documentation, - creating or changing tools, - changing prompts, system behavior, or policy rules, - adding automation, - collecting analytics, - changing security, privacy, or approval boundaries, - applying an improvement outside the current user request. The agent must not: - hide failed checks, - claim improvement without evidence, - optimize for engagement, flattery, or user dependence, - bypass user approvals, - expand the task because an improvement seems useful, - keep private information for future use unless the user explicitly asks. ## Review Payload When using a configured AANA checker, send only a minimal redacted review payload. Prefer summaries over raw private content: - `task_summary` - `candidate_improvement` - `evidence_summary` - `risk_level` - `requires_user_approval` - `allowed_scope` Do not include secrets, access tokens, full payment data, unnecessary private records, or unrelated user messages. ## Decision Rule - If the improvement is low-risk and stays inside the current task, use it now. - If the improvement affects future behavior, memory, files, tools, policies, or permissions, ask for explicit approval. - If the improvement is based on weak evidence, label it as a hypothesis. - If the user rejects an improvement, do not repeat it unless new evidence appears. - If an AANA checker is unavailable or untrusted, use manual review. ## Output Format When reporting improvement work, keep it short: ```text What I noticed: ... Next improvement: ... Risk: low / needs approval / do not apply Evidence: observed / inferred / uncertain ``` Do not include this report unless the user asks, the task failed, or the improvement affects future behavior.

path: manifest.json
content: {
  "name": "aana-continuous-improvement",
  "slug": "aana-continuous-improvement",
  "version": "0.1.0",
  "type": "instruction_only_skill",
  "description": "AANA-grounded continuous self-improvement instructions for OpenClaw-style agents, with explicit approval, memory, and scope boundaries.",
  "entrypoint": "SKILL.md",
  "bundled_code": false,
  "installs_dependencies": false,
  "executes_commands": false,
  "writes_files": false,
  "writes_event_files": false,
  "persists_memory": false,
  "requires_user_approval_for_persistence": true,
  "requires_user_approval_for_policy_changes": true,
  "requires_user_approval_for_tool_changes": true,
  "data_handling": {
    "prefer_redacted_summaries": true,
    "store_payloads_by_default": false,
    "requires_redaction": true,
    "forbidden_payload_content": [
      "API keys",
      "bearer tokens",
      "passwords",
      "full payment numbers",
      "unnecessary private records",
      "unrelated user messages"
    ]
  },
  "self_improvement_boundary": {
    "may_reflect_on_current_task": true,
    "may_propose_future_improvements": true,
    "may_not_modify_system_instructions": true,
    "may_not_change_tools_without_approval": true,
    "may_not_save_memory_without_approval": true,
    "may_not_expand_task_scope": true,
    "must_label_uncertainty": true
  },
  "aana_boundary": {
    "checker_bundled": false,
    "checker_required": false,
    "allowed_checker_interfaces": [
      "approved host tool configured by the user or administrator",
      "approved in-memory API connector configured by the user or administrator",
      "manual review"
    ]
  },
  "schemas": [
    "schemas/improvement-cycle.schema.json"
  ],
  "examples": [
    "examples/redacted-improvement-cycle.json"
  ]
}


path: README.md
content: # AANA Continuous Self-Improvement Skill

This OpenClaw-style skill helps agents improve across repeated work without silently changing their authority, memory, tools, or safety boundaries.

## Marketplace Slug

Recommended slug:

```text
aana-continuous-improvement
```

## Contents

- `SKILL.md`: agent-facing instructions.
- `manifest.json`: review metadata and safety boundaries.
- `schemas/improvement-cycle.schema.json`: optional review-payload shape.
- `examples/redacted-improvement-cycle.json`: safe example payload.

## What It Does

The skill gives the agent a disciplined improvement loop:

1. Observe the task and result.
2. Score against explicit constraints.
3. Diagnose the smallest useful improvement.
4. Propose a future improvement.
5. Gate the improvement against scope, memory, files, tools, and policy boundaries.
6. Apply only low-risk current-task improvements.
7. Ask before persisting or reusing improvements later.

## What It Does Not Do

This package does not:

- install dependencies,
- execute code,
- call remote services,
- write files,
- persist memory,
- change agent instructions,
- alter tool permissions,
- create automations.

## Safety Model

Self-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.

Use redacted summaries for review payloads. Do not include secrets, tokens, passwords, full payment numbers, unnecessary private records, or unrelated user messages.


path: schemas/improvement-cycle.schema.json
content: {
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "title": "AANA Continuous Improvement Cycle",
  "type": "object",
  "required": [
    "task_summary",
    "candidate_improvement",
    "risk_level",
    "requires_user_approval"
  ],
  "properties": {
    "task_summary": {
      "type": "string",
      "minLength": 1
    },
    "outcome_summary": {
      "type": "string"
    },
    "constraints_checked": {
      "type": "array",
      "items": {
        "type": "string"
      }
    },
    "misses_or_uncertainties": {
      "type": "array",
      "items": {
        "type": "string"
      }
    },
    "candidate_improvement": {
      "type": "string",
      "minLength": 1
    },
    "evidence_summary": {
      "type": "array",
      "items": {
        "type": "string"
      }
    },
    "risk_level": {
      "type": "string",
      "enum": [
        "low",
        "needs_approval",
        "do_not_apply"
      ]
    },
    "requires_user_approval": {
      "type": "boolean"
    },
    "allowed_scope": {
      "type": "string",
      "enum": [
        "current_task_only",
        "future_tasks_after_approval",
        "manual_review_only"
      ]
    },
    "forbidden_changes": {
      "type": "array",
      "items": {
        "type": "string",
        "enum": [
          "system_instructions",
          "tool_permissions",
          "long_term_memory",
          "files",
          "automations",
          "security_policy",
          "privacy_policy",
          "task_scope"
        ]
      }
    }
  },
  "additionalProperties": false
}


path: examples/redacted-improvement-cycle.json
content: {
  "task_summary": "Drafted a source-grounded answer for a user question.",
  "outcome_summary": "The answer was useful but should have labeled uncertainty sooner.",
  "constraints_checked": [
    "Use only provided evidence.",
    "Do not overstate confidence.",
    "Keep answer concise."
  ],
  "misses_or_uncertainties": [
    "Uncertainty label appeared after the main claim."
  ],
  "candidate_improvement": "For future source-grounded answers, place uncertainty and source limits before the main takeaway when evidence is incomplete.",
  "evidence_summary": [
    "The task had incomplete evidence.",
    "The answer included an uncertainty caveat, but late."
  ],
  "risk_level": "low",
  "requires_user_approval": false,
  "allowed_scope": "current_task_only",
  "forbidden_changes": [
    "system_instructions",
    "tool_permissions",
    "long_term_memory",
    "files",
    "automations",
    "security_policy",
    "privacy_policy",
    "task_scope"
  ]
}


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)