Claude Code Skills · page 133

Crawls a new codebase to infer stack, conventions, and key invariants, then generates a PROJECT.md context file for the agent

Handles PR review feedback by fetching comments, grouping issues, fixing one group at a time, and verifying before replies.

Detects skill name shadowing and description-overlap conflicts that cause OpenClaw to trigger the wrong skill or silently ignore one when two skills compete for the same intent.

Reviews whether a skill will trigger reliably, guide useful behavior, avoid overlap, and produce testable outcomes.

Validates that a skill's companion scripts declare their OS and binary dependencies correctly, and checks whether those dependencies are actually present on the current machine.

Scores a skill's description field against sample user prompts to predict whether OpenClaw will correctly trigger it — before you publish or install.

Reviews a ClawHub skill's source code for security risks before installation. Use before installing any new skill.

Parallel subagent execution for complex tasks. Use when a task has independent parallel workstreams.

4-phase root cause process before any fix. Use whenever you encounter an error.

Red-green-refactor discipline. Use when writing new functionality.

Bootstrap skill — teaches the agent how to find and invoke skills. Use when starting any new task or session.

Ensures tasks are actually done, not just attempted. Use before declaring any task complete.

Creates clear, reviewable implementation plans before executing. Use after brainstorming and before writing any code.

Detects when the agent is stuck in a loop and escapes systematically. Use when you notice repeated failures or loss of direction.

Writes a resumé card at session end so context survives channel switches — and auto-injects it as a primer at the start of any new session

Searches Reddit communities for OpenClaw pain points and feature requests, scores them by signal strength, and writes a prioritized PROPOSALS.md for you to review and act on.

Monitors memory compaction for failures and enforces a three-level fallback chain — normal, aggressive, deterministic truncation — ensuring compaction always makes forward progress.

Scans OpenClaw config directories for plaintext API keys, tokens, and secrets in unencrypted files — flags exposure risks and suggests encryption or environment variable migration.

Scores how well the current context represents the full conversation — detects information blind spots, stale summaries, and coverage gaps that cause the agent to forget critical details.

Proactively monitors estimated token usage during long sessions and triggers context-window-management before overflow, not after. Use at the start of any session expected to last more than 30 minutes.

Prevents context overflow on long-running OpenClaw sessions. Use when approaching context limits.

Audits cron-scheduled skills for session mode, token waste, and cost efficiency — and enforces concise-reply constraints on cron contexts

Walks the memory DAG to recall detailed context on demand — query, expand, and assemble cited answers from hierarchical summaries without re-reading raw transcripts.

End-of-day structured summary and next-session prep. Use at the end of each working day or significant work block.

Intercepts irreversible or destructive actions and requires explicit user confirmation before proceeding

YAML-based delegation grant ledger — issues, validates, and tracks scoped permission grants for sub-agent expansions with token budgets and auto-expiry.

Enforces per-skill execution budgets for scheduled cron skills — pauses runaway skills that exceed their token or wall-clock budget before they drain your monthly API allowance.

Weekly audit of all installed third-party and community skills for malicious patterns, stale credentials, and drift from last-known-good state.

Detects oversized files that would blow the context window, generates structural exploration summaries, and stores compact references — preventing a single paste from consuming the entire budget.

Breaks multi-hour tasks into checkpointed stages with resume capability. Use when a task is expected to take more than 30 minutes or multiple sessions.

Detects infinite tool-call retry loops from deterministic errors and breaks them before they drain context or budget

Monitors MCP server connections for health, latency, and availability — detects stale connections, timeouts, and unreachable servers before they cause silent tool failures.

Builds hierarchical summary DAGs from MEMORY.md with depth-aware prompts — leaf summaries preserve detail, higher depths condense to durable arcs, preventing information loss during compaction.

Parses OpenClaw's flat MEMORY.md into a structured knowledge graph — detects duplicates, contradictions, and stale entries, then builds a compressed memory digest optimized for system prompt injection.

Validates memory summary DAGs for structural integrity — detects orphan nodes, circular references, token inflation, broken lineage, and stale summaries that corrupt the agent's memory.

Compiles a daily morning briefing from active tasks, priorities, and pending items. Use at the start of each working day.

Orchestrates multiple parallel OpenClaw agents — tracks health, detects timeouts, reconciles conflicting outputs, and manages structured handoffs

Diagnoses OpenClaw provider, fallback, channel, MCP, and gateway config issues with read-only scans and stateful summaries.

Keeps OpenClaw's memory store clean, structured, and useful. Use at session end and during periodic maintenance.

Detects and intercepts prompt injection attempts in external content before the agent acts on them

Tracks required validation gates, records pass/fail/waived results, and reports readiness before task completion.

Audits which skills have access to secrets, flags stale or unrotated credentials, and prompts rotation. Use weekly to keep credentials clean.

Imports OpenClaw session transcripts into a local SQLite database with FTS5 full-text search — the agent never loses a message, even after context compaction or session rollover.

Checks whether installed skills are compatible with the current OpenClaw version and flags skills that require runtime features not present in your installation.

Diagnoses silent skill discovery failures — YAML parse errors, path violations, schema mismatches — so broken skills don't disappear without a trace.

Manages named skill profiles (loadouts) so you can switch between focused skill sets and prevent system prompt bloat from too many active skills.

Tracks cumulative API spend against a monthly budget and pauses non-essential automations when thresholds are crossed

Gracefully hands off incomplete tasks across agent restarts. Use when work must be paused mid-task.

Analyzes skill descriptions for trigger quality — scores clarity, keyword density, and specificity, then suggests rewrites that improve discovery accuracy.

Chains multiple skills into a named multi-step workflow with conditions and state tracking. Use when a recurring task requires the same sequence of skills every time.

Detects drift or tampering in SOUL.md, AGENTS.md, MEMORY.md, and other critical workspace files and prompts recovery

Every AI output has structural blind spots determined by the generation process itself. Future Tokens operations are named, composable instruments that target specific blind spots. They surface new information on every pass because expanding the output changes the blind spot geometry.

>

Set up and maintain Hierarchical Agent Memory (HAM) for Codex using scoped local memory files. Trigger on "go ham", "set up HAM", "ham commands", "ham help", "ham route", "ham remove", "ham update", "ham status", "ham benchmark", "ham baseline start", "ham baseline stop", "ham metrics clear", "HAM savings", "HAM stats", "HAM dashboard", "HAM sandwich", "HAM insights", "HAM carbon", or "HAM audit".

Use this skill when reverse-engineering Apple platforms with the ipsw CLI — analyzing iOS/macOS Mach-O binaries, disassembling functions inside dyld_shared_cache (DSC), dumping Objective-C/Swift headers from private frameworks, extracting kernelcaches, KEXTs, SEP, iBoot, or DeviceTree from IPSWs/OTAs, decompiling/querying sandbox profiles (SBPL), querying entitlements, symbolicating crashes/panics, diffing two firmware versions, mounting IPSW DMGs, or downloading Apple firmware. Triggers on iOS/macOS internals, kernel research, dyld_shared_cache, KEXT diffing, sandbox/Seatbelt profile analysis (SBPL/SBASM/sandboxd), capability/IOKit queries, entitlement lookup, class-dump, IMG4/AEA handling, or vulnerability research on Apple platforms.

>-

>-

>-

>-

>-

>-

>-

>-

>

>

>

>

>

>

>

>

>

Corezoid marketplace pre-publication validator. Standalone skill — no external skill dependencies required. Use this skill whenever the user wants to publish, release, or submit a project or folder to the Corezoid marketplace, or asks to check if a project is ready to publish. Activate on phrases: \"publish to marketplace\", \"готово до публікації\", \"можна публікувати\", \"перевір перед публікацією\", \"validate for marketplace\", \"pre-publish check\", \"marketplace validation\", \"publication readiness\", \"check before publish\", \"submit to marketplace\", \"перевірка публікації\". Also activate when the user asks \"what's blocking publication\" or \"why can't I publish this\".

>

>

>

>

>

>

>

>

>

>

>

>

>

>

>

>

>

>

>

>

>

>

>

>

>

>