129 skills · sorted by relevance
Compress memory files and task lists into shorter wording to reduce input tokens. Keeps code and document structure intact, saves a readable backup separately, and sends the text to the configured model provider.
Connect an application's LLM calls through the Caveman Cloud gateway to measure usage and cost without changing request content. The skill updates connection settings and verifies the integration with one small, real provider request.
Look up commands for concise response modes, commit messages, review comments, and Markdown compression. The reference also explains how to check the current mode, return to normal responses, and configure startup defaults.
Investigate unclear failures, intermittent behavior, and performance regressions before changing code. Separate symptoms from assumptions, trace how failures arise, and rank possible causes by evidence and ease of testing, then report the cause and supporting proof.
Create short commit messages using Conventional Commits, a format that labels the type of change. Emphasize intent, keep the subject concise, and include explanatory text when breaking changes, security fixes, data migrations, or reversions require context.
Rewrite agent replies in Classical Chinese, a compact historical writing style, while keeping code and technical identifiers unchanged. Use it to shorten on-screen text and avoid repeated explanations, with ordinary prose when clarity requires it.
Compress agent replies into short phrases while preserving technical details, negations, numbers, and exact commands. Use it to reduce repeated explanations and keep chat responses brief until you explicitly turn the mode off.
Make agent replies shorter and easier to scan by putting answers first and removing filler. Preserves technical details, exact commands, numbers, and essential conditions while keeping the user's chosen language.
Review code changes and produce concise comments ready to paste into a pull request. Each finding includes its location, the problem, and a concrete fix, with fuller explanations for security issues, architectural disagreements, or authors who need context.
Inspect RuView sensing hardware, including ESP32 nodes, mmWave radars, and LiDAR distance sensors. Find connected devices, read logs and live measurements, and use SSH to access supported read-only tools on another machine.
Review HTTP response headers and restrict browser features an application does not need, such as camera, microphone, and location access. Guide Permissions-Policy settings that limit what attackers could use if the site is compromised.
Support authorized hardware security research by locating debug interfaces, recording pin assignments and voltage levels, and collecting boot logs. Assess interface locks and firmware extraction options, then hand extracted images to firmware analysis tools.
Design monitoring for industrial attacks that alter controller programs while hiding changes with false sensor readings. Compare PLC program blocks against known-good copies and check physical measurements against process models to identify suspicious inconsistencies.
Analyze endpoint, cloud storage, and email logs for signs of insider data theft. Use pandas to compare upload volumes with normal user behavior and examine data loss prevention alerts, unusual file access, and activity outside working hours.
Investigate malware that survives OS reinstallation by checking UEFI firmware, boot files, and Secure Boot settings. Use chipsec and supporting tools to compare trusted baselines, identify bootkit indicators, and document findings.
Investigate malware hidden in boot records, UEFI firmware, or the operating system’s core. The skill guides evidence collection, boot integrity checks, firmware and memory analysis, and reporting of persistence mechanisms and remediation steps when compromise survives an OS reinstall.
Add a NanoClaw status icon to the macOS menu bar. Check whether the service is running through a green or red dot, then start, stop, restart, or open its logs from the menu.
Run MNN checks and regression tests on a host computer or Android device, with stage summaries and failure logs. A separate workflow benchmarks LLM prompt processing and token generation on real iPhones and iPads, including branch comparisons.
Plan security testing for IoT and embedded devices, from circuit board inspection and firmware analysis to wireless links, companion apps, and cloud APIs. Includes tools, investigation checklists, and guidance for documenting findings.
Create a project-specific SKILL.md that teaches an agent how to launch, operate, and verify an app. It tests a representative workflow first, then records reliable steps, evidence of success, cleanup instructions, and maintenance guidance.
Dynamic instrumentation via Frida - attach to or spawn a process, load a JS hook script, capture send() events into a lifecycle-managed run directory. Supports local, USB-attached, and remote frida-server targets.
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Traffic analysis and PCAP forensics playbook. Use when analyzing network captures including Wireshark filters, protocol analysis (HTTP/DNS/FTP/SMTP/USB/WiFi), data extraction, covert channel detection, PCAP repair, TLS decryption, and tshark command-line analysis.
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Extract structured specifications from electronic component datasheet PDFs — pinouts, electrical characteristics, peripherals, topology, and features. Cache extractions per project for consumption by schematic and PCB analyzers. Primary consumer infrastructure for `kicad`, `emc`, `spice`, and `thermal` analyzers. Use this skill whenever the user asks to extract, verify, or read specs from a component datasheet; when analyzers need verified IC knowledge (EN pin thresholds, PG presence, USB peripheral speed); or when a review mentions datasheet coverage, extraction quality, or per-MPN specifications. Also triggers on "extract this datasheet", "what are the specs for MPN X", "verify datasheet extraction", or "check pin functions for part Y".
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Extract structured specifications from electronic component datasheet PDFs — pinouts, electrical characteristics, peripherals, topology, and features. Cache extractions per project for consumption by schematic and PCB analyzers. Primary consumer infrastructure for `kicad`, `emc`, `spice`, and `thermal` analyzers. Use this skill whenever the user asks to extract, verify, or read specs from a component datasheet; when analyzers need verified IC knowledge (EN pin thresholds, PG presence, USB peripheral speed); or when a review mentions datasheet coverage, extraction quality, or per-MPN specifications. Also triggers on "extract this datasheet", "what are the specs for MPN X", "verify datasheet extraction", or "check pin functions for part Y".
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