What makes AANA Continuous Self-Improvement Skill notable?

1K downloads. Trust evidence available

How should AANA Continuous Self-Improvement Skill be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

agentCLAWHUBUnverified

AANA Continuous Self-Improvement Skill

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

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.0k

Updated

Oct 11, 2026

Version

1.0.0

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1K downloads reported by the source. Last updated 10/11/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 1K downloadsadoption · observed Oct 11, 2026
Latest release: 1.0.0release · observed May 2, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s177cynx168hg36ac5nrkb9wdn85z9dq:aana-continuous-improvement

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-mindbomber-aana-continuous-improvement/snapshot"

Run-check

$0.02 USD

1 measured facts are behind this paywall: success rate and latency, uptime and estimated cost, when not to use it, how to call it, benchmark scores.

Agents pay $0.02 in USDC. A card payment is $0.50, the smallest a card allows.

Documentation

CLAWHUB

13,976 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

# 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 co

README.md

# 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.

_meta.json

{
  "ownerId": "kn7cgkmd5zmvhysnw2553a3gk185za2x",
  "slug": "aana-continuous-improvement",
  "version": "1.0.0",
  "publishedAt": 1777753074068
}

skill-card.md

## Description:

AANA-grounded continuous self-improvement instructions for OpenClaw-style agents, with explicit approval, memory, and scope boundaries.

This skill is ready for commercial/non-commercial use.

## Publisher:

[mindbomber](https://clawhub.ai/user/mindbomber)

### License/Terms of Use:

MIT-0

## Use Case:

Developers 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.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: Self-improvement suggestions could be applied beyond the current task or alter memory, files, tools, policies, permissions, or user expectations without approval.

Mitigation: Keep improvements inside the current task unless the user explicitly approves future behavior, persistence, files, tools, policy, permission, or scope changes.

Risk: Review payloads could expose secrets or unnecessary private content.

Mitigation: Use minimal redacted summaries and exclude access tokens, passwords, full payment data, unnecessary private records, and unrelated user messages.

Risk: Weak evidence could lead to misleading claims that an agent improved.

Mitigation: Separate observed outcomes from guesses, label uncertain improvements as hypotheses, and verify the next output against the original user request.

## Reference(s):

- [ClawHub Skill Page](https://clawhub.ai/mindbomber/skills/aana-continuous-improvement)
- [README](artifact/README.md)
- [Improvement Cycle Schema](artifact/schemas/improvement-cycle.schema.json)
- [Redacted Improvement Cycle Example](artifact/examples/redacted-improvement-cycle.json)

## Skill Output:

**Output Type(s):** [guidance, markdown, configuration]

**Output Format:** [Markdown or short text reports with optional JSON review payloads]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [Instruction-only; optional review payloads should use redacted summaries and follow the bundled JSON schema.]

## Skill Version(s):

1.0.0 (source: server release metadata; artifact manifest reports 0.1.0)

## Ethical Considerations:

Users 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.

examples/redacted-improvement-cycle.json

{
  "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"
  ]
}

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