vision-alignment-foresight

vision-alignment-foresight is a skill for Claude Code from Arenukvern/skill_steward. It costs 80 tokens per session (1,349 once invoked), scanned A, original, MIT.

A framework for checking whether a product, repository, skill, plugin, or architecture still matches its purpose, implementation, evidence, and future direction.

In plain words
What is it for?
Use it to assess strategic fit, critique roadmaps, compare vision with real application behavior, and identify explicit, inferred, and unknown assumptions.
Why use it?
It exposes gaps between what a project says it is, what it actually does, and where it is likely to go.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the skill-steward plugin — 15 skills shipped together

Good fit Use it to assess strategic fit, critique roadmaps, compare vision with real application behavior, and identify explicit, inferred, and unknown assumptions.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/arenukvern/skill_steward/vision-alignment-foresight
Install

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.

Any agent
npx skills add Arenukvern/skill_steward --skill vision-alignment-foresight
Clone the repo
git clone --depth 1 https://github.com/Arenukvern/skill_steward

Made for: Claude Code.

Or install skill-steward, the plugin that ships this one along with the rest of its 15 skills.

Wrote 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.

agentmods badge for vision-alignment-foresight

README.md
[![agentmods](https://agentmods.dev/badge/skills/arenukvern/skill_steward/vision-alignment-foresight/github.svg)](https://agentmods.dev/skills/arenukvern/skill_steward/vision-alignment-foresight)
Your own site
<a href="https://agentmods.dev/skills/arenukvern/skill_steward/vision-alignment-foresight"><img src="https://agentmods.dev/badge/skills/arenukvern/skill_steward/vision-alignment-foresight/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.

agentmods 80×15 button for vision-alignment-foresight

Your own site · 80×15
<a href="https://agentmods.dev/skills/arenukvern/skill_steward/vision-alignment-foresight"><img src="https://agentmods.dev/badge/skills/arenukvern/skill_steward/vision-alignment-foresight.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 80 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,349 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00080 $0.01349
Opus 5 $0.00040 $0.00674
Sonnet 5 $0.00016 $0.00270
Haiku 4.5 $0.00008 $0.00135

Measured 9d ago against content hash 7b265a87afad, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

vision-alignment-foresight 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 9d 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.

skills/vision-alignment-foresight/SKILL.md · 139 lines

How it starts

The opening of the file, as written. The whole thing — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Vision Alignment Foresight

Test whether a vision is still worth building, not just whether it sounds coherent.

Use this skill to connect four things that often drift apart: stated intent, real implementation, observed evidence, and future direction.

Trigger Examples

  • Should trigger: "Analyze this vision against the app and future agent direction."
  • Should trigger: "Does our repo strategy still make sense given current usage and platform changes?"
  • Should trigger: "Criticize this roadmap and predict where it will fail."
  • Should not trigger: "Write an ADR for this accepted decision." Use repository-governance-lifecycle.
  • Should not trigger: "Run a broad multi-agent critique only." Use mixture-of-experts.

Workflow

  1. Name the intent.

    • Restate the human goal in one or two sentences.
    • Separate the durable purpose from the proposed implementation.
    • Mark what is explicit, inferred, and unknown.
  2. Map the application reality.

    • Read the North Star, ADRs, FAQs, skill docs, config, harness entrypoints, tests, and release notes that define current behavior.
    • Identify the actual user, maintainer, agent, and runtime surfaces.
    • Record any contradiction between docs, code, config, and user behavior.
  3. Collect evidence.

    • Prefer concrete signals: usage logs, eval results, issue/PR history, support questions, benchmark outcomes, adoption friction, failing tests, repeated manual workflows, and rollback history.
    • If future direction matters, do current research using primary sources where possible.
    • Label every major claim as observed, researched, inferred, or speculative.
  4. Run critical lenses.

    • Product/user lens: who benefits, who pays the complexity cost, what workflow improves?
    • Engineering lens: what must be true in code, tests, data, deploys, and interfaces?
    • Agent experience lens: can another agent discover, execute, verify, and recover without hidden context?
    • Maintenance lens: what will decay, fork, or become impossible to support?
    • Ecosystem/future lens: does the plan align with platform movement, standards, and likely integration paths?
    • Adversarial lens: what evidence would prove the vision wrong?

Read the full file on GitHub · 139 lines

Files

What ships with it

4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

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.

  1. 9d ago First seen · 139 lines · 80 tokens per session scan A 7b265a87afad

Subscribe to this mod's changes

vision-alignment-foresight is a skill published in the GitHub repository Arenukvern/skill_steward (11 stars, last pushed 24d ago), licensed MIT. It adds 80 tokens to every session and 1,349 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.