Run three static audits covering architecture alignment, threats, and MCP server policies, permissions, and dependencies. Store timestamped results, track changes across runs, and fail scheduled CI jobs when findings reach a chosen severity.
Find candlestick and chart patterns in previously ingested market data. Classify detections by reversal or continuation, bullish or bearish direction, and reliability score, then rank and store them for later reference.
Create TypeScript scaffolding for a domain aggregate, a group of objects governed by shared business rules. Generate its main entity, value objects, repository interface, domain events, and unit test stubs inside an existing context.
Extract classes, functions, modules, and their relationships from source code or documentation into AgentDB. Build a knowledge graph, a map of connected entities, to understand code structure, dependencies, and potential change impacts.
Define reusable reasoning and decision patterns for agents, embed them in executable workflows, and review their performance. Share successful approaches, including conditional decision trees, stepwise problem solving, analogies, and evaluation from multiple perspectives.
Capture lessons from completed tasks, train patterns from explicit examples, and consolidate accumulated learning into long-term storage. Use SONA and MicroLoRA adaptation tools for individual domains, then verify results in the appropriate stores.
Track objectives across multiple conversations by defining milestones, saving progress and findings, and planning each session. Review completion criteria and flag schedule risks, scope changes, or dependencies that may require a different approach.
Verify the witness chain, a linked record of evidence, for a Cognitum Seed device. Check for missing recording periods and broken hash links, report an integrity score, and save detected gaps for auditing.
Organize Cognitum Seed devices into fleets, or device groups. Create and list fleets, add or remove devices, and delete fleets through dedicated commands, with explicit confirmation required before deletion.
Generate a read-only security report on threats involving MCP connections and operations. It groups findings by threat, reports the highest severity, and supports table or JSON output with a configurable severity threshold for failure.
Register a Cognitum Seed device at a supplied endpoint or its default USB Ethernet address. The workflow establishes an agent connection, retrieves device identity, assigns initial trust, optionally pairs the device, and records registration.
Inspect evolved prompt configurations, validate their structure, and view the system prompts they produce. You can also classify failures in run transcripts to support reviews before adopting or changing an agent policy.
Compare a saved repository audit with a fresh audit in one command. Receive a structured report showing structural similarity, with an alert and exit code when similarity falls below your chosen threshold.
Compare two JSON records describing agent setups and their scores. Receive an overall similarity score with component details to help assess reuse, compare template candidates, identify overlapping work, or check how much a setup has changed.
Investigate complex topics using web sources, stored knowledge, and relevant code. Break questions into smaller parts, compare evidence, and produce a structured report with confidence assessments, unresolved questions, and next steps.
Analyze Git changes before submitting or merging a pull request. Assess risk and complexity, classify changes, suggest reviewer roles, and identify files needing closer inspection using diff statistics and file-level risk analysis.
Create agents that learn from interactions and adapt their behavior over time. Monitor learning progress, review efficiency and accuracy metrics, trigger adjustments from feedback, and share acquired knowledge with other agents through dedicated tools.
Experiment with the policies that govern an AI agent, such as planning, memory, and tool use. Test one policy change per variant in a sandbox, an isolated environment, and promote only candidates with measured improvements.
Manage Git branches, resolve conflicts, and coordinate pull requests, which propose code changes. Check change risks and automated test status before merging, and inspect metrics such as merge frequency and review time.
Check federation health, the connectivity between agents in separate installations. Summarize connected peers, trust levels and scores, active sessions, message exchanges, personal information redactions, and threat detections, with a search of stored federation history.
Evaluate and improve the policy instructions that guide an AI agent using scored coding tasks. Run a learning cycle to compare candidates, or use the default dry run to inspect tasks and estimated costs without model calls.
Generate and update API or project documentation, and flag inconsistencies between existing docs and current code. Schedule recurring maintenance and save the documentation approach for reuse in later work.
Manage firmware updates across IoT device fleets through staged rollouts. Check a small initial group for anomalies before expanding deployment, inspect rollout status, and roll back updates with explicit user approval for device changes.
Turn research findings stored in memory into structured reports with evidence ratings, source references, and practical recommendations. Compare conflicting claims, identify unresolved questions, and explain limitations so accumulated research can support decisions and next steps.
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