Results for “fid”

Analyzes malicious PDF files using PDFiD, pdf-parser, and peepdf to identify embedded JavaScript, shellcode, exploits, and suspicious objects without opening the document. Determines the attack vector and extracts embedded payloads for further analysis. Activates for requests involving PDF malware analysis, malicious document analysis, PDF exploit investigation, or suspicious attachment triage.

The English summary is being prepared. Showing the original description.

[32] ANALYZE. Ensure every critical claim has verifiable evidence with confidence levels. Each fact must have source + confidence percentage. If confidence <85%, enter Loop150 to find more sources. Use for critical decisions, factual claims, legal/compliance work, or any situation where unverified claims are dangerous.

The English summary is being prepared. Showing the original description.

Analyzes malicious PDF files using PDFiD, pdf-parser, and peepdf to identify embedded JavaScript, shellcode, exploits, and suspicious objects without opening the document. Determines the attack vector and extracts embedded payloads for further analysis. Activates for requests involving PDF malware analysis, malicious document analysis, PDF exploit investigation, or suspicious attachment triage.

The English summary is being prepared. Showing the original description.

Turns untargeted LC-MS/MS features (m/z, RT, MS/MS) into confidence-stratified metabolite annotations using spectral-library matching (matchms), in-silico tools (SIRIUS/CSI:FingerID, MetFrag) and molecular networking, and assigns a defensible MSI/Schymanski confidence level to each. Use when naming detected features, scoring MS/MS against a reference library, running SIRIUS, or deciding what confidence level an evidence set actually supports. For upstream feature extraction see metabolomics/xcms-preprocessing and metabolomics/msdial-preprocessing; for downstream enrichment that must respect these levels see metabolomics/pathway-mapping; for lipid-specific structural annotation see metabolomics/lipidomics.

The English summary is being prepared. Showing the original description.

Build low-fidelity wireframes, screen mockups, and user-flow diagrams in Frame0 (the hand-drawn Balsamiq-style wireframing tool) through its connected MCP server. Use when asked to wireframe a screen, sketch a UI, make a low-fi mockup, lay out an app's screens, or diagram a user flow with clickable links. Triggers: "wireframe", "frame0", "low-fi mockup", "sketch a screen", "user flow", "lo-fi", "rough out the UI". NOT for high-fidelity visual design, brand systems, or color/typography polish (use web-design-expert / Figma); NOT for production UI code (use frontend skills).

The English summary is being prepared. Showing the original description.

Validates agent outputs against schemas and quality criteria, scores confidence, detects hallucinations, monitors convergence, decides when to iterate, and synthesizes actionable feedback. Use when checking if a node's output is acceptable, scoring confidence, detecting fabricated content, deciding whether to re-execute, or generating improvement feedback. Activate on "validate output", "check quality", "confidence score", "hallucination check", "should we iterate", "improvement feedback". NOT for executing DAGs (use dag-runtime), planning DAGs (use dag-planner), or matching skills (use dag-skills-matcher).

The English summary is being prepared. Showing the original description.

Diagnose when intuitive judgment, agent confidence, or expert routing can be trusted by classifying environment validity, feedback quality, and task-boundary fit. Use for confidence calibration, agent routing, expertise audits, and escalation design. NOT for deterministic implementation tasks, pure syntax debugging, or domains with explicit verifiable answers.

The English summary is being prepared. Showing the original description.

Diagnose when intuitive judgment, agent confidence, or expert routing can be trusted by classifying environment validity, feedback quality, and task-boundary fit. Use for confidence calibration, agent routing, expertise audits, and escalation design. NOT for deterministic implementation tasks, pure syntax debugging, or domains with explicit verifiable answers.

The English summary is being prepared. Showing the original description.

Audit a compliance-ladder design (C0-C6) and transcript-fidelity ladder (T0-T5) plus their adapter conformance fixtures — Codex, Claude Code, Cloudflare, Ollama/LM Studio, custom stdio/HTTP agents — for the two failure modes that make a C-badge or T-fidelity label a lie: the ladder disagreeing with itself across doc/schema/ui/probe surfaces, and a level being reachable by self-report because no daemon-witnessed negative probe actually tries to forge it. Use when designing or freezing a compliance ladder, wiring an adapter conformance probe suite, deciding whether a numeric C-badge or T-label is safe to ship, or investigating a self-attestation or observed-to-controlled bypass risk. NOT for auditing a cryptographic protocol or whitepaper for adversarial soundness (`redteam-review`), writing TLA+/formal invariants for a daemon monitor (`runtime-verification-for-agents`), or agent identity, successor linkage, and reputation across restarts (`agent-identity-continuity-reputation`).

The English summary is being prepared. Showing the original description.

[32] ANALYZE. Ensure every critical claim has verifiable evidence with confidence levels. Each fact must have source + confidence percentage. If confidence <85%, enter Loop150 to find more sources. Use for critical decisions, factual claims, legal/compliance work, or any situation where unverified claims are dangerous.

The English summary is being prepared. Showing the original description.

Analyzes malicious PDF files using PDFiD, pdf-parser, and peepdf to identify embedded JavaScript, shellcode, exploits, and suspicious objects without opening the document. Determines the attack vector and extracts embedded payloads for further analysis. Activates for requests involving PDF malware analysis, malicious document analysis, PDF exploit investigation, or suspicious attachment triage.

The English summary is being prepared. Showing the original description.

Turn Claude Code session patterns into reusable behavior rules with confidence scores. Keep project-specific lessons separate, review and share learned rules, and combine related patterns into skills, commands, or agents.

• Capture lessons from corrections and repeated workflows
• Keep learned conventions separate for each project
• Turn related behavior rules into skills or commands

Research a topic across multiple web sources, break it into focused questions, and read key pages in depth. Produce a report with source links, confidence levels, practical findings, and clear distinctions between facts and estimates.

• Investigating competitors and market trends
• Comparing technologies before choosing one
• Researching market size and growth forecasts

Cross-reference public records to investigate companies, contracts, sanctions, property, and litigation. Match names across sources, label confidence levels, and build structured findings linked to original records, with optional statistical analysis of event timing.

• Researching company control and corporate filings
• Checking entities against sanctions and offshore records
• Comparing lobbying records with government contracts

Build or refine a 3D scene by comparing live screenshots with a target image. This skill repeatedly evaluates composition, lighting, materials, and detail, then guides revisions while checking that rendering speed remains acceptable for the target platform.

• Build a 3D scene to match a target image
• Improve lighting and materials in a browser game
• Refine the appearance of an existing 3D project

Create a desktop landing page wireframe, a simple layout made of grey boxes, with numbered markers and a matching specification panel. The result shows page structure alongside short implementation and user experience notes.

• Planning a desktop landing page layout
• Sharing implementation notes with developers
• Reviewing navigation, hero, and feature placement

Read, create, and edit .docx documents while paying attention to formatting and layout fidelity. Use it for Word document tasks where both the written content and its presentation need attention.

• Reading the contents of an existing .docx document
• Creating a Word document with formatting and layout
• Editing a document while preserving its presentation

Retrieve a country's resilience score, a measure of preparedness and recovery capacity, on a 0–100 scale. Review six domain scores, three broader components, recent trends, the 30-day change, and a confidence range for the result.

• Checking one country's overall resilience
• Reviewing strengths and weaknesses across six domains
• Tracking the score's 30-day change

Guide market prediction workflows with LSTM, Transformer, and N-BEATS models using neural-trader commands and result storage. It explicitly identifies version 2.8.11 outputs as placeholders, so they cannot be treated as fitted models or evidence of prediction accuracy.

• Preparing market prediction model commands
• Checking whether training outputs are placeholders
• Organizing model run records

Apply simple code changes across files without calling an LLM, using agent-booster's local transformation engine. The skill checks a confidence score before saving results and records outcomes for cost tracking and future routing decisions.

• Apply repetitive code changes across many files
• Remove console statements
• Run repeatable code transformations in CI

Choose the next task using patterns learned from completed work and current progress. The skill acts on confident predictions, falls back to task priorities when confidence is low, and updates its learning after completion.

• Choosing the next task from current progress
• Guiding repeated work within a /loop
• Finding relevant examples in past completions

Investigate failed Agent QA runs using recorded steps, logs, and saved evidence. Classify each failure, explain confidence and supporting findings, and identify the likely area to fix and the next action.

• Investigating a failed or interrupted Agent QA run
• Reviewing saved evidence and execution logs
• Comparing recurring failures across recent runs

Route a goal through acc's scored-memory workflow, which uses evaluated stored information. Handle returned reasoning frames, submit proposals with confidence estimates, and present final answers with reference IDs before recording the eventual outcome.

• Submit a goal to acc's scored-memory workflow
• Review a returned frame and submit a proposal
• Present answers with commitment and reference IDs

Solve competition mathematics problems and examine proofs through independent agents that actively look for gaps, counterexamples, and misused theorems. Produces a checked argument or clearly states what remains unresolved, with optional PDF output.

• Solve an IMO or Putnam problem
• Check a competition proof for gaps
• Find counterexamples to claims within a proof