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Found 12 Skills
Bounded, evidence-driven Loop Engineering for Codex and Claude Code. Use to design, run, resume, or audit a coding loop that requires repeated implementation and verification, multi-agent routing, checkpoint recovery, measurable improvement, deterministic budgets, or explicit stop conditions.
Drive an evidence-driven, iterative product+engineering spec process that produces a full PRD + technical spec (often as SPEC.md). Use when scoping a feature or product surface area end-to-end; defining requirements; researching external/internal prior art; mapping current system behavior; comparing design options; making 1-way-door decisions; planning phases; and maintaining a live Decision Log + Open Questions backlog. Triggers: spec, PRD, proposal, technical spec, RFC, scope this, design doc, end-to-end requirements, phase plan, tradeoffs, open questions.
Optimize Rust libraries with criterion benchmarks, feature-flag matrices, cargo bench workflows, dependency gating, regression baselines, and evidence-driven API performance tradeoffs. Use when adding or reviewing Rust library benchmarks, cargo features, optional dependencies, or performance claims before publishing.
Hermes-native AIOps agent for evidence-driven incident response, approval-gated remediation, and runbook learning
Performs a white-box security review of Atlassian Forge apps using structured, Forge-specific security rules and evidence-driven reporting. Use when the user asks for a Forge security review, security audit, vuln assessment, pentest-style code review, authz review, tenant isolation analysis, web trigger hardening, or static analysis execution for a Forge app.
Run bounded, evidence-driven training research through W&B Launch: assess project readiness, establish launchable code and queue capacity, smoke-test real jobs, execute serial trials, compare metrics, and persist resumable research state. Use when a coding agent is asked to autonomously test training hypotheses or tune a real W&B-tracked workload.
Evidence-driven investigation for network, streaming, and protocol-layer bugs. Use when debugging connection resets (ECONNRESET, HTTP/2 RST_STREAM, INTERNAL_ERROR), SSE or long-polling stalls, fixed-time connection drops, CDN/proxy/CGNAT idle timeouts, or any incident where symptoms do not match the obvious cause. Applies falsification-first methodology — layered isolation experiments to pin down the responsible network layer, env-gated runtime instrumentation for non-invasive observation, and counter-review agent teams to challenge single-cause assumptions. Strongly trigger on "socket closed unexpectedly", "stream interrupted", "ECONNRESET", "HTTP/2 INTERNAL_ERROR", "fails after N seconds", "works sometimes but not always", "upstream silent for X seconds", or any scenario where the investigator might jump to conclusions before evidence. Generalizes to any multi-layer system investigation where assumption-first thinking is the failure mode.
Author and optionally direct-launch a two-node loop-graph run that compares feasible technical approaches with open-source evidence, primary research, and controlled experiments before selecting one. Use when a decision needs several research and evaluation rounds with durable evidence and independent audit. Not for implementing an already chosen requirement, code cleanup, or a quick literature summary. Do not execute or resume generated runtime node files from this authoring skill.
Discover and discuss new capabilities for a project, one evidence-grounded idea at a time, with persistent exploration history.
Turn papers, study notes, and preliminary data into decision-grade research decks for lab meetings, PI review, grants, and seminars—with faithful numbers, evidence chains, and caveats. Use when the user asks for a research deck, lab meeting slides, study summary deck, academic review deck, or paper-to-slides for a faithful talk (not stylized social image decks). Create research decks with AI-native design and the highest aesthetic standards, powered by the K2F format and engine. Purpose-built for AI agents, K2F lets them focus on semantic content and design intent while automatically handling layout, typography, and formatting complexity.
Discover evidence-backed ways an existing project could serve more real contexts while preserving necessary specificity. Use only when the user explicitly invokes $set-theory-for-projects.
A step-by-step problem-solving loop (classify the ask, define done, gather evidence, decide, act surgically, verify by observation, report outcome-first). Use when the user says "/fable-method", "use the fable method", or "approach this like Fable", or proactively when starting any multi-step task that no task-specific skill covers. Subcommands - plan (stop after the plan), audit (grade finished work against the loop), report (rewrite an answer outcome-first).