prime-radiant-inc

Reverse engineering - multi-source product intelligence analysis with provenance tracking. Master methodology for all analysis agents.

Layer 1 intelligence source discovery - auto-detect available sources, search for public information, negotiate with user, produce inventory manifest

Layer 3 deep documentation methodology. Per-module behavioral specifications, external and behavioral integration contracts, behavior documentation, end-to-end user journey analysis. Transforms Layer 2 synthesis into implementable behavioral specifications.…

Layer 1 methodology for extracting behavioral intelligence from compiled binaries, bytecode archives, managed assemblies, and bundled applications. Covers artifact identification, string extraction strategy, decompilation workflows, provenance requirements,…

Layer 1 skill for community intelligence gathering. Search channels, extraction methodology, consensus analysis, version-aware behavioral changes, structural contamination guard. Loaded by the analyzer agent for community intelligence gathering.

Infrastructure skill for containerized target execution. Runtime detection, container lifecycle, security restrictions, interaction patterns.

Layer 1 skill for parsing machine-readable API contracts. OpenAPI/Swagger, GraphQL, Protobuf/gRPC, and JSON Schema detection, extraction, and behavioral claim generation. Loaded by the analyzer agent during Layer 1.

Public documentation extraction methodology. Search sequence (3 tiers), behavioral claim extraction rules, output structure, termination criteria, and gap analysis. Loaded by the analyzer agent for documentation research.

Use when executing a batch of TDD-sized tasks inside a running-an-iteration call — dispatches an implementer subagent per task following red-green-refactor discipline and returns per-task completion status.

Use when executing the next pending iteration from an iterative-development roadmap — picks the iteration, decomposes into code and evidence tasks, runs sentinel corpus baseline, dispatches implementing-tasks, runs impacted + sentinel scenarios, and updates…

Use when implementing a project with a large, comprehensive, or ambiguous spec — extracts requirements with proof obligations, defines a walking skeleton with its first journey scenario, then loops through audited sprints that continuously build a behavior…

Use when turning extracted requirements into a roadmap — selects the walking skeleton iteration with its first journey scenario, orders remaining work into follow-on iterations, and applies story splitting when ACs have different dependency profiles.

Use when starting an iterative-development run on human spec collateral — reads the spec, produces per-epic requirement files with proof obligations and behavior scenario cards with stable IDs.

Use when an iteration has just finished and you need to verify behavior evidence quality in three tiers — deep evidence for current stories, impacted behavior for touched scenarios, and sentinel corpus for high-value regression detection.

Use when designing, running, or diagnosing Serf tool-fluency experiments for a real built-in, plugin, or newly added tool. Covers probe manifests, model comparisons, semantic oracles, and interpreting fluency failures without ad-hoc transcript parsing.

Use when diagnosing a serf session, job, watch, or the session tree — investigating watch runaways (self-influence depth + runaway-fuse drops), dropped/coalesced deliveries, "how many times did X actually get called", stuck turns, or parent↔delegate↔observer…

Use when a surface parses untrusted or model-generated input — a wire protocol, tool-call JSON, upstream SSE, an HTTP body — or after a parser/dispatch bug, to fuzz that surface and turn any deterministic failure into a permanent, flake-guarded regression…

Use when a toil run has failed or produced unexpected results and you need to investigate what happened

Use when monitoring an in-progress toil run, checking if a run is stuck, or observing live workflow execution

Use when writing, editing, validating, or debugging toil workflow YAML — node/edge structure, ForEach, decision tags, expression syntax (${workflow_input.X} vs ${input.X}, supported node fields, required ! refs), or analyzing why a run misbehaved

Use for high-stakes review where you want multiple model providers reviewing the same artifact and critiquing each other. Shells out to installed coding-agent CLIs (claude, codex, gemini, pi, opencode by default; amp and droid available opt-in) to run…

Use when reviewing a diff, commit, branch, or implementation against a spec — dispatches two same-model reviewer subagents in parallel under a competitive scoring frame, then aggregates findings. Triggers on "review this", "PAR review", "adversarial review",…

Use when the user asks for a greeting - produces a friendly greeting

Use when starting any conversation - explains the kitchen-sink fixture plugin's capabilities

Use when working a shared ledger issue board — picking and claiming tasks, closing with evidence, breaking cycles, and reconciling contested state after sync.

Use when work spans sessions or agents and needs durable, verifiable state — starting multi-session or multi-agent work, dispatching subagent fleets, resuming after context death, handing off, tracking an investigation, running an issue board, picking…

Manage Linear issues. Use when asked about tasks, tickets, bugs, or Linear.

Use when writing or reviewing a Gauntlet story card. Establishes the calibration framing — outcomes, persona binding, observable acceptance criteria — and explicitly rules out wrong-shaped frames (Agile user stories, QA step-scripts, BDD Given/When/Then) so…

Use when asked for a demo, screencast, tutorial, walkthrough, or proof video of software actually running, when a reviewer needs to see a feature work rather than take your word for it, or when handing over any video artifact of app behavior

Use when sending, reading, or reacting to Slack messages, watching for bot events (mentions, DMs, reactions, thread replies), downloading Slack files, or provisioning/creating/deploying a new Slack bot — all from the command line via the slackline CLI.

Use when saving, correcting, or curating persistent memory — recording facts for future sessions, retracting wrong memories, end-of-session housekeeping, or deciding what deserves remembering.