{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-mksglu--context-mode","slug":"mksglu--context-mode","name":"context-mode","type":"product","url":"https://context-mode.com","page_url":"https://unfragile.ai/mksglu--context-mode","categories":["code-editors"],"tags":["antigravity","claude","claude-code","claude-code-hooks","claude-code-plugins","claude-code-skill","codex","codex-cli","context-mode","copilot","cursor-plugin","kiro","mcp","mcp-server","mcp-tools","openclaw","opencode","pi-agent","skills","zed-extension"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-mksglu--context-mode__cap_0","uri":"capability://code.generation.editing.sandboxed.polyglot.code.execution.with.context.aware.output.filtering","name":"sandboxed polyglot code execution with context-aware output filtering","description":"Executes code in isolated subprocess environments across 11 languages (Python, Node.js, Go, Rust, Java, C++, C#, Ruby, PHP, Bash, Deno) using PolyglotExecutor runtime detection. Only stdout is captured and returned to context; stderr, logs, and intermediate state remain sandboxed. Implements intent-driven filtering to reduce 56 KB Playwright snapshots to 299 B (99% reduction) by extracting only semantically relevant output lines rather than raw dumps.","intents":["Execute code snippets without flooding the context window with full output","Run multi-language scripts safely without exposing system state or background processes","Get concise execution results that preserve intent while dropping noise (logs, warnings, intermediate state)","Test code changes iteratively without context window degradation over 30+ iterations"],"best_for":["AI coding agents working on multi-language projects (Python + Node.js + Go stacks)","Teams using Claude Code, Gemini CLI, or Cursor with long-running sessions","Developers building agents that need to execute untrusted or exploratory code safely"],"limitations":["Subprocess isolation adds ~50-200ms latency per execution depending on language startup time","Background processes spawned in subprocesses are not automatically tracked; requires explicit cleanup hooks","No built-in timeout enforcement — long-running code can block the MCP server unless wrapped with external timeout wrapper","Language-specific features (e.g., async/await in Node.js) require explicit runtime configuration per language"],"requires":["Node.js 18+ (MCP server runtime)","Python 3.9+ (if executing Python code)","Go 1.18+, Rust 1.70+, Java 11+, or other language runtimes for respective language support","Unix-like shell (bash/zsh) for subprocess spawning; Windows requires WSL or native Windows subprocess API"],"input_types":["code (raw string or file path)","execution arguments (command-line flags, environment variables)","language identifier (auto-detected or explicit)"],"output_types":["stdout (filtered, intent-aware)","exit code (integer)","execution metadata (duration, memory usage if available)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mksglu--context-mode__cap_1","uri":"capability://memory.knowledge.fts5.based.full.text.search.knowledge.base.with.bm25.ranking","name":"fts5-based full-text search knowledge base with bm25 ranking","description":"Indexes arbitrary content (code files, documentation, API responses, logs) into a SQLite FTS5 (Full-Text Search 5) database with BM25 relevance ranking. Agents query the knowledge base via ctx_search to retrieve semantically relevant snippets (40 B average) instead of dumping entire 60 KB documents into context. Supports incremental indexing via ctx_index and batch fetch-and-index via ctx_fetch_and_index for GitHub issues, API responses, and file trees.","intents":["Search across indexed codebase or documentation without loading full files into context","Retrieve relevant code snippets or API docs on-demand based on semantic queries","Build a persistent knowledge base that survives session compaction and context window resets","Batch-index external content (GitHub issues, API docs, logs) and query it with natural language"],"best_for":["Long-running coding sessions where agents need to reference large codebases or documentation","Teams managing multi-repo projects with shared knowledge bases across sessions","Agents building context-aware code generation by searching relevant patterns before writing"],"limitations":["FTS5 BM25 ranking is lexical, not semantic — queries like 'how to authenticate' may miss conceptually similar code if keywords don't match","Indexing large codebases (>100K files) can consume 500 MB+ SQLite database; no built-in sharding or distributed indexing","Search results are limited to indexed content; real-time data (live API responses, streaming logs) requires explicit re-indexing","No vector embeddings — cannot perform semantic similarity search across code semantics, only keyword matching"],"requires":["SQLite 3.9+ (FTS5 extension support)","Disk space for database (rough estimate: 1 MB per 10K lines of indexed code)","Content to index must be provided as text or file paths; binary files are skipped"],"input_types":["text content (code, documentation, logs)","file paths (for batch indexing)","search queries (natural language or keyword-based)","external URLs (for ctx_fetch_and_index)"],"output_types":["ranked search results (snippets with relevance scores)","metadata (file path, line number, context window size)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mksglu--context-mode__cap_10","uri":"capability://automation.workflow.cli.based.diagnostics.and.health.checks.with.auto.remediation","name":"cli-based diagnostics and health checks with auto-remediation","description":"Provides ctx_doctor CLI command that runs comprehensive health checks on the context-mode installation, session database, knowledge base, and platform adapters. Checks include: verifying SQLite database integrity, validating hook registration with the platform, checking for orphaned sessions, detecting corrupted index entries, and verifying language runtime availability. For detected issues, ctx_doctor suggests remediation steps (e.g., 'run ctx_upgrade to fix schema version mismatch') or automatically applies fixes (e.g., removing orphaned sessions).","intents":["Diagnose context-mode installation issues and get remediation recommendations","Verify that all platform adapters and hooks are correctly registered","Check database integrity and detect corrupted or orphaned sessions","Automatically fix common issues without manual intervention"],"best_for":["Developers troubleshooting context-mode installation or configuration issues","Operations teams monitoring context-mode health in production","Users experiencing unexpected behavior and needing to diagnose root causes"],"limitations":["Auto-remediation is limited to safe operations (e.g., removing orphaned sessions) — does not attempt to fix complex issues","Diagnostics are point-in-time snapshots — may not catch intermittent issues","No integration with external monitoring systems — diagnostics are CLI-only","Remediation suggestions are generic — may not be optimal for all configurations"],"requires":["Node.js 18+ (MCP server runtime)","SQLite 3.9+ (for database integrity checks)"],"input_types":["optional: specific check to run (e.g., 'database', 'hooks', 'runtimes')"],"output_types":["health check results (pass/fail for each check)","list of detected issues with severity (error, warning, info)","remediation suggestions (list of recommended actions)","auto-remediation log (list of fixes applied)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mksglu--context-mode__cap_11","uri":"capability://tool.use.integration.hook.based.lifecycle.interception.with.event.extraction.and.state.mutation","name":"hook-based lifecycle interception with event extraction and state mutation","description":"Implements a hook system that intercepts agent execution at four lifecycle points: PreToolUse (before tool execution), PostToolUse (after tool execution), PreCompact (before context compaction), and SessionStart (at session initialization). Each hook receives event data (tool call, tool output, context state) and can mutate state (filter output, inject snapshots, modify directives). PostToolUse hook includes event extraction logic that parses tool output and extracts semantic events (file edited, test passed, error resolved) for session continuity. Hooks are registered per-platform and can be chained (multiple hooks per lifecycle point).","intents":["Intercept tool calls and outputs at specific lifecycle points to apply context optimization","Extract semantic events from tool output for session continuity and state reconstruction","Inject session snapshots and directives at the right time in the agent's execution","Implement custom logic at agent lifecycle points without modifying platform code"],"best_for":["Platform developers integrating context-mode into their AI agents","Teams implementing custom context optimization logic beyond the built-in tools","Developers needing fine-grained control over agent execution and state management"],"limitations":["Hook timing is platform-dependent — some platforms may not support all four hook points","Event extraction is heuristic-based (regex matching on tool output) — may miss or misinterpret events","Hook execution is synchronous — long-running hooks can block agent execution","No hook composition or dependency management — hooks are executed in registration order with no way to express dependencies"],"requires":["Platform support for hook registration (Claude Code, Gemini CLI, VS Code Copilot, Cursor, OpenCode, Codex CLI)","Hook implementation (TypeScript function with specific signature)"],"input_types":["hook lifecycle point (string: 'PreToolUse', 'PostToolUse', 'PreCompact', 'SessionStart')","event data (tool call, tool output, context state, session metadata)","hook handler (TypeScript function)"],"output_types":["hook response (allow/block tool call, filtered output, injected snapshot)","extracted events (list of semantic events from tool output)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mksglu--context-mode__cap_2","uri":"capability://memory.knowledge.session.continuity.through.event.capture.and.priority.tiered.snapshot.restoration","name":"session continuity through event capture and priority-tiered snapshot restoration","description":"Captures tool calls, code edits, and agent decisions into a SessionDB (persistent SQLite store) as timestamped events. When context window fills and compaction occurs, the PreCompact hook builds a priority-tiered snapshot (recent edits > active files > task state > resolved errors) that is restored at SessionStart, preserving working memory across context resets. Snapshots are serialized as structured directives that guide the agent to resume from the last known state without re-explaining context.","intents":["Resume multi-hour coding sessions without losing track of which files were being edited or what tasks are in progress","Preserve error resolution history so the agent doesn't repeat failed approaches after context compaction","Maintain task continuity when the LLM's context window fills and older messages are dropped","Reconstruct agent state from events for debugging or auditing long-running sessions"],"best_for":["Long-running AI coding sessions (2+ hours) where context window compaction is inevitable","Multi-file refactoring tasks that span multiple context windows","Teams needing session replay or audit trails for compliance or debugging"],"limitations":["Snapshot restoration adds ~100-300ms overhead per session start (SQLite query + snapshot serialization)","Priority-tiering heuristics are fixed (recent edits > active files > task state) — cannot be customized per use case","Snapshots capture state at compaction time; intermediate state between compaction and snapshot restoration is lost","No conflict resolution if agent state diverges from snapshot (e.g., user edits file while agent is working) — snapshot overwrites take precedence"],"requires":["SQLite 3.9+ (SessionDB storage)","Disk space for event log (rough estimate: 1 KB per tool call)","Hook system integration (PreCompact, SessionStart hooks must be registered with the MCP server)"],"input_types":["tool call events (from PostToolUse hook)","code edit events (from file system or agent directives)","task state (from agent planning or reasoning steps)"],"output_types":["snapshot directives (structured text describing state to restore)","event log (timestamped sequence of actions)","session metadata (start time, file count, task summary)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mksglu--context-mode__cap_3","uri":"capability://tool.use.integration.multi.platform.adapter.system.with.hook.based.integration","name":"multi-platform adapter system with hook-based integration","description":"Provides platform-specific adapters for Claude Code, Gemini CLI, VS Code Copilot, Cursor, OpenCode, and Codex CLI. Each adapter implements the MCP server protocol and registers hooks (PreToolUse, PostToolUse, PreCompact, SessionStart) that intercept agent execution at key lifecycle points. Hooks allow context-mode to filter tool output before it enters the context window, extract events for session continuity, and inject snapshots at session start without modifying the underlying AI platform.","intents":["Deploy context-mode across multiple AI coding platforms without rewriting integration code","Intercept tool calls and context updates at platform-specific lifecycle points","Inject session snapshots and directives into the agent's context at the right time in the conversation","Support platform-specific features (e.g., Claude Code plugins, VS Code extensions) while maintaining a unified core"],"best_for":["Teams using multiple AI coding platforms (Claude Code + Cursor + VS Code Copilot) and wanting consistent context optimization","Platform developers integrating context-mode into their own AI agents","Enterprises standardizing on a single context-optimization layer across heterogeneous tooling"],"limitations":["Each platform adapter requires platform-specific hook registration code; adding a new platform requires ~200-500 lines of adapter code","Hook timing is platform-dependent — some platforms (Claude Code) support PreCompact hooks, others (Cursor) may not, limiting feature parity","Adapters are tightly coupled to platform APIs; breaking changes in platform APIs require adapter updates","No automatic platform detection — users must explicitly specify which platform they're using or context-mode must probe for it at startup"],"requires":["MCP server protocol support in the target platform (Claude Code, Gemini CLI, VS Code Copilot, Cursor, OpenCode, Codex CLI)","Platform-specific configuration (e.g., .claude-plugin/plugin.json for Claude Code, VS Code extension manifest for Copilot)","Node.js 18+ (MCP server runtime)"],"input_types":["platform identifier (string: 'claude-code', 'cursor', 'copilot', etc.)","hook lifecycle events (tool calls, context updates, session starts)","platform-specific configuration (API keys, workspace paths)"],"output_types":["filtered tool output (reduced context)","hook responses (allow/block tool call, inject snapshot)","platform-specific directives (e.g., Claude Code plugin commands)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mksglu--context-mode__cap_4","uri":"capability://code.generation.editing.batch.code.execution.with.error.recovery.and.retry.logic","name":"batch code execution with error recovery and retry logic","description":"Executes multiple code snippets or files in sequence via ctx_batch_execute, with per-item error handling and optional retry logic. If one item fails, subsequent items continue executing (fail-fast disabled by default). Captures exit codes, stdout, and error messages for each item, allowing agents to identify which operations succeeded and which failed without stopping the entire batch. Useful for running test suites, migrations, or multi-step setup scripts where partial success is acceptable.","intents":["Run test suites and capture which tests pass/fail without stopping on first failure","Execute database migrations or setup scripts where some steps may fail but others should continue","Batch-process multiple files (e.g., linting, formatting) and report per-file results","Retry failed operations with exponential backoff without re-executing successful operations"],"best_for":["Agents running test suites or CI/CD-like workflows where partial success is meaningful","Teams with multi-step setup or migration scripts that need robust error handling","Developers debugging flaky tests or operations that benefit from retry logic"],"limitations":["Batch execution is sequential, not parallel — no performance benefit for independent operations","Retry logic is simple (exponential backoff) — no support for conditional retries or circuit breakers","Error recovery is per-item; no transaction semantics or rollback if a batch partially succeeds","No built-in timeout per item — long-running items can block the entire batch"],"requires":["Node.js 18+ (MCP server runtime)","Language runtimes for each code snippet (Python 3.9+, Node.js 18+, etc.)"],"input_types":["array of code snippets or file paths","retry configuration (max retries, backoff strategy)","per-item timeout (optional)"],"output_types":["array of results (one per input item)","per-item: exit code, stdout, stderr, retry count, duration"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mksglu--context-mode__cap_5","uri":"capability://code.generation.editing.file.aware.code.execution.with.automatic.dependency.resolution","name":"file-aware code execution with automatic dependency resolution","description":"Executes code from files (ctx_execute_file) with automatic dependency resolution and working directory context. Detects the file's language, resolves imports/requires, and executes in the file's directory so relative paths and local dependencies work correctly. Supports executing partial file ranges (e.g., a single function or test case) without running the entire file, useful for testing individual components without side effects from module-level code.","intents":["Execute individual functions or test cases from a file without running module-level setup code","Run scripts with correct working directory and local dependency resolution","Test code changes in context without extracting snippets or rewriting imports","Debug specific functions by executing them in isolation with their original file context"],"best_for":["Agents iterating on code changes and needing to test individual functions","Developers debugging specific test cases or functions without running full test suites","Teams with complex dependency graphs where relative imports and local modules are critical"],"limitations":["Partial file execution (single function) requires AST parsing to extract the function body — not supported for all languages","Working directory context is file-based; no support for monorepo-aware working directories or custom root paths","Dependency resolution is basic (looks for imports/requires in the file) — does not handle transitive dependencies or package manager metadata","No caching of parsed ASTs — each execution re-parses the file, adding ~10-50ms overhead per execution"],"requires":["File must exist on disk and be readable","Language runtime for the file's language (Python 3.9+, Node.js 18+, etc.)","For partial execution: tree-sitter or language-specific AST parser (adds ~50 MB to bundle size)"],"input_types":["file path (absolute or relative)","optional: line range (start, end) for partial execution","optional: function name for function-level execution"],"output_types":["stdout (filtered, intent-aware)","exit code (integer)","execution metadata (duration, working directory used)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mksglu--context-mode__cap_6","uri":"capability://memory.knowledge.content.indexing.and.incremental.knowledge.base.updates","name":"content indexing and incremental knowledge base updates","description":"Indexes arbitrary content (code files, documentation, API responses, logs) into the FTS5 knowledge base via ctx_index. Supports incremental updates — new content is added without re-indexing existing content. Automatically detects content type (code, markdown, JSON, plain text) and applies language-specific tokenization (e.g., camelCase splitting for code identifiers). Provides ctx_fetch_and_index for batch-indexing external content (GitHub issues, API docs, file trees) with automatic deduplication.","intents":["Build a searchable knowledge base from codebase, documentation, and external APIs","Incrementally add new content to the knowledge base without re-indexing everything","Batch-index external content (GitHub issues, API responses) and query it alongside local code","Automatically deduplicate content so the same file or API response isn't indexed twice"],"best_for":["Agents building context-aware code generation by indexing relevant docs and code patterns","Teams managing large codebases and wanting to search across code, docs, and external APIs","Long-running sessions where the knowledge base grows over time and needs incremental updates"],"limitations":["Content type detection is heuristic-based (file extension, first few lines) — may misclassify mixed-format files","Language-specific tokenization (camelCase splitting) is implemented for common languages (Python, JavaScript, Java) but not all","Deduplication is content-hash based; if the same content is indexed with different metadata (e.g., different file paths), it may be indexed twice","No automatic refresh — external content (GitHub issues, API docs) must be explicitly re-indexed if it changes"],"requires":["SQLite 3.9+ (FTS5 extension support)","Disk space for database (rough estimate: 1 MB per 10K lines of indexed code)","For external content: API credentials (GitHub token for GitHub issues, etc.)"],"input_types":["content (text, code, markdown, JSON)","content type (auto-detected or explicit: 'code', 'docs', 'api-response', 'log')","metadata (file path, source URL, timestamp)","external URLs (for ctx_fetch_and_index)"],"output_types":["indexing result (success/failure, items indexed, deduplication count)","knowledge base statistics (total items, database size)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mksglu--context-mode__cap_7","uri":"capability://data.processing.analysis.context.window.usage.diagnostics.and.optimization.recommendations","name":"context window usage diagnostics and optimization recommendations","description":"Provides ctx_stats and ctx_doctor tools that analyze context window usage and identify optimization opportunities. ctx_stats reports current session size (tokens, characters), breakdown by message type (code, conversation, tool output), and identifies the largest context consumers. ctx_doctor runs diagnostics (checks for unindexed large files, suggests content to move to knowledge base, identifies inefficient tool calls) and recommends optimizations (e.g., 'index this 50 KB file to save 49 KB context'). Helps agents and developers understand where context is being consumed and how to optimize.","intents":["Understand why the context window is filling up and which messages/files are consuming the most space","Get actionable recommendations for optimizing context usage (e.g., index this file, batch these tool calls)","Monitor context usage over time to identify trends and prevent unexpected compaction","Debug context-related issues (e.g., why did the agent forget a task after compaction?)"],"best_for":["Developers optimizing long-running AI coding sessions and wanting to understand context bottlenecks","Teams monitoring context usage across multiple sessions and looking for patterns","Agents that need to make context-aware decisions (e.g., decide whether to index a file or load it inline)"],"limitations":["Diagnostics are heuristic-based — recommendations may not be optimal for all use cases","Token counting is approximate (uses character-to-token ratio, not actual tokenizer) — may be off by 10-20%","No real-time monitoring — diagnostics are point-in-time snapshots, not continuous tracking","Recommendations are generic (e.g., 'index this file') — no context-specific optimization (e.g., 'index this file because it's referenced in 5 tool calls')"],"requires":["Active session with context-mode running","SessionDB populated with events (requires hook integration)"],"input_types":["optional: session ID (if multiple sessions are active)"],"output_types":["context statistics (total size, breakdown by message type, largest consumers)","diagnostic results (list of issues found)","optimization recommendations (list of suggested actions with estimated savings)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mksglu--context-mode__cap_8","uri":"capability://safety.moderation.security.policy.enforcement.with.configurable.execution.restrictions","name":"security policy enforcement with configurable execution restrictions","description":"Implements a security architecture that enforces configurable policies on code execution, file access, and tool usage. Policies are defined in a configuration file and include restrictions like 'allow only read-only file operations', 'block execution of shell scripts', 'restrict network access to whitelisted domains'. The PreToolUse hook intercepts tool calls and checks them against policies before execution, blocking disallowed operations. Supports role-based policies (e.g., 'agent' role has fewer permissions than 'user' role) and audit logging of all policy violations.","intents":["Prevent agents from executing dangerous operations (e.g., deleting files, running shell scripts with side effects)","Enforce organizational security policies (e.g., no network access, read-only file operations)","Audit all tool calls and policy violations for compliance and debugging","Support different permission levels for different agent roles or users"],"best_for":["Enterprise teams running AI agents in production and needing security guardrails","Organizations with compliance requirements (e.g., no network access, read-only operations)","Teams sharing AI agents across multiple users and needing role-based access control"],"limitations":["Policies are static (defined at startup) — no dynamic policy updates without restarting the MCP server","Policy enforcement is at the tool level (PreToolUse hook) — cannot prevent side effects that occur within the tool (e.g., a Python script that deletes files despite being blocked)","No fine-grained resource limits (e.g., CPU, memory) — policies are binary (allow/block), not quantitative","Audit logging is in-process (SQLite) — no integration with external audit systems or SIEM platforms"],"requires":["Policy configuration file (YAML or JSON format)","Role definitions (if using role-based policies)","SQLite 3.9+ (for audit logging)"],"input_types":["policy configuration (YAML/JSON)","tool call (from PreToolUse hook)","user/agent role (for role-based policies)"],"output_types":["policy decision (allow/block)","audit log entry (tool call, policy applied, decision, timestamp)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mksglu--context-mode__cap_9","uri":"capability://automation.workflow.upgrade.and.migration.utilities.for.context.mode.versions","name":"upgrade and migration utilities for context-mode versions","description":"Provides ctx_upgrade and upgrade CLI command that migrates context-mode installations and session databases to new versions. Handles schema migrations (e.g., adding new columns to SessionDB), data transformations (e.g., re-indexing content with new tokenization), and compatibility checks (e.g., verifying that the platform adapter supports the new version). Allows users to upgrade without losing session history or knowledge base content.","intents":["Upgrade context-mode to a new version without losing session history or knowledge base","Migrate session databases and configuration files to new schema versions","Check compatibility between context-mode version and platform adapter version","Rollback to previous version if upgrade fails or causes issues"],"best_for":["Teams running context-mode in production and needing to upgrade without downtime","Users with large session databases or knowledge bases that need careful migration","Organizations with strict change management processes requiring upgrade verification"],"limitations":["Rollback is manual — no automatic rollback if upgrade fails; users must restore from backups","Schema migrations are one-way — downgrading to previous versions may not be possible if schema changes are breaking","No zero-downtime upgrades — MCP server must be restarted, causing brief interruption to running sessions","Compatibility checks are basic (version number comparison) — no deep compatibility analysis"],"requires":["Backup of session database and configuration files (recommended before upgrade)","Node.js 18+ (MCP server runtime)"],"input_types":["target version (string, e.g., '1.2.0')","optional: backup directory (for manual backups)"],"output_types":["upgrade status (success/failure)","migration log (list of migrations applied)","compatibility report (version compatibility, breaking changes)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":36,"verified":false,"data_access_risk":"high","permissions":["Node.js 18+ (MCP server runtime)","Python 3.9+ (if executing Python code)","Go 1.18+, Rust 1.70+, Java 11+, or other language runtimes for respective language support","Unix-like shell (bash/zsh) for subprocess spawning; Windows requires WSL or native Windows subprocess API","SQLite 3.9+ (FTS5 extension support)","Disk space for database (rough estimate: 1 MB per 10K lines of indexed code)","Content to index must be provided as text or file paths; binary files are skipped","SQLite 3.9+ (for database integrity checks)","Platform support for hook registration (Claude Code, Gemini CLI, VS Code Copilot, Cursor, OpenCode, Codex CLI)","Hook implementation (TypeScript function with specific signature)"],"failure_modes":["Subprocess isolation adds ~50-200ms latency per execution depending on language startup time","Background processes spawned in subprocesses are not automatically tracked; requires explicit cleanup hooks","No built-in timeout enforcement — long-running code can block the MCP server unless wrapped with external timeout wrapper","Language-specific features (e.g., async/await in Node.js) require explicit runtime configuration per language","FTS5 BM25 ranking is lexical, not semantic — queries like 'how to authenticate' may miss conceptually similar code if keywords don't match","Indexing large codebases (>100K files) can consume 500 MB+ SQLite database; no built-in sharding or distributed indexing","Search results are limited to indexed content; real-time data (live API responses, streaming logs) requires explicit re-indexing","No vector embeddings — cannot perform semantic similarity search across code semantics, only keyword matching","Auto-remediation is limited to safe operations (e.g., removing orphaned sessions) — does not attempt to fix complex issues","Diagnostics are point-in-time snapshots — may not catch intermittent issues","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.35860653916494467,"quality":0.34,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.062Z","last_scraped_at":"2026-05-03T13:56:56.344Z","last_commit":"2026-05-03T12:22:52Z"},"community":{"stars":12155,"forks":842,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mksglu--context-mode","compare_url":"https://unfragile.ai/compare?artifact=mksglu--context-mode"}},"signature":"dAD0EBqH/LyXZTT7hbXqEe//52mJx4WbH5zlmrTiYmLH/Q7yYXT33HnUSNVDusEI5xmSpQJez3PNx6f3kxwVBw==","signedAt":"2026-06-22T10:47:11.362Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mksglu--context-mode","artifact":"https://unfragile.ai/mksglu--context-mode","verify":"https://unfragile.ai/api/v1/verify?slug=mksglu--context-mode","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}