Monitor CI
nrwl/nx
Monitor Nx Cloud CI pipeline and handle self-healing fixes. An agent skill from nrwl/nx.
Documentation-first skill and workflow toolkit for intent-based security.
$ npx skills add LeoYeAI/openclaw-master-skills --skill self-improving-intent-security-agent -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills self-improving-intent-security-agent --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/self-improving-intent-security-agent .claude/skills/self-improving-intent-security-agent && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "self-improving-intent-security-agent" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/self-improving-intent-security-agent into .claude/skills/self-improving-intent-security-agent/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "self-improving-intent-security-agent", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/self-improving-intent-security-agentType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add LeoYeAI/openclaw-master-skills --skill self-improving-intent-security-agent -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills self-improving-intent-security-agent --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/self-improving-intent-security-agent .agents/skills/self-improving-intent-security-agent && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "self-improving-intent-security-agent" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/self-improving-intent-security-agent into .agents/skills/self-improving-intent-security-agent/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "self-improving-intent-security-agent", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add LeoYeAI/openclaw-master-skills --skill self-improving-intent-security-agent -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills self-improving-intent-security-agent --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/self-improving-intent-security-agent .cursor/skills/self-improving-intent-security-agent && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "self-improving-intent-security-agent" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/self-improving-intent-security-agent into .cursor/skills/self-improving-intent-security-agent/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "self-improving-intent-security-agent", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/LeoYeAI/openclaw-master-skills.git --path skills/self-improving-intent-security-agent--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add LeoYeAI/openclaw-master-skills --skill self-improving-intent-security-agent -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills self-improving-intent-security-agent --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/self-improving-intent-security-agent .gemini/skills/self-improving-intent-security-agent && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "self-improving-intent-security-agent" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/self-improving-intent-security-agent into .gemini/skills/self-improving-intent-security-agent/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "self-improving-intent-security-agent", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install LeoYeAI/openclaw-master-skills self-improving-intent-security-agentInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add LeoYeAI/openclaw-master-skills --skill self-improving-intent-security-agent -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/self-improving-intent-security-agent .github/skills/self-improving-intent-security-agent && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "self-improving-intent-security-agent" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/self-improving-intent-security-agent into .github/skills/self-improving-intent-security-agent/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "self-improving-intent-security-agent", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add LeoYeAI/openclaw-master-skills --skill self-improving-intent-security-agent -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills self-improving-intent-security-agent --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/self-improving-intent-security-agent .opencode/skills/self-improving-intent-security-agent && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "self-improving-intent-security-agent" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/self-improving-intent-security-agent into .opencode/skills/self-improving-intent-security-agent/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "self-improving-intent-security-agent", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
self-improving-intent-security-agentDocumentation-first skill and workflow toolkit for intent-based security.
Self Improving Intent Security Agent is an agent skill from LeoYeAI/openclaw-master-skills. Documentation-first skill and workflow toolkit for intent-based security. Provides templates, examples, and local helper scripts for capturing intent, reviewing actions, documenting rollbacks, and recording learnings. Use when: (1) designing or prototyping intent validation workflows, (2) documenting high-risk operations, (3) creating audit trails and rollback records, (4) building your own runtime enforcement layer.
Its SKILL.md is about 4.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 43 other files, including scripts, reference files and assets (for example `CLAUDE.md`, `DEVELOPMENT_CONTEXT.md` and `PUBLISHING.md`).
It sits in DevOps & Cloud, covering Prototyping. The repository describes itself as: 🧠 Curated collection of 1209+ best OpenClaw skills — weekly updated by MyClaw.ai. The licence is MIT.
9 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit e5199b5. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Ships 1 file in scripts/, which the agent can run.
Shell commands in SKILL.md call:
npxFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md. Its commands use npx, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Self Improving Intent Security Agent loads about 4.8k tokens when it runs, and up to ~22k if it reads all its reference files. Until then it costs about 114 tokens; SKILL.md has 1,222 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
The full file from LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 1,222 words, ~4,752 tokens.
.claude/skills/self-improving-intent-security-agent/SKILL.md (or your agent's skills folder). This skill also uses 39 other files; get the full folder from GitHub.npx skills add nishantapatil3/self-improving-intent-security-agentUse this skill to structure and document intent validation workflows. It does not ship a production runtime engine that automatically intercepts agent actions; instead, it provides templates, examples, and local scripts that help you build, simulate, or document that workflow.
| Situation | Action |
|---|---|
| Starting autonomous task | Capture intent specification (goal, constraints, expected behavior) |
| Before each action | Validate against intent, check authorization |
| Action violates intent | Document the violation and follow the rollback workflow |
| Unusual behavior detected | Log an anomaly, assess severity, and decide whether to halt or roll back |
| Task completes | Analyze outcome, extract patterns, update strategies |
| High-risk operation | Require human approval before execution |
| Need transparency | Review audit log with full action history |
| Strategy improves | A/B test new approach, adopt if better |
| Recurring violation | Promote to permanent constraint in CLAUDE.md |
Create .agent/ directory in project root:
mkdir -p .agent/{intents,violations,learnings,audit}Copy templates from assets/ or create files with headers. Review the included shell scripts before running them if you want to understand exactly what they do.
For a complete conversation-driven working folder, scaffold a run pack:
./scripts/scaffold-run.sh examples/my-demo customer_feedback mediumThis creates:
conversation.md for the user/agent transcriptreport.md for the final summary.agent/ tree with intent, audit, violation, rollback, learning, and strategy filesBefore executing autonomous tasks, capture structured intent:
## [INT-YYYYMMDD-XXX] task_name
**Created**: ISO-8601 timestamp
**Risk Level**: low | medium | high
**Status**: active | completed | violated
### Goal
What you want to achieve (single clear objective)
### Constraints
- Boundary 1 (e.g., "Only modify files in ./src")
- Boundary 2 (e.g., "Do not make network calls")
- Boundary 3 (e.g., "Preserve existing test coverage")
### Expected Behavior
- Pattern 1 (e.g., "Read files before modifying")
- Pattern 2 (e.g., "Run tests after changes")
- Pattern 3 (e.g., "Create backups of modified files")
### Context
- Relevant files: path/to/file.ext
- Environment: development | staging | production
- Previous attempts: INT-20250115-001 (if retry)
---Save to .agent/intents/INT-YYYYMMDD-XXX.md.
Use this when you want the skill to document not just the intent, but the full user and agent interaction over time.
conversation.md.agent/intents/.agent/audit/.agent/violations/ANOMALIES.md.agent/violations/.agent/audit/ROLLBACKS.md.agent/learnings/.agent/learnings/STRATEGIES.mdreport.mdSee examples/customer-feedback-demo/ for a full run showing:
Before each action, validate:
If ANY check fails → block action, log violation.
Intent: "Process customer feedback files"
Constraints: ["Only read ./feedback", "No file modifications"]
Action: "delete ./feedback/temp.txt"
Validation:
- Goal Alignment: ❌ Deleting isn't "processing"
- Constraint Check: ❌ Violates "no modifications"
- Behavior Match: ❌ Not expected for this task
- Authorization: ✓ (but blocked by other checks)
Result: BLOCKED → Log violation → Consider rollbackWhen validation fails, log to .agent/violations/:
## [VIO-YYYYMMDD-XXX] violation_type
**Logged**: ISO-8601 timestamp
**Severity**: low | medium | high | critical
**Intent**: INT-20250115-001
**Status**: pending_review
### What Happened
Action that was attempted
### Validation Failures
- Goal Alignment: [reason]
- Constraint Check: [which constraint violated]
- Behavior Match: [how it deviated]
### Action Taken
- [ ] Action blocked
- [ ] Checkpoint rollback
- [ ] Alert sent
- [ ] Execution halted
### Root Cause
Why the agent attempted this (if analyzable)
### Prevention
How to prevent this in the future
### Metadata
- Related Intent: INT-20250115-001
- Action Type: file_delete | api_call | command_execution
- Risk Level: high
- See Also: VIO-20250110-002 (if recurring)
---Monitor execution for behavioral anomalies:
| Type | Description | Response |
|---|---|---|
| Goal Drift | Actions diverging from stated goal | Halt, request clarification |
| Capability Misuse | Using tools inappropriately | Rollback to checkpoint |
| Side Effects | Unexpected consequences detected | Log warning, continue with monitoring |
| Resource Exceeded | CPU/memory/time limits breached | Throttle or halt |
| Pattern Deviation | Behavior differs from expected | Log for analysis |
Log to .agent/violations/ANOMALIES.md:
## [ANO-YYYYMMDD-XXX] anomaly_type
**Detected**: ISO-8601 timestamp
**Severity**: low | medium | high
**Intent**: INT-20250115-001
### Anomaly Details
What unusual behavior was detected
### Evidence
- Metric that triggered alert
- Baseline vs. actual values
- Timeline of deviation
### Assessment
Why this is anomalous
### Response Taken
- [ ] Continued with monitoring
- [ ] Applied constraints
- [ ] Rolled back
- [ ] Halted execution
---After task completion, log learnings to .agent/learnings/:
## [LRN-YYYYMMDD-XXX] category
**Logged**: ISO-8601 timestamp
**Intent**: INT-20250115-001
**Outcome**: success | failure | partial
### What Was Learned
Pattern or insight discovered
### Evidence
- Success rate: 95%
- Execution time: 2.3s
- Actions taken: 15
- Checkpoints: 3
### Strategy Impact
How this affects future executions
### Application Scope
- Tasks: file_processing, data_transformation
- Risk Levels: low, medium
- Conditions: when X and Y are true
### Safety Check
- Complexity: low | medium | high
- Performance: baseline_comparison
- Risk: assessment
### Metadata
- Category: pattern | optimization | error_handling | security
- Confidence: low | medium | high
- Sample Size: N tasks observed
- Pattern-Key: file.batch_processing (if recurring)
---Before risky operations:
const checkpoint = await agent.checkpoint.create({
intent: currentIntent,
reason: "Before bulk file operations"
});Automatic rollback when intent violated:
// Happens automatically, but can also trigger manually:
await agent.rollback.restore(checkpointId, {
reason: "Detected constraint violation",
notify: true
});Track in .agent/audit/ROLLBACKS.md:
## [RBK-YYYYMMDD-XXX] checkpoint_id
**Executed**: ISO-8601 timestamp
**Intent**: INT-20250115-001
**Trigger**: automatic | manual
### Reason
Why rollback was necessary
### Actions Reversed
- Action 1 (reversed successfully)
- Action 2 (reversed successfully)
- Action 3 (reversal failed - manual intervention needed)
### Checkpoint Restored
- Checkpoint: CHK-20250115-001
- Created: 2025-01-15T10:00:00Z
- Actions since checkpoint: 15
### Status
- [ ] Fully restored
- [ ] Partially restored (see notes)
- [ ] Manual intervention required
---When agent learns better approaches:
Track in .agent/learnings/STRATEGIES.md:
## [STR-YYYYMMDD-XXX] strategy_name
**Created**: ISO-8601 timestamp
**Domain**: file_processing | api_interaction | error_handling
**Status**: testing | adopted | rejected | superseded
### Approach
What this strategy does differently
### Performance
- Baseline: 85% success, 3.2s avg
- Candidate: 92% success, 2.1s avg
- Improvement: +7% success, -34% time
### A/B Test Results
- Test Tasks: 50
- Candidate Used: 5 tasks
- Wins: 4, Losses: 1, Ties: 0
### Safety Validation
- Complexity: within limits (complexity: 45/100)
- Permissions: no expansion
- Risk: acceptable (no high-risk changes)
### Adoption Decision
- [ ] Adopt (outperforms baseline)
- [ ] Reject (underperforms baseline)
- [ ] Extend testing (inconclusive)
---When learnings are broadly applicable, promote to project files:
| Target | What Belongs There |
|---|---|
CLAUDE.md | Intent patterns, common constraints for this project |
AGENTS.md | Agent-specific workflows, validation rules |
.github/copilot-instructions.md | Security guidelines, constraint templates |
SECURITY.md | Security-critical constraints and validation rules |
Promote when:
Violation (recurring):
VIO-20250115-001: Attempted to modify files outside ./src VIO-20250118-002: Attempted to modify files outside ./src VIO-20250120-003: Attempted to modify files outside ./src
Promote to CLAUDE.md:
## File Modification Constraints
- Only modify files within `./src` directory
- Other directories are read-only unless explicitly authorizedLearning (proven strategy):
LRN-20250115-005: Batch processing with checkpoints every 10 files Results: 95% success, 40% faster, easy rollback on failures
Promote to AGENTS.md:
## File Processing Strategy
- Use batch processing (10 files per batch)
- Create checkpoint before each batch
- Enables fast rollback on errorsImportant: All environment variables are optional. The skill works with sensible defaults without any configuration.
Security Note: This skill does NOT require any credentials or secrets. All data stays local in the .agent/ directory. No data is transmitted externally.
# Paths (optional - defaults shown)
export AGENT_INTENT_PATH=".agent/intents" # Default: .agent/intents
export AGENT_AUDIT_PATH=".agent/audit" # Default: .agent/audit
# Security Settings (optional tuning)
export AGENT_RISK_THRESHOLD="medium" # low | medium | high
export AGENT_AUTO_ROLLBACK="true" # true | false
export AGENT_ANOMALY_THRESHOLD="0.8" # 0.0 - 1.0
# Learning Settings (optional tuning)
export AGENT_LEARNING_ENABLED="true" # true | false
export AGENT_MIN_SAMPLE_SIZE="10" # Min observations before adopting
export AGENT_AB_TEST_RATIO="0.1" # 10% of tasks for A/B testing
# Monitoring (optional tuning)
export AGENT_METRICS_INTERVAL="1000" # Metrics collection (ms)
export AGENT_AUDIT_LEVEL="detailed" # minimal | standard | detailedCreate .agent/config.json:
{
"security": {
"requireApproval": ["file_delete", "api_write", "command_execution"],
"autoRollback": true,
"anomalyThreshold": 0.8,
"maxPermissionScope": "read-write"
},
"learning": {
"enabled": true,
"minSampleSize": 10,
"abTestRatio": 0.1,
"maxStrategyComplexity": 100
},
"monitoring": {
"metricsInterval": 1000,
"auditLevel": "detailed",
"retentionDays": 90
}
}Format: TYPE-YYYYMMDD-XXX
INT: Intent specificationVIO: Violation (failed validation)ANO: Anomaly (behavioral deviation)LRN: Learning (insight from execution)STR: Strategy (new approach)RBK: Rollback operationCHK: CheckpointExamples: INT-20250115-001, VIO-20250115-A3F, LRN-20250115-002
| Priority/Severity | When to Use |
|---|---|
critical | Immediate security risk, data loss, system compromise |
high | Intent violation, unauthorized action, goal drift |
medium | Anomaly detected, suboptimal strategy, warning condition |
low | Minor deviation, optimization opportunity, observation |
Automatically apply intent security when:
High-Risk Operations:
Autonomous Workflows:
Learning Opportunities:
Enable automatic intent validation through agent hooks.
Create .claude/settings.json:
{
"hooks": {
"UserPromptSubmit": [{
"matcher": "",
"hooks": [{
"type": "command",
"command": "./skills/self-improving-intent-security-agent/scripts/intent-capture.sh"
}]
}],
"PostToolUse": [{
"matcher": "Bash|Edit|Write",
"hooks": [{
"type": "command",
"command": "./skills/self-improving-intent-security-agent/scripts/action-validator.sh"
}]
}]
}
}| Script | Hook Type | Purpose |
|---|---|---|
scripts/intent-capture.sh | UserPromptSubmit | Prompts for intent specification |
scripts/action-validator.sh | PostToolUse | Validates actions against intent |
scripts/learning-capture.sh | TaskComplete | Captures learnings after tasks |
See references/hooks-setup.md for detailed configuration.
# Initialize agent structure
mkdir -p .agent/{intents,violations,learnings,audit}
# Count active intents
grep -h "Status**: active" .agent/intents/*.md | wc -l
# List high-severity violations
grep -B5 "Severity**: high" .agent/violations/*.md | grep "^## \["
# Find learnings for file processing
grep -l "Domain**: file_processing" .agent/learnings/*.md
# Review recent rollbacks
ls -lt .agent/audit/ROLLBACKS.md | head -5
# Check strategy adoption rate
grep "Status**: adopted" .agent/learnings/STRATEGIES.md | wc -lSee examples/README.md for detailed usage examples:
Works with Claude Code, Codex CLI, GitHub Copilot, and OpenClaw. See references/multi-agent.md for agent-specific configurations.
✓ Intent Alignment - Every action validated against goal ✓ Permission Boundaries - Cannot exceed authorized scope ✓ Reversibility - Checkpoint-based rollback ✓ Auditability - Complete action history ✓ Bounded Learning - Safety-constrained improvements ✓ Human Oversight - Approval gates for high-risk operations
MIT
Note: This skill provides strong safety mechanisms but requires proper configuration and usage. Always:
Intent-based security is a powerful approach, but human judgment remains essential.
© LeoYeAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 39 other files (scripts, references, assets) in skills/self-improving-intent-security-agent of LeoYeAI/openclaw-master-skills.
Open the folder on GitHubat commit e5199b5
Self Improving Intent Security Agent next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Self Improving Intent Security Agent this skillLeoYeAI/openclaw-master-skills | 2.2k | — | ~4.8k | Automated safety check: Pass | MIT | |
| Monitor CInrwl/nx | 29k | 6 repos | ~4.7k | Automated safety check: Pass | MIT | |
| Terraform and OpenTofu Guideagentscope-ai/QwenPaw | 35k | 6 repos | ~4.2k | Automated safety check: Pass | Apache-2.0 | |
| Vercel Optimize Auditvercel-labs/agent-skills | 32k | 9 repos | ~4.3k | Automated safety check: Pass | None | |
| Openclaw Live Updateropenclaw/openclaw | 392k | — | ~3.7k | Automated safety check: Pass | MIT | |
| Analyze GitHub Action Logswithastro/astro | 63k | 1 repos | ~1.3k | Automated safety check: Pass | Custom licence |
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Runs four advisor roles, a fitness coach, nutritionist, data analyst and TCM practitioner, to build a health profile and track workouts, diet and wellness over time.
Categories
Documentation-first skill and workflow toolkit for intent-based security. Self Improving Intent Security Agent is an agent skill from LeoYeAI/openclaw-master-skills. Documentation-first skill and workflow toolkit for intent-based security.
Self Improving Intent Security Agent fits situations like: prototyping intent validation workflows; documenting high-risk operations; creating audit trails and rollback records; building your own runtime enforcement layer.
Run `npx skills add LeoYeAI/openclaw-master-skills --skill self-improving-intent-security-agent -a claude-code`. Or copy the skill folder (skills/self-improving-intent-security-agent in LeoYeAI/openclaw-master-skills) into .claude/skills/self-improving-intent-security-agent in your project. Claude Code loads it when a task matches its description.
Run `npx skills add LeoYeAI/openclaw-master-skills --skill self-improving-intent-security-agent -a codex`. Or copy the skill folder (skills/self-improving-intent-security-agent in LeoYeAI/openclaw-master-skills) into .agents/skills/self-improving-intent-security-agent in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add LeoYeAI/openclaw-master-skills --skill self-improving-intent-security-agent -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/self-improving-intent-security-agent, .gemini/skills/self-improving-intent-security-agent, .github/skills/self-improving-intent-security-agent and .opencode/skills/self-improving-intent-security-agent in your project.
Going by SKILL.md and its folder, Self Improving Intent Security Agent needs the command-line tools its instructions call (npx). Our summary lists: Node.js.
SKILL.md contains no URLs. Its commands use npx, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
Self Improving Intent Security Agent is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 4.8k tokens (SKILL.md is roughly 19k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 17k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Self Improving Intent Security Agent: Monitor CI (nrwl/nx, 29k stars), Terraform and OpenTofu Guide (agentscope-ai/QwenPaw, 35k stars), Vercel Optimize Audit (vercel-labs/agent-skills, 32k stars) and Openclaw Live Updater (openclaw/openclaw, 392k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
LeoYeAI (a GitHub user) maintains it in LeoYeAI/openclaw-master-skills, which has 2,159 GitHub stars. The repository holds 972 skills in this directory. The repository was last updated on July 20, 2026.
Source: LeoYeAI/openclaw-master-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.