measuring-ai-proficiency: Skill for Claude Code

.claude/skills/intent-framed-agent/SKILL.md

intent-framed-agent is a skill for Claude Code from pskoett/measuring-ai-proficiency. It costs 54 tokens per session (1,541 once invoked), scanned A, original, MIT.

A session framework that records the intended outcome before a coding agent starts substantial implementation work, then checks for scope drift and records how the work ended.

In plain words
What is it for?
Use it when moving from planning into a multi-step coding task. It is not intended for research, planning-only conversations, documentation-only work, or trivial edits.
Why use it?
It makes implicit goals explicit, so changes to code, refactors, features, and bug fixes are less likely to move beyond the agreed scope.

Skill for Claude Code

Written for Claude Code: installed under .claude/.

This is pskoett/measuring-ai-proficiency's own configuration. It tells Claude Code how to work on measuring-ai-proficiency itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything measuring-ai-proficiency configures →

Reuse

Borrowing it

Nothing to install: this file belongs to pskoett/measuring-ai-proficiency. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/pskoett/measuring-ai-proficiency/main/.claude/skills/intent-framed-agent/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/pskoett/measuring-ai-proficiency

Made for: Claude Code.

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 intent-framed-agent

README.md
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Your own site
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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.

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Your own site · 80×15
<a href="https://agentmods.dev/skills/pskoett/measuring-ai-proficiency/intent-framed-agent"><img src="https://agentmods.dev/badge/skills/pskoett/measuring-ai-proficiency/intent-framed-agent.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 54 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,541 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 $0.00054 $0.01541
Opus 5.5 $0.00022 $0.00616
Sonnet 5.5 $0.00011 $0.00308
Haiku 4.5 $0.00005 $0.00154

Measured 2mo ago against content hash 51739ca42971, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

intent-framed-agent 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 2mo 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.

.claude/skills/intent-framed-agent/SKILL.md · 208 lines

How it starts

The opening of the file, as written. The whole thing — 208 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Intent Framed Agent

Install

npx skills add pskoett/pskoett-ai-skills
npx skills add pskoett/pskoett-ai-skills/skills/intent-framed-agent

Purpose

This skill turns implicit intent into an explicit, trackable artifact at the moment execution starts. It creates a lightweight intent contract, watches for scope drift while work is in progress, and closes each intent with a short resolution record.

Scope (Important)

Use this skill for coding tasks only. It is designed for implementation work that changes executable code.

Do not use it for general-agent activities such as:

  • broad research
  • planning-only conversations
  • documentation-only work
  • operational/admin tasks with no coding implementation

For trivial edits (for example, simple renames or typo fixes), skip the full intent frame.

Trigger

Activate at the planning-to-execution transition for non-trivial coding work.

Common cues:

  • User says: "go ahead", "implement this", "let's start building"
  • Agent is about to move from discussion into code changes

Workflow

Phase 1: Intent Capture

At execution start, emit:

## Intent Frame #N

**Outcome:** [One sentence. What does done look like?]
**Approach:** [How we will implement it. Key decisions.]
**Constraints:** [Out-of-scope boundaries.]
**Success criteria:** [How we verify completion.]
**Estimated complexity:** [Small / Medium / Large]

Rules:

  • Keep each field to 1-2 sentences.
  • Ask for confirmation before coding:
    • Does this capture what we are doing? Anything to adjust before I start?
  • Do not proceed until the user confirms or adjusts.

Phase 2: Intent Monitor

During execution, monitor for drift at natural boundaries:

  • before touching a new area/file
  • before starting a new logical work unit
  • when current action feels tangential

Drift examples:

  • work outside stated scope
  • approach changes with no explicit pivot
  • new features/refactors outside constraints
  • solving a different problem than the stated outcome

Read the full file on GitHub · 208 lines

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. 2mo ago First seen · 208 lines · 54 tokens per session scan A 51739ca42971

Subscribe to this mod's changes

intent-framed-agent is a skill published in the GitHub repository pskoett/measuring-ai-proficiency (13 stars, last pushed 3mo ago), licensed MIT. It adds 54 tokens to every session and 1,541 once invoked, about $0.0002 per session on Opus 5.5. 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.

Related

intent-framed-agent compared with skills from other repositories

intent-framed-agent next to 6 skills from other repositories
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intent-framed-agentpskoett/measuring-ai-proficiencySkill133mo agoMITA54Claude Code
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