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.
curl -O https://raw.githubusercontent.com/pskoett/measuring-ai-proficiency/main/.claude/skills/intent-framed-agent/SKILL.mdgit clone --depth 1 https://github.com/pskoett/measuring-ai-proficiencyWrote 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/pskoett/measuring-ai-proficiency/intent-framed-agent)<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/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/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>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 | $0.00054 | $0.01541 |
| Opus 5 | $0.00027 | $0.00771 |
| Sonnet 5 | $0.00011 | $0.00308 |
| Haiku 4.5 | $0.00005 | $0.00154 |
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 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 — 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
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 · 208 lines · 54 tokens per session scan A 51739ca42971
intent-framed-agent is a skill published in the GitHub repository pskoett/measuring-ai-proficiency (11 stars, last pushed 1mo ago), licensed MIT. It adds 54 tokens to every session and 1,541 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.
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