CommunityRecherche & Datenanalysegithub.com

grahama1970/analyze-elf

Reverse-Engineering von Features aus ELF-Binaries. Extrahiert CLI-Befehle, Zustandsmaschinen, Protokolle, Zod-Schemas und Datenmodelle. Erzeugt automatisch eine /create-walkthrough-Anklageskizze mit Mermaid-Diagrammen. Nutzt /treesitter zur AST-Analyse von gebündeltem JS/TS-Quellcode.

Was ist analyze-elf?

Dieses Skill führt strukturelle Feature-Extraktion an kompilierten ELF-Binaries durch, ohne auf traditionelles Disassemblieren angewiesen zu sein. Es identifiziert, wofür ein Binary verwendet wird (z.B. CLI-Tools, Server, Agenten-Frameworks) und extrahiert CLI-Befehle, Zustandsmaschinen, JSON-RPC-Methoden, Zod-Schemas und API-Routen. Die Analyse nutzt Mustererkennung in Zeichenketten (help text patterns, enum assignments) und, falls mit `--ast` aktiviert, AST-Parsing über /treesitter für gebündelten JavaScript/TypeScript-Code. Das Skill erzeugt automatisch eine /create-walkthrough-Anklageskizze mit Mermaid-Diagrammen (CLI-Befehlsbaum, Zustandsmaschinen, Ereignistypen) und unterstützt Low-Level-ELF-Abfragen (Headers, Symbole, Laufzeitumgebung) sowie regex-gefilterte String-Extraktion. Es ist für Plattformen wie Cursor, Claude Code (via .pi/skills) und Codex geeignet. Wichtige Abhängigkeiten sind pyelftools, httpx, typer, loguru und Systemtools (binutils). Entwickler können damit schnell die Fähigkeiten und das Verhalten eines unbekannten kompilierten Programms verstehen, ohne Quellcode zu benötigen.

Funktioniert mit✓Claude Code~Codex CLI~Cursor✓Antigravity
npx skills add https://github.com/grahama1970/agent-skills/tree/main/skills/analyze-elf

Installed? Explore more Recherche & Datenanalyse skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →

Original anzeigen→Alle Skills durchsuchen

In Ihrer bevorzugten KI fragen

Öffnet einen neuen Chat, in dem dieser Agent-Skill bereits geladen ist.

OpenAIChatGPTClaudeClaudeGeminiGeminiGrokGrokPerplexityPerplexityDeepSeekDeepSeek

Dokumentation

STOP. LESEN SIE DIESES SKILL.MD VOLLSTÄNDIG, BEVOR SIE EINEN ENDPUNKT AUFRUFEN.

Was macht analyze-elf?

Reverse-Engineering von Features aus kompilierten ELF-Binaries. Kein Disassembler — ein Feature-Extraktor, der die Frage beantwortet: „Was macht dieses Binary?“

Wann verwenden

  • Verstehen, was ein kompiliertes CLI-Tool tut (Bun SEA, Go, Rust, C/C++)
  • Extrahieren von CLI-Befehlen, Flags und Hilfetexten
  • Wiederherstellen von Zustandsmaschinen, Protokollschemata und Ereignistypen
  • Abbilden von JSON-RPC-Methoden und Zod-Datenmodellen
  • Erstellen einer Anklageskizze mit Mermaid-Diagrammen

Schnellstart

# Primärer Befehl — Features extrahieren, Walkthrough erzeugen
.pi/skills/analyze-elf/run.sh features /pfad/zum/binary --goal „das Missionssystem verstehen“

# Mit AST-Analyse über /treesitter (langsamer, tiefergehend)
.pi/skills/analyze-elf/run.sh features /pfad/zum/binary --ast --goal „alle RPC-Methoden finden“

# JSON-Ausgabe für programmatische Nutzung
.pi/skills/analyze-elf/run.sh features /pfad/zum/binary --output-format json

# Low-Level-ELF-Informationen (Header, Symbole, Laufzeiterkennung)
.pi/skills/analyze-elf/run.sh elf /pfad/zum/binary

# String-Extraktion mit regulären Ausdrücken
.pi/skills/analyze-elf/run.sh strings /pfad/zum/binary --pattern „mission|worker“

Befehle

BefehlBeschreibung
featuresPrimär. Features extrahieren → Walkthrough mit Mermaid-Diagrammen erzeugen
elfLow-Level: Header, Abschnitte, Symbole, Laufzeiterkennung
stringsRegex-gefilterte String-Extraktion

Was features extrahiert

KategorieMethodeBeispiel
CLI-BefehleMusterabgleich von Hilfetextenexec, daemon, mcp
ZustandsmaschinenEnum-Zuweisungsmuster (M.State="wert")orchestrator_turn, paused
EreignistypenKH.literal("typenname")-Mustermission_accepted, worker_completed
JSON-RPC-Methodenmethod: KH.literal("name")-Musterdroid.session_notification
Zod-SchemasFeld-Extraktion aus KH.object({...})Feature, WorkerHandoff, State
npm-Pakete"name": "pkg" in gebündelten package.json@grpc/grpc-js
API-RoutenPfad-Literale mit /api/ und /v1//api/auth/login
Klassen/FunktionenAST über /treesitter (mit --ast-Flag)MissionRunner, spawnWorker

Zusammenspiel

/treesitter (AST-Analyse)

Wenn --ast übergeben wird, führt das Skill folgende Schritte aus:

  1. Ausschneiden von JS-Quelltext-Strings aus .rodata
  2. Filtern auf JS-relevante Zeilen (Muster für Funktionen/Klassen/Imports/Zod)
  3. Übergeben der Abschnitte an /treesitter parse --language javascript
  4. Extrahieren von Klassen, Funktionen und Methodensignaturen

/create-walkthrough (automatisch erzeugt)

Der Befehl features erzeugt eine Anklageskizze mit:

  • Mermaid-CLI-Befehlsbaum
  • Mermaid-Zustandsmaschinendiagramme
  • Mermaid-Ereignistyp-Taxonomie
  • Schematabellen mit Feldtypen
  • Risikobewertung („Was könnte schiefgehen?“)

/create-figure

DOT-Format-Abhängigkeits- und Moduldiagramme sind über den Befehl elf verfügbar zur Darstellung durch /create-figure.

Abhängigkeiten

  • pyelftools — Strukturierte ELF-Analyse
  • httpx — /scillm-API-Aufrufe
  • typer — CLI
  • loguru — Protokollierung
  • System: binutils (readelf, nm, strings)

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

grahama1970/acceptance-contract

Turn a client brief, zip bundle, directory, or single requirements file into a typed acceptance-contract bundle with extracted requirements, acceptance checks, open questions, an immutable-goal draft, and a create-report-backed decision report. Use when users say acceptance contract, brief to requirements, freeze the goal, create immutable goal, amend immutable goal, build a Battle requirements bundle, or extract requirements from this bundle.

grahama1970/agent-ecosystem

Canonical map and shared contracts for the agent-governance ecosystem: the pi.receipt_envelope.v1 boundary envelope, the component graph, and the rules for which component owns which schema. Use when wiring a skill or extension into the shared receipt world, when asking how shame, triage-error, tau, ask, project-watchdog, ops-herdr, ponytail, and Memory fit together, or when validating an envelope.

grahama1970/agentic-evals

Agentic evaluation of skills using multi-trial fixtures, deterministic command assertions, trajectory checks, safety constraints, and evidence-backed readiness scoring. Use when users ask for agentic evals, multi-trial skill evaluation, skill trajectory validation, or readiness scoring for a skill workflow.

grahama1970/agent-inbox

File-based inter-agent messaging with headless dispatch. Check inbox, send bugs/requests to other projects, automatically spawn headless agents to fix bugs, and track progress via task-monitor.

grahama1970/agents-registry

Generate and query the centralized agent identity registry. Scans .pi/agents/*/AGENTS.md, parses frontmatter, outputs agents-registry.json and optionally syncs to /memory for semantic search.

grahama1970/agent-status

Artifact-driven status surfaces for long-running project-agent work. Maintains status.json, events.jsonl, proof manifests, and a stale-aware STATUS.html so humans can tell where the agent is, what passed, what is still unproven, and what decision or action is next — without dashboard theater.

grahama1970/align

Round-based context alignment before execution. Use when the human, project agent, WebGPT, scillm, ask, dogpile, memory, or project-knowledge may each hold different facts about a task; especially before ambiguous design, infographic, product workflow, high-stakes implementation, plan-iterate, project-infographic, or multi-review work.

grahama1970/analytics

Flexible data science analytics for any dataset. Auto-discovers schema, recommends charts, exports to create-figure. Works with JSONL, JSON, CSV from any source.

grahama1970/analyze-chatterbox-emotions

Evaluate generated Chatterbox voice files as voice-quality artifacts: affect match, arousal/valence proxies, pause placement, intelligibility inputs, clipping, loudness, and discontinuity flags. Use when reviewing Chatterbox emotional tags, pauses, Turbo/base affect delivery, Persona Dream utterance renders, or whether generated speech matches an intended product-facing affect.

grahama1970/animation-vocabulary

Reverse-lookup glossary that turns a vague description of a web animation or motion effect into its exact term ("the bouncy thing when a popover opens" → Pop in; "the iOS rubber-band scroll" → Rubber-banding). Use when the user asks "what's it called when…", or describes a motion effect without knowing its name and wants the right word to prompt an AI or designer with. For naming an effect, not designing or building one.

grahama1970/anonymize-data

Anonymize supported CSV, JSON, UTF-8 text, and SQLite files using an explicit policy through the oai-trial project. Use for anonymize data, pseudonymize exports, redact policy literals, or discover and explicitly approve fuzzy name aliases. The skill is a thin CLI/Docker interface, not another engine.

grahama1970/anvil

Heavy-duty "No-Vibes" debugging and hardening orchestrator. Use this for complex, stubborn bugs where `review-code` has failed, or for "Red Teaming" (hardening) a codebase. Runs multiple agents in parallel (Thunderdome) using git worktree isolation.

grahama1970/apple-design

Apple's approach to interface design and fluid, physical motion, translated for the web. Use when building or reviewing gesture-driven UI, spring animations, drag/swipe/sheet interactions, momentum and interruptible transitions, translucent materials and depth, typography (optical sizing, tracking, leading), reduced-motion, or the design foundations (feedback, spatial consistency, restraint) behind Apple-style interfaces.

grahama1970/argue

Multi-persona structured debate orchestrator. Personas research via /dogpile, consult colleagues via /ask, and argue toward nuanced synthesis on complex questions.

grahama1970/arxiv

Search arXiv for papers and extract knowledge into memory. Use `search` to find papers, `learn` to extract knowledge.

grahama1970/ask

Use when the user asks to query project memory, ask an oracle, use supported browser-backed reviewers, run Tau roundtable/single-handler workflows, ask Pi-native subagents from within Pi, run persona/deep-review workflows, generate image prompts, check OS/project health through composed skills, or run an ask DAG. This skill is the executable /ask runtime; do not replace it with an informal subagent, plain web search, or hand-written review; inside Pi, explicit Pi-native subagent targets are routed through the pi-subagents tool as an Ask target type.

grahama1970/assess

Step back and critically reassess project state. Use when asked to "assess", "step back", "fresh eyes", "check alignment", "sanity check", "health check", "prune documentation", or "evaluate what's working". Offers documentation pruning and doc-code alignment analysis. Offer to run after major changes (don't auto-run).

grahama1970/assistant

Shared GPT + classifier inference gateway for persona monitor tasks. Routes validation and classification through a 4-tier cascade: heuristic → classifier → local GPT → scillm.

grahama1970/assistant-lab

Self-improvement workbench for /assistant. All the tools needed to diagnose, train, evaluate, and promote models in a continuous loop. The "warm pond" where /assistant evolves its own inference stack.

grahama1970/batch-quality

Pre-flight validation and quality gates for batch LLM operations. ACTUALLY tests samples through LLM before burning tokens. Uses SPARTA contracts for DuckDB validation queries. Integrates with task-monitor for enforced quality gates.

Verwandte Skills

affaan-m/uspto-database

USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.

community

affaan-m/scholar-evaluation

Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.

community

affaan-m/literature-review

Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.

community

affaan-m/everything-claude-code

The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.

community

forrestchang/andrej-karpathy-skills

Agent skill repository discovered by 10x-chat research.

community

langgenius/backend-code-review

Review backend code for quality, security, maintainability, and best practices based on established checklist rules. Use when the user requests a review, analysis, or improvement of backend files (e.g., `.py`) under the `api/` directory. Do NOT use for frontend files (e.g., `.tsx`, `.ts`, `.js`). Supports pending-change review, code snippets review, and file-focused review.

community
← More Recherche & Datenanalyse skills