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 matteoroversi/lookward --skill lw-forgegit clone --depth 1 https://github.com/matteoroversi/lookwardWrote 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/matteoroversi/lookward/lw-forge)<a href="https://agentmods.dev/skills/matteoroversi/lookward/lw-forge"><img src="https://agentmods.dev/badge/skills/matteoroversi/lookward/lw-forge/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/matteoroversi/lookward/lw-forge"><img src="https://agentmods.dev/badge/skills/matteoroversi/lookward/lw-forge.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 | $0.00065 | $0.01422 |
| Opus 5 | $0.00032 | $0.00711 |
| Sonnet 5 | $0.00013 | $0.00284 |
| Haiku 4.5 | $0.00006 | $0.00142 |
Grade A, and why
lw-forge 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 — 45 lines — stays where its author put it; the contents beside it link to each section on GitHub.
lw-forge
The factory — it agentifies the kinetic layer. Once the ontology means something, lw-forge turns its actions into skills/agents/workflows and its actors into the agents that run them. It reads the layers — semantic (objects + links), kinetic (actions + actors), dynamic (governance) — and the will on top (intent + direction), and proposes bespoke tooling fitted to this world model. Loads ontology/contract.md. The base set stays minimal; the surface grows by forging. Everything forged is proposed (code + manifest) for human review before it runs.
What it forges
- The kinetic-execution family — skills, agents, and workflows (the three execution primitives). This is the payoff: each
actioninontology.mdbecomes an executable. An action is already a typed contract in the contract —parameters+rules(the closed verb catalog) +submission_criteria+side_effects+execution: auto | confirm.lw-forgecompiles it:
- Skills (the primary output) — one action the subject repeats (prep, write, brief, assess…). Output: a new
skills/<name>/SKILL.md. The action'sexecution: auto | confirmflag carries over, so the same definition serves a human invoking it and an agent calling it as a tool — governed identically. - Agents — bounded, independent, or parallel workers a skill spawns (a reviewer, a researcher, a verifier), or the agentified form of an actor in the ontology. Forged when a skill needs silent heavy lifting it shouldn't do in the user's conversation.
- Workflows — deterministic orchestrations of skills/agents for a recurring multi-step job (a sequence of actions along the will's loop). Forge proposes one when the signal shows a repeated sequence (A→B→C) or a fan-out worth running deterministically — only when volume/recurrence demands it, never by default. Propose-don't-apply holds: the workflow is reviewed before it runs, and any write goes through the action write-back (append-only to
edits/, human-gated).
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 · 45 lines · 65 tokens per session scan A d765dc9260e4
lw-forge is a skill published in the GitHub repository matteoroversi/lookward (7 stars, last pushed 2mo ago), licensed MIT. It adds 65 tokens to every session and 1,422 once invoked, about $0.0003 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 2026-08-31.
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…