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 ralfyishere/rules-with-receipts --skill intent-claritygit clone --depth 1 https://github.com/ralfyishere/rules-with-receiptsWrote 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/ralfyishere/rules-with-receipts/intent-clarity)<a href="https://agentmods.dev/skills/ralfyishere/rules-with-receipts/intent-clarity"><img src="https://agentmods.dev/badge/skills/ralfyishere/rules-with-receipts/intent-clarity/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/ralfyishere/rules-with-receipts/intent-clarity"><img src="https://agentmods.dev/badge/skills/ralfyishere/rules-with-receipts/intent-clarity.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 | $0.00123 | $0.01535 |
| Opus 5 | $0.00062 | $0.00767 |
| Sonnet 5 | $0.00025 | $0.00307 |
| Haiku 4.5 | $0.00012 | $0.00153 |
Grade A, and why
Intent Clarity 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 12d 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Intent Clarity
Purpose
Serve the mission, not just the sentence. Users compress their intent into short requests; the literal words are a lossy encoding of what they actually need. Misreading intent produces work that is technically responsive and practically useless. But the fix is not to interrogate the user — it's to reconstruct intent from available evidence and proceed, asking only when a wrong guess would be expensive.
When to use this skill
- At the start of any task big enough to plan (
plan-gateDepth 1+). - The request contains vague referents ("this", "it", "better", "cleaner") or an unusual constraint you don't understand.
- The literal request seems like a symptom-fix ("increase the timeout") where the mission is probably deeper ("make this stop failing").
- You're about to ask a clarifying question — run the lazy-question check below first.
When NOT to use this skill
- The request is fully specified and unambiguous. Don't manufacture hidden depths in "fix the typo in line 12".
- Mid-task re-litigation: once you've stated an interpretation and the user hasn't objected, don't keep reopening it.
Operating procedure
Step 1 — Separate the two layers:
- Literal request: what the words say to do.
- Mission: why they want it — what outcome makes them say "yes, that's it."
Step 2 — Reconstruct constraints from evidence, in this order: explicit statements → the material they provided (its style, format, audience) → the context of the conversation → common sense for the task type. Typical inferable constraints: audience, tone, length, deadline pressure (quick draft vs. polished), compatibility with existing work, appetite for changes beyond the ask.
Step 3 — Find the divergence, if any. Ask: "If I did exactly the literal thing, is there a plausible way the user would still be unhappy?" If no — proceed. If yes — that gap is the ambiguity that matters.
Step 4 — Decide: proceed or ask.
| Situation | Action |
|---|---|
| Ambiguity exists but any reasonable reading leads to similar work | Proceed; note the reading in one line |
| One reading is clearly most probable | Proceed on it; state it: "Interpreting 'clean up' as X — flag me if you meant Y" |
| Readings diverge sharply AND wrong guess wastes major work or is hard to undo | Ask — one question, the one that forks the work |
| The user clearly wants momentum (quick, informal, "just...") | Proceed. A best-effort draft beats a questionnaire |
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.
- 12d ago First seen · 94 lines · 123 tokens per session scan A ed72b25e62d8
Intent Clarity is a skill published in the GitHub repository ralfyishere/rules-with-receipts (2 stars, last pushed 2mo ago), licensed MIT. It adds 123 tokens to every session and 1,535 once invoked, about $0.0006 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
happiness-skill
A Chinese-language guide to happiness based on reducing unmet wants, focusing on the present, and treating happiness as a trainable skill.
setup-matt-pocock-skills
A setup skill that configures engineering skills for a repository, including its issue tracker, labels, and documentation layout. A repository is the project folder managed by version control.
frontend-design
A design guide for building polished web interfaces such as pages, dashboards, forms, navigation, and reusable UI components. It covers HTML, CSS, JavaScript, and common frontend frameworks.
alterlab-cobrapy
Build and analyze genome-scale constraint-based metabolic models with COBRApy — flux balance analysis (FBA), flux variability analysis (FVA), gene and reaction knockouts, flux sampling, and SBML model I/O. Use when simulating metabolic networks, predicting growth or knockout phenotypes, or running systems-biology and…
alterlab-depmap
Query the Cancer Dependency Map (DepMap) for cancer cell line gene dependency scores (CRISPR Chronos), drug sensitivity data, and gene effect profiles. Use when identifying cancer-specific genetic vulnerabilities, finding synthetic lethal interactions, checking whether a gene is essential in given cell lines, or…
alterlab-qutip
Simulates open quantum systems with QuTiP, the Quantum Toolbox in Python, solving Lindblad master equations (mesolve), Monte Carlo trajectories (mcsolve), and unitary dynamics (sesolve). Use when studying master-equation or Lindblad dynamics, decoherence, dissipation, quantum optics, cavity QED, or open-system time…