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grahama1970/analyze-elf

Reverse-engineer features from ELF binaries. Extracts CLI commands, state machines, protocols, Zod schemas, and data models. Automatically generates a /create-walkthrough prosecution brief with Mermaid diagrams. Uses /treesitter for AST analysis of bundled JS/TS source.

O que é analyze-elf?

analyze-elf is a Claude Code agent skill that reverse-engineer features from ELF binaries. Extracts CLI commands, state machines, protocols, Zod schemas, and data models. Automatically generates a /create-walkthrough prosecution brief with Mermaid diagrams. Uses /treesitter for AST analysis of bundled JS/TS source.

Funciona comClaude Code~Codex CLI~CursorAntigravity
npx skills add https://github.com/grahama1970/agent-skills/tree/main/skills/analyze-elf

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Documentação

STOP. READ THIS ENTIRE SKILL.MD BEFORE CALLING ANY ENDPOINT.

O que analyze-elf faz?

Reverse-engineer features from compiled ELF binaries. Not a disassembler — a feature extractor that answers "what does this binary do?"

When to Use

  • Understanding what a compiled CLI tool does (Bun SEA, Go, Rust, C/C++)
  • Extracting CLI commands, flags, and help text
  • Recovering state machines, protocol schemas, and event types
  • Mapping JSON-RPC methods and Zod data models
  • Producing a prosecution brief walkthrough with Mermaid diagrams

Quick Start

# Primary command — extract features, generate walkthrough
.pi/skills/analyze-elf/run.sh features /path/to/binary --goal "understand the mission system"

# With AST analysis via /treesitter (slower, deeper)
.pi/skills/analyze-elf/run.sh features /path/to/binary --ast --goal "find all RPC methods"

# JSON output for programmatic use
.pi/skills/analyze-elf/run.sh features /path/to/binary --output-format json

# Low-level ELF info (headers, symbols, runtime detection)
.pi/skills/analyze-elf/run.sh elf /path/to/binary

# String extraction with regex
.pi/skills/analyze-elf/run.sh strings /path/to/binary --pattern "mission|worker"

Commands

CommandDescription
featuresPrimary. Extract features → generate walkthrough with Mermaid diagrams
elfLow-level: headers, sections, symbols, runtime detection
stringsRegex-filtered string extraction

What features Extracts

CategoryMethodExample
CLI commandsHelp text pattern matchingexec, daemon, mcp
State machinesEnum assignment patterns (M.State="value")orchestrator_turn, paused
Event typesKH.literal("type_name") patternsmission_accepted, worker_completed
JSON-RPC methodsmethod: KH.literal("name") patternsdroid.session_notification
Zod schemasKH.object({...}) field extractionFeature, WorkerHandoff, State
npm packages"name": "pkg" in bundled package.json@grpc/grpc-js
API routes/api/ and /v1/ path literals/api/auth/login
Classes/functionsAST via /treesitter (with --ast flag)MissionRunner, spawnWorker

Composition

/treesitter (AST analysis)

When --ast is passed, the skill:

  1. Carves JS source strings from .rodata
  2. Filters to JS-bearing lines (function/class/import/Zod patterns)
  3. Pipes chunks through /treesitter parse --language javascript
  4. Extracts classes, functions, and method signatures

/create-walkthrough (auto-generated)

The features command produces a prosecution brief with:

  • Mermaid CLI command tree
  • Mermaid state machine diagrams
  • Mermaid event type taxonomy
  • Schema tables with field types
  • Risk assessment ("What Could Go Wrong")

/create-figure

DOT-format dependency and module diagrams available via the elf command for rendering through /create-figure.

Dependencies

  • pyelftools — structured ELF parsing
  • httpx — /scillm API calls
  • typer — CLI
  • loguru — logging
  • 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.

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