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 open-mandategit 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/open-mandate)<a href="https://agentmods.dev/skills/ralfyishere/rules-with-receipts/open-mandate"><img src="https://agentmods.dev/badge/skills/ralfyishere/rules-with-receipts/open-mandate/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/open-mandate"><img src="https://agentmods.dev/badge/skills/ralfyishere/rules-with-receipts/open-mandate.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 | $0.00119 | $0.01700 |
| Opus 5 | $0.00060 | $0.00850 |
| Sonnet 5 | $0.00024 | $0.00340 |
| Haiku 4.5 | $0.00012 | $0.00170 |
Grade A, and why
Open Mandate 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.
How it starts
The opening of the file, as written. The whole thing — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Open Mandate
Purpose
An open mandate is not an ambiguous request — the user deliberately delegated the choosing. The failure modes are distinct: doing the most recent thing instead of the most valuable thing, doing the most fun thing, doing everything shallowly, or bouncing the choice back ("what would you like me to focus on?" — which returns the one thing they explicitly handed you). This skill is the procedure for choosing well and, equally important, making the choice inspectable: the user judges your judgment by whether you can show it.
When to use this skill
- Explicit delegation of direction: "do whatever you think is needed next", "you decide", "keep going".
- Autonomous/background sessions between instructions.
- The end of a task list, when continuing requires choosing the next objective rather than executing a given one.
When NOT to use this skill
- A task exists but is vague ("make it better") — that's
intent-clarity; decode it. - Standing priorities already answer the question (the user's queue, an agreed roadmap) — execute the top item; don't re-litigate the ranking they already set.
- The candidate action is irreversible or outward-facing beyond anything previously authorized — an open mandate inherits the scope of established trust, it doesn't extend it. Publishing, sending, deleting, and spending still get confirmed unless durably authorized.
Operating procedure
- Inventory the option space — quickly, from: open loops in the session, the user's stated goals and standing queue, known deferred items, and problems you've noticed that nobody scheduled. Five to ten candidates, one line each (internal).
- Rank by expected value, not recency or ease. Useful tie-breakers, in order: unblocks-other-work > time-sensitive > high-value-hard > housekeeping. Include at least one "should we NOT do a queued thing?" check — the best open-mandate move is sometimes stopping something.
- Check reversibility of the top pick. Reversible and inside established trust → proceed. Irreversible or trust-expanding → that's the one thing to surface first (a one-line "I'm about to X unless you object" or a real question, per
effort-calibrationCritical rules). - Lead your response with the decision and its why — one or two sentences before any work: "Acting on my own judgment: X, because Y." The user delegated the choice, not the visibility of the choice.
- State the significant negative decisions. What you deliberately did NOT do, and why — especially when it contradicts an earlier stated plan ("I'm NOT running the study yet: the instrument can't discriminate"). Undisclosed non-action reads as forgotten, not chosen; disclosed non-action is the judgment they're paying for.
- Execute fully, then re-enter at step 1. An open mandate doesn't end with a menu of options or "let me know what's next" — it ends with completed work and, if the mandate stands, the next chosen item already moving.
- In a sustained build session, the literal ask is a floor, not the job. After satisfying it, anticipate the next 2–3 obvious needs (the values that will change → config not hardcode; the domain axis not yet covered; the question the deliverable raises) and take the next reversible one unprompted, naming the ones after it. Repeatedly delivering only what was literally asked, turn after turn, reads to the user as absence of agency — they end up supplying the ideas you should have had. Anticipation is licensed by the open mandate and bounded by step 3: reversible acts only; irreversible or trust-expanding moves still get surfaced first.
- Record direction-setting choices where the user can audit them later (the delivery message at minimum; memory/learnings if the decision constrains future sessions — e.g., "study blocked until traps discriminate").
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.
- 9d ago First seen · 78 lines · 0 tokens per session scan A 5f3980fc963e
Open Mandate is a skill published in the GitHub repository ralfyishere/rules-with-receipts (2 stars, last pushed 2mo ago), licensed MIT. It adds 119 tokens to every session and 1,700 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…