human-intent-audit

human-intent-audit is a skill for Codex from ConnorRX56/presentation-delivery-skills. It costs 77 tokens per session (840 once invoked), scanned A, original, MIT.

An audit method that connects a person's plain-language goal to checks a computer can perform and evidence that a reviewer can understand. It keeps the original meaning of requirements such as “same,” “complete,” or “safe” visible during review.

In plain words
What is it for?
Use it during audits, reviews, quality checks, compliance or security assessments, code and architecture reviews, visual comparisons, and data validation.
Why use it?
Automated checks can pass while the actual user goal is still unmet. This helps reveal missing links between requirements, system behavior, and the final result people see or use.

Skill for Codex

Written for Codex: agents/openai.yaml present.

Good fit Use it during audits, reviews, quality checks, compliance or security assessments, code and architecture reviews, visual comparisons, and data validation.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/connorrx56/presentation-delivery-skills/human-intent-audit
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 ConnorRX56/presentation-delivery-skills --skill human-intent-audit
Clone the repo
git clone --depth 1 https://github.com/ConnorRX56/presentation-delivery-skills

Made for: Codex.

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 human-intent-audit

README.md
[![agentmods](https://agentmods.dev/badge/skills/connorrx56/presentation-delivery-skills/human-intent-audit/github.svg)](https://agentmods.dev/skills/connorrx56/presentation-delivery-skills/human-intent-audit)
Your own site
<a href="https://agentmods.dev/skills/connorrx56/presentation-delivery-skills/human-intent-audit"><img src="https://agentmods.dev/badge/skills/connorrx56/presentation-delivery-skills/human-intent-audit/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 human-intent-audit

Your own site · 80×15
<a href="https://agentmods.dev/skills/connorrx56/presentation-delivery-skills/human-intent-audit"><img src="https://agentmods.dev/badge/skills/connorrx56/presentation-delivery-skills/human-intent-audit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 77 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 840 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 $0.00077 $0.00840
Opus 5 $0.00039 $0.00420
Sonnet 5 $0.00015 $0.00168
Haiku 4.5 $0.00008 $0.00084

Measured 11d ago against content hash 873d032a9a1a, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

human-intent-audit 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 11d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/validate_intent_traceability.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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/human-intent-audit/SKILL.md · 92 lines

How it starts

The opening of the file, as written. The whole thing — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Human Intent Audit

Role

Own the translation from human goals to machine-verifiable evidence. Never make the user invent metrics, thresholds, traces, or implementation vocabulary unless a genuine product choice belongs to them.

[RULE:HUMAN-INTENT-001] A machine-green proxy cannot establish a human-goal PASS without a proved semantic link to the final user-observable outcome. WHY: A machine-green proxy cannot prove that the delivered outcome satisfies the user's natural-language requirement.

Input Contract

Collect the user's natural-language requirements, authoritative references, available artifacts, execution context, and any explicit non-goals. Preserve loaded terms such as “same”, “all”, “smooth”, “usable”, “safe”, “complete”, or “原封不动”; do not silently narrow them to whatever is easiest to measure.

Workflow

  1. Record each material human requirement and the interpreted intent.
  2. Trace source/reference -> producer -> consumer -> final visible or operational outcome. Mark every missing edge.
  3. Select final observables. Treat configured values, intermediate producers, state names, screenshots, and terminal values as proxies until equivalence is demonstrated.
  4. Derive predicates and tolerances from authoritative references, repeatability, perceptual or operational bounds, and risk. Never choose a tolerance merely because current output passes it.
  5. Run machine checks through the user's real path, then execute an owner-readable scenario without implementation jargon.
  6. Run a negative fixture reproducing the prior false-positive pattern.
  7. Audit both implementation-vs-predicate and predicate-vs-human-intent.

Perform the scenario before asking the owner to test. The owner is final authority, not the first QA worker or detector author.

Output Contract

For every requirement, record:

  • human_requirement and interpreted_intent;
  • final_observables and machine_predicates;
  • threshold_basis;
  • proxy_risks and unresolved semantic gaps;
  • machine evidence;
  • owner_acceptance_scenario;
  • status: pass, partial, open, blocked, or fail.

Read the full file on GitHub · 92 lines

Files

What ships with it

5 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. 11d ago First seen · 92 lines · 77 tokens per session scan A 873d032a9a1a

Subscribe to this mod's changes

human-intent-audit is a skill published in the GitHub repository ConnorRX56/presentation-delivery-skills (11 stars, last pushed 16d ago), licensed MIT. It adds 77 tokens to every session and 840 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 2026-08-31.

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