skilldrop

Browse the catalogue → — all 56 skills, filterable by pack, tag, and model tier.

56 portable AI-agent skills for the deliverables knowledge workers actually ship — diagrams, design docs, ADRs, PRDs, runbooks, decks, decision logs, threat models, adversarial reviews — installable into Claude Code, Cursor, Kiro, Codex, GitHub Copilot, and any AI tool that accepts custom instructions, one skill or one role-based pack at a time:

npx skilldrop-cli install --pack product-manager     # or: solution-architect, dev-team,
npx skilldrop-cli list                               #     sre-oncall, stakeholder-comms, ai-engineering

Originally scoped to solution architects, now broadly useful to PMs, founders, consultants, engineering leaders, exec assistants — anyone who turns ideas into stakeholder-ready artifacts.

Every skill is a plain SKILL.md folder — the Agent Skills open standard Anthropic ratified — so the same folder runs unchanged in Claude.ai, Claude Code, the Claude Agent SDK, Cursor, Kiro, Codex, Copilot, Antigravity, and anything else that reads the format. Portable by construction: no runtime, no platform, no lock-in.

Why skilldrop over heavier agent platforms: every skill is a plain folder (SKILL.md + manifest.json + supporting files) and installs by copy, never transformation — what runs in your IDE is byte-identical to what's reviewed in this repo. The flat layout doubles as an open catalog contract: any repo shaped like this one is installable through the same CLI. Skills are opinionated — each ships a quality bar, anti-patterns, and acceptance evals, so the output is an artifact, not a vibe.

How skilldrop works

skilldrop runs two value streams, and nothing comes out of either until it passes a review gate. Both diagrams render on GitHub; the Mermaid sources live in docs/ for easy re-rendering.

Knowledge-work pipeline

Raw input becomes a stakeholder-ready artifact — and loops back through review until it's approved.

%%{init: {'theme':'base','themeVariables':{'fontFamily':'Segoe UI, Helvetica, Arial','fontSize':'15px','lineColor':'#9AA5B1'},'flowchart':{'curve':'basis','rankSpacing':70,'nodeSpacing':50,'padding':16}}}%%
flowchart LR
    classDef input  fill:#FFF4E0,stroke:#E8A93B,stroke-width:2px,color:#7A4E00,font-weight:bold;
    classDef gen    fill:#E8F0FE,stroke:#4C7DF0,stroke-width:1.5px,color:#1A3A8F;
    classDef review fill:#FDEAEA,stroke:#E05B5B,stroke-width:1.5px,color:#8A1F1F;
    classDef ship   fill:#E6F7EC,stroke:#34A853,stroke-width:2px,color:#0F6B33,font-weight:bold;

    IN(["Raw input<br/>notes · ticket · transcript"]):::input
    brief["Structure the brief"]:::gen
    gens["Draft the artifact"]:::gen
    crit["Review and refine"]:::review
    ART(["Stakeholder-ready<br/>artifact"]):::ship

    IN --> brief --> gens --> crit
    crit -- "revise" --> brief
    crit == "approved" ==> ART

Code: implement and verify

A feature spec becomes shippable code through a self-correcting loop — generate, adversarially challenge, close the gaps, re-check — until the review is clean or a 3-round cap is hit. This is the feature-implement-loop skill.

%%{init: {'theme':'base','themeVariables':{'fontFamily':'Segoe UI, Helvetica, Arial','fontSize':'15px','lineColor':'#9AA5B1'},'flowchart':{'curve':'basis','rankSpacing':70,'nodeSpacing':50,'padding':16}}}%%
flowchart LR
    classDef input  fill:#FFF4E0,stroke:#E8A93B,stroke-width:2px,color:#7A4E00,font-weight:bold;
    classDef gen    fill:#E8F0FE,stroke:#4C7DF0,stroke-width:1.5px,color:#1A3A8F;
    classDef review fill:#FDEAEA,stroke:#E05B5B,stroke-width:1.5px,color:#8A1F1F;
    classDef gate   fill:#FBE3A2,stroke:#D9971E,stroke-width:2px,color:#6B4500,font-weight:bold;
    classDef ship   fill:#E6F7EC,stroke:#34A853,stroke-width:2px,color:#0F6B33,font-weight:bold;

    IN(["Feature / story<br/>description + acceptance criteria"]):::input
    gC["Write and update<br/>code and tests"]:::gen
    revC["Challenge the code"]:::review
    gate{"Gaps found?"}:::gate
    SHIP(["Feature ready<br/>to be shipped"]):::ship

    IN --> gC --> revC --> gate
    gate -- "yes (up to 3 rounds)" --> gC
    gate == "no" ==> SHIP

Skills in this repo

Pipeline glue

Planning & delivery

Skills for the SDLC steps around the code itself — turning raw requirements into shippable, testable units of work and tracking them to release. (feature-implement-loop under Dev workflow is the natural downstream of these.)

Product strategy

Skills for the direction-setting layer above any single feature — testing whether a product idea deserves a team, analyzing a market position, and turning strategy into aligned goals. (business-case under Planning & delivery is the natural costed follow-on.)

Dev workflow

Diagrams

Documentation

Agent engineering

Designing the agentic systems themselves — the loops, orchestrations, and budgets that turn "prompting an agent" into infrastructure a team can run unattended. (feature-implement-loop under Dev workflow is the in-repo worked example of a supervised loop; llm-eval-harness below builds the verifier's quality gate.)

| agents-md-generator | Generate or audit the AGENTS.md that Claude Code, Codex, Cursor, Copilot, Kiro and Antigravity all read before your prompt. Built around one rule: every command traces to evidence in the repo — a CI step, a Makefile target, a package.json script — or it appears as [missing: no test command in CI or manifests] rather than a guess, because an invented command is a fault the agent will trust over the filesystem. Convention lines must pass a counterfactual test (without this line, would a competent agent do the wrong thing?); virtue instructions — "clean", "best practice", "thorough" — are stripped, not softened, since they spend context and change no behaviour. Names forbidden actions explicitly, the section most files omit. Hard ~150-line cap when generating, a warning when auditing, because an existing file may be long for good reason. Audit mode emits a diff, never a silent rewrite. Satellites (CLAUDE.md, .github/copilot-instructions.md) are opt-in and delta-only — a satellite that repeats AGENTS.md is guaranteed drift. |

AI adoption & observability