Skills when-optimizing-agent-learning-use-reasoningbank-intelligence

📦

when-optimizing-agent-learning-use-reasoningbank-intelligence

Name: when-optimizing-agent-learning-use-reasoningbank-intelligence
Author: DNYoussef

v1.0.0 Content revision r1 High Risk ⚙️ External commands📁 Filesystem access

Optimize Agent Learning with ReasoningBank

Agents often repeat weak strategies because learning data stays unstructured. This skill organizes trajectories, patterns, benchmarks, and recommendations into a ReasoningBank workflow.

Supports: Claude Codex Code(CC)

⚠️ 38 Poor

Stop for confirmation before installing.

Review the plan and obtain explicit user consent before changing files.

Install with my Agent

Copy this request to your Agent. It includes the canonical Skill page and manifest.

Agent request

Review the Skillstore skill "when-optimizing-agent-learning-use-reasoningbank-intelligence" from https://skillstore.io/skills/dnyoussef-when-optimizing-agent-learning-use-reasoningbank-intelligence.md and its manifest at https://skillstore.io/api/skills/dnyoussef-when-optimizing-agent-learning-use-reasoningbank-intelligence/manifest. Verify the artifact. Stop and obtain explicit user consent before installing or changing files.

Your Agent should still show its plan and request any confirmation required by the security policy.

Agent-readable resources

Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.

Markdown detail GET /skills/dnyoussef-when-optimizing-agent-learning-use-reasoningbank-intelligence.md Signed manifest GET /api/skills/dnyoussef-when-optimizing-agent-learning-use-reasoningbank-intelligence/manifest Signed lockfile GET /api/skills/dnyoussef-when-optimizing-agent-learning-use-reasoningbank-intelligence/lockfile

Test it

Using "when-optimizing-agent-learning-use-reasoningbank-intelligence". I need my support agent to learn from repeated ticket resolutions.

Expected outcome:

A trajectory schema covering ticket context, decision steps, actions, observations, and outcome metrics.
Verdict criteria for speed, accuracy, customer satisfaction, and escalation avoidance.
A validation plan comparing baseline and optimized resolution performance.

Using "when-optimizing-agent-learning-use-reasoningbank-intelligence". I want to know which learned strategies should be applied first.

Expected outcome:

A ranked recommendation list based on frequency, average score, consistency, and applicable contexts.
A short explanation of why each strategy is safe to test.
Benchmark checks that confirm improvement before production rollout.

Using "when-optimizing-agent-learning-use-reasoningbank-intelligence". I need a deployment checklist for a trained decision model.

Expected outcome:

Export readiness checks for model weights, patterns, recommendations, and performance evidence.
Security checks for redaction, file permissions, and approved storage locations.
Monitoring steps for drift, success rate changes, and scheduled retraining.

Security Audit

High Risk

v8 • 7/5/2026 Open versioned report

Most external-command alerts are markdown fences or JavaScript template literals and were false positives. I confirmed one documented npx workflow command and the fixed /tmp export examples as medium risks. I also found a high-severity data-handling issue because trajectories and exports can contain sensitive agent reasoning without redaction.

Files scanned

883

Lines analyzed

Review items

False positives ignored

Confirmed security concerns (1)

High

SKILL.md:178-218 SKILL.md:501-521 SKILL.md:562-586

Sensitive Agent Trajectory Capture and Export

The workflow records agent thoughts, actions, observations, environment details, and learned patterns, then exports model and report artifacts without redaction guidance. These records can contain private prompts, secrets, customer data, or proprietary reasoning traces.

The cited code explicitly stores thought, action, observation, environment, patterns, and report data. The file provides no redaction, consent, retention, or access-control safeguards before export.

Capability review items (6)

These are real local capabilities that may be expected for this skill, so they require review but are not counted as confirmed malicious behavior.

Medium

SKILL.md:159-167

Ruby/shell backtick execution

```bash

This block instructs users to run npx claude-flow@alpha hooks, which executes an external npm package during workflow setup. The arguments are fixed, but package execution remains a supply-chain risk.

Medium

SKILL.md:519

Node.js fs operations

await fs.writeFile(

The sample writes a model export with fs.writeFile to a fixed /tmp path. If used directly, that can expose sensitive model data or overwrite files unexpectedly.

Medium

SKILL.md:583

Node.js fs operations

await fs.writeFile(

The sample writes a learning report with fs.writeFile to a fixed temporary path. The report may contain trajectory and performance details that should not be placed in shared temp storage.

Medium

SKILL.md:520

Temp directory access

'/tmp/reasoningbank-export.json',

The fixed /tmp/reasoningbank-export.json path is a predictable export destination. This can leak data to shared temp storage or collide with existing files.

Medium

SKILL.md:524

Temp directory access

console.log('✅ Model exported to: /tmp/reasoningbank-export.json');

The log message documents the same fixed /tmp model export path used by the preceding write operation. It reinforces a shared temporary storage pattern for sensitive output.

Medium

SKILL.md:584

Temp directory access

'/tmp/learning-report.json',

The fixed /tmp/learning-report.json path is predictable and may expose learning reports on shared systems. A controlled output directory with restricted permissions is safer.

Risk Factors

⚙️ External commands (43)

📁 Filesystem access (5)

SKILL.md:519 SKILL.md:583 SKILL.md:520 SKILL.md:524 SKILL.md:584

Audited by: codex View Audit History →

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Open versioned report

Copy report link

https://skillstore.io/skills/dnyoussef-when-optimizing-agent-learning-use-reasoningbank-intelligence/audits/8?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_report

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Academic citations (APA · BibTeX · CFF)

APA citation

DNYoussef. (2026). when-optimizing-agent-learning-use-reasoningbank-intelligence security audit report (audit version 8) [Author version 1.0.0]. Skillstore. https://skillstore.io/skills/dnyoussef-when-optimizing-agent-learning-use-reasoningbank-intelligence/audits/8

BibTeX citation

@techreport{dnyoussef-dnyoussef-when-optimizing-agent-learning-use-reasoningbank-intelligence-2026,
  author = {DNYoussef},
  title = {when-optimizing-agent-learning-use-reasoningbank-intelligence security audit report (audit version 8)},
  institution = {Skillstore},
  year = {2026},
  number = {8},
  url = {https://skillstore.io/skills/dnyoussef-when-optimizing-agent-learning-use-reasoningbank-intelligence/audits/8},
  note = {Author version 1.0.0}
}

CITATION.cff

cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "when-optimizing-agent-learning-use-reasoningbank-intelligence security audit report (audit version 8)"
version: "1.0.0"
type: report
authors:
  - name: "DNYoussef"
date-released: "2026-07-05"
url: "https://skillstore.io/skills/dnyoussef-when-optimizing-agent-learning-use-reasoningbank-intelligence/audits/8"
identifiers:
  - type: other
    value: "skillstore:dnyoussef-when-optimizing-agent-learning-use-reasoningbank-intelligence:audit:8"
    description: "Skillstore immutable audit report identifier"

Skillstore Score

Why this score Evidence Confidence: Medium

Architecture

100

Maintainability

Content

Community

Spec Compliance

What You Can Build

Improve Agent Strategy Selection

Capture successful and failed trajectories, then turn repeated patterns into prioritized strategy recommendations.

Benchmark Learning Gains

Compare baseline and optimized agents with shared test cases and clear performance metrics.

Prepare Learning Artifacts for Deployment

Package models, pattern libraries, integration notes, and learning reports for controlled production rollout.

Try These Prompts

Plan a Learning Setup

Use this skill to outline a ReasoningBank setup for my agent. Include required storage, trajectory fields, verdict criteria, and validation checks.

Capture Useful Trajectories

Use this skill to design a trajectory capture plan for my agent. Include success signals, failure labels, privacy safeguards, and pattern extraction steps.

Optimize Existing Agents

Use this skill to analyze my current agent workflow and propose ReasoningBank optimizations, benchmark tests, and recommendation ranking criteria.

Deploy a Learning System

Use this skill to create a production rollout plan for learned strategies. Include export controls, monitoring, retraining cadence, and rollback criteria.

Best Practices

Collect both successful and failed trajectories before training recommendations.
Define measurable verdict criteria before comparing baseline and optimized agents.
Redact private data before storing trajectories, reports, or exported learning artifacts.

Avoid

Do not train from only successful examples because recommendations will be biased.
Do not deploy learned strategies before benchmark results show stable improvement.
Do not store reasoning traces or reports in shared locations without access controls.

Frequently Asked Questions

What does this skill help build?

It helps build a workflow for adaptive agent learning with trajectory capture, pattern extraction, model training, and validation.

Does it include a complete runnable application?

No. It provides implementation guidance and examples that require real project code and dependencies.

Can it work with Claude, Codex, and Claude Code?

Yes. The workflow is tool-agnostic and the marketplace metadata lists Claude, Codex, and Claude Code support.

What data should be collected?

Collect task context, reasoning steps, actions, observations, outcomes, and metrics that are safe to retain.

How should teams validate improvement?

Run baseline and optimized agents on shared test cases, then compare score, success rate, efficiency, and quality.

What security precautions are important?

Pin external packages, avoid fixed temporary paths, redact sensitive traces, and restrict access to exported artifacts.

Developer Details

Author

DNYoussef

License

MIT

Author version

v1.0.0

Skillstore revision

Repository

https://github.com/DNYoussef/ai-chrome-extension/tree/main/.claude/skills/utilities/when-optimizing-agent-learning-use-reasoningbank-intelligence

Ref

c579efd02184f1128d6741d16e6c8d951d7d4208

Maintenance freshness

7/18/2026

Usage

4 downloads · 178 views

File structure

📄 process-diagram.gv

📄 PROCESS.md

📄 README.md

📄 SKILL.md