ai-engineering

Plugin ai-engineering from marketplace skilldrop in sananthanarayan/skilldrop. Commands read from the repo's manifest — 2 days ago.

Skills (21)

• agent-adoption-stage

  Place an engineering team on the agentic-coding adoption ladder (0 gated → 4 intent-steered) using observables like agents-in-flight per engineer and who writes the code, name the one bottleneck gating the next step, prescribe the single next unlock, and state which current guardrail must change. Us

• agent-budget

  Write the spend spec for an agentic workflow — per-stage model tiers, token caps with hard abort rules, a graceful-degradation order, and cost-per-outcome as the governing metric. Use when the user asks what an agent loop or multi-agent workflow should be allowed to spend, wants token/cost budgets a

• agent-config-review

  Review a coding agent's configuration for access broader than the work needs and give a verdict — SAFE AS CONFIGURED, TIGHTEN BEFORE USE or DO NOT USE — reading what actually wins across every scope (Claude Code user, project, local and managed settings; permission allow/deny/ask lists, defaultMode,

• agent-incident-response

  Plan the first hour after something went wrong with an AI coding agent or its tooling — a leaked or stolen API key, a destructive command (a dropped table, a force-push), a runaway bill, a malicious MCP server or skill, an agent prompt-injected into sending data out. It answers the person's actual q

• agent-loop-design

  Design a supervised agent loop — the generate→verify→gate cycle, observable exit criteria, hard iteration cap, human gates at irreversible steps, and failure routes — as a loop spec a team can implement in any agent harness. Use when the user wants to automate a recurring task with an AI agent loop,

• agent-threat-model

  Threat-model an AI agent deployment against the lethal trifecta — private data, untrusted content, and an exfiltration vector — producing a per-capability matrix, a named architectural fix for every unsafe path, and a pre-launch checklist. Use before shipping an agent, when reviewing MCP server or t

• ai-adoption-rollout

  Plan the human rollout of an AI tool or workflow to an organisation — sized cohorts, enablement per cohort, named champions, a comms plan that states what will not change, and an adoption gate every wave must pass before the next one opens. Use when a tool has been chosen and the question is how it

• ai-readiness-assessment

  Score an organisation's or team's readiness to adopt AI tooling across six gating dimensions — data, tooling, skills, governance, process, and culture — with an evidence line per score and the blocking gaps ranked by what stops adoption first. Use when the user asks "are we ready for AI", wants an A

• ai-usage-policy

  Draft an organisation's AI use policy or staff guidance from what it has told you about its people, tools, data and worries — every "don't" paired with what to do instead (or who decides, when no approved way exists yet), what to do if data has already gone into a tool, and no claim about a tool, li

• ai-use-case-triage

  Turn a pile of candidate AI use cases into a ranked portfolio — each scored on value, feasibility, and risk with the weights shown, a recommended first slice that can prove or kill the thesis, and an explicit not-yet list with the condition that would promote each entry. Use when a team has more AI

• brief-intake

  Turn raw mess — a Slack thread, meeting transcript, Jira ticket, half-written Notion page, email chain, or paragraph of free-form context — into a structured brief that the downstream skill (adr-generator, design-doc, runbook-generator, exec-summary, slide-outliner) can consume without further intak

• context-graph

  Map the decisions behind a codebase from its ADRs, RFCs and design records — each record's status and date, what supersedes or amends what, which records cite which, the files and folders each decision governs, and with git the commits that implemented it and the commits that changed each decision's

• council-review

  Convene a council of distinct senior-engineer personas (Architect, Security, Operator/SRE, Pragmatist, User-Advocate, plus optional bench seats) to deliberate a decision, design, or code change — each takes an independent stance, the genuine disagreements are surfaced as named cruxes, and a Chair is

• doc-critique

  Review an existing document — ADR, design doc, runbook, exec summary, tech-comparison matrix, deck outline, decision log — and produce a structured critique against the same quality bars the skilldrop generators enforce. Categorizes findings by severity (blocker / major / minor / nit), points to the

• judge-audit

  Audit whether an LLM-as-judge, or a panel of judges, can be trusted for the decision its scores drive, before anyone acts on them — above all whether the judge errs differently on the variants being compared (prompt v1 vs v2, model A vs B, short vs long inputs), plus chance-corrected and weighted ag

• llm-eval-harness

  Design an evaluation harness for an LLM-powered feature — a versioned golden set (representative + adversarial + regression cases), the cheapest adequate grading method per case, a metric with a pre-set pass bar and regression gate, and a failure taxonomy that targets iteration. Use when the user is

• mcp-pairing

  Recommend which MCP servers to connect for a loop, workflow or task, stage by stage, with the narrowest access that works — what outside context or action each stage needs, the server that provides it (only ones the user named or the vendor officially publishes), the scopes to grant (read-only by de

• output-hygiene

  Review agent-written text before it ships and strip the machine artifacts it carries — invisible Unicode, non-breaking spaces, homoglyphs, harness-added provenance trailers, trailing chat closers — then flag the prose tells a script cannot catch, each quoted with a rewrite. Use when the user wants t

• ship-a-draft

  Take raw mess to a stakeholder-ready artifact by wrapping any skilldrop generator in the two passes either side of it — structured intake before, critique and machine-residue scrub after. Use when the user has notes, a transcript, or a ticket and wants a finished document rather than a first draft.

• ship-agent

  Take an AI agent from an idea to a launch a person signed: the loop spec with its exit criteria and cap, the spend caps per stage, a threat model where every lethal-trifecta path has an architectural fix, an eval harness with its pass bar set before the first run, and a review of the real agent conf

• subagent-design

  Decompose a task into an orchestrator and subagents — one-mission role cards with typed output contracts, a topology (pipeline / parallel / judge panel) chosen with a reason, context-isolation rationale, and a verification stage that is never a generator. Use when the user wants to fan work out acro

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