Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add sam-dumont/claude-skills --skill outcome-engineeringgit clone --depth 1 https://github.com/sam-dumont/claude-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/sam-dumont/claude-skills/outcome-engineering)<a href="https://agentmods.dev/skills/sam-dumont/claude-skills/outcome-engineering"><img src="https://agentmods.dev/badge/skills/sam-dumont/claude-skills/outcome-engineering/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/sam-dumont/claude-skills/outcome-engineering"><img src="https://agentmods.dev/badge/skills/sam-dumont/claude-skills/outcome-engineering.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00084 | $0.02181 |
| Opus 5 | $0.00042 | $0.01091 |
| Sonnet 5 | $0.00017 | $0.00436 |
| Haiku 4.5 | $0.00008 | $0.00218 |
Grade A, and why
outcome-engineering scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 235 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Outcome Engineering (o16g)
Based on the o16g manifesto by Cory Ondrejka. Software engineering is outcome delivery, not code production.
| Task Thinking | Outcome Thinking |
|---|---|
| "Build feature X" | "Enable users to achieve Y, verified by Z" |
| "Fix bug B" | "Restore correct behavior, prevent recurrence" |
| "Refactor module M" | "Reduce change cost from X to Y, measured by Z" |
| "Add technology T" | "Solve problem P with measurable improvement" |
Phase 1: The Outcome Frame
Before starting significant work, produce an Outcome Frame. This takes ~3 minutes, not 30. Skip it for trivial tasks (typo fixes, obvious single-line bugs).
1. Define the Outcome (P01: Human Intent)
State the measurable change being delivered. Not what you'll build — what will be different when you're done.
Anti-pattern: "Build a REST API for user management" Outcome-first: "External services can create, read, update, and delete user records over HTTP, with <200ms p95 latency"
Ask: "What is measurably different when this is done?"
2. Define Verification (P02: Verified Reality)
How will you PROVE it worked? Not "it looks right" — observable, repeatable evidence.
Verification must be concrete:
- Tests that pass/fail (unit, integration, e2e)
- Observable behavior (API returns X, UI shows Y)
- Metrics that move (latency drops, error rate decreases)
- User action that succeeds (can complete workflow Z)
Anti-pattern: "Test it manually and make sure it works" Outcome-first: "Integration test hits /users CRUD endpoints, asserts 2xx responses and correct payloads. Load test confirms p95 <200ms at 100 RPS."
Ask: "What specific evidence proves this outcome was achieved?"
3. Justify the Cost (P04: Backlog Dead)
Is this worth the compute/effort? Not everything that could be built should be built.
Consider:
- Does this directly serve a user need or business goal?
- What's the cost of NOT doing this?
- Is there a simpler way to achieve 80% of the outcome?
- Are we building or should we buy/integrate?
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 235 lines · 84 tokens per session scan A 89af7c2a62e9
outcome-engineering is a skill published in the GitHub repository sam-dumont/claude-skills (39 stars, last pushed 6mo ago), licensed MIT. It adds 84 tokens to every session and 2,181 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…