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.
git clone --depth 1 https://github.com/markmhendrickson/foundationWrote 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/rules/markmhendrickson/foundation/autonomous_execution)<a href="https://agentmods.dev/rules/markmhendrickson/foundation/autonomous_execution"><img src="https://agentmods.dev/badge/rules/markmhendrickson/foundation/autonomous_execution/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/rules/markmhendrickson/foundation/autonomous_execution"><img src="https://agentmods.dev/badge/rules/markmhendrickson/foundation/autonomous_execution.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 | $0.00027 | $0.01161 |
| Opus 5.5 | $0.00011 | $0.00464 |
| Sonnet 5.5 | $0.00005 | $0.00232 |
| Haiku 4.5 | $0.00003 | $0.00116 |
Grade A, and why
autonomous_execution 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 1mo 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 — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Autonomous Execution Rule
Purpose
Ensures agents proceed autonomously for routine work, stopping only for high-stakes architectural/design ambiguities that could lead to substantively wrong implementation if answered incorrectly.
Scope
This rule defines:
- When to proceed without asking
- When to stop and ask the user
- How to phrase questions when necessary
- Alignment with risk management hold points
Proceed Without Asking
Execute immediately without confirmation for:
- Routine implementation: Implementing features, refactors, bug fixes where the approach is clear from docs/codebase
- Tests: Writing unit tests, integration tests, E2E tests per testing standards
- Documentation: Updating docs to reflect code changes, fixing doc errors, adding missing documentation
- Lint fixes: Fixing linter errors, type errors, formatting issues
- Code style: Applying code conventions, normalizing naming, organizing imports
- Allowed tool use: Running any Bash commands, edits, or reads allowed by permissions in settings
- Clear requirements: Any task where the docs, codebase, or user request provide a clear answer
Do NOT ask for confirmation before:
- Making edits (permissions already grant Edit)
- Running npm scripts, git commands, or other allowed Bash commands
- Running tests or type checks
- Fixing obvious issues (typos, broken imports, syntax errors)
Stop and Ask Only When
Stop and ask the user ONLY when ALL of these are true:
-
Unclear architectural or design choice: There is ambiguity about:
- Where to put a new module (which subsystem, which layer)
- Which abstraction to use (repository pattern, service pattern, reducer pattern)
- How to cross subsystem boundaries (direct import, event, API)
- Which consistency model to use (strong, eventual, bounded)
- Data model changes (new tables, new entity types, new relationships)
-
High stakes: Choosing wrong would likely cause:
- Wrong layer (strategy/execution logic in Truth Layer)
- Wrong consistency model (eventual when strong required)
- Violating architectural boundaries (cross-layer dependencies)
- Breaking determinism (introducing nondeterministic logic)
- Breaking immutability (modifying observations/source after creation)
- Security issues (exposing PII, bypassing auth)
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.
- 1mo ago First seen · 118 lines · 27 tokens per session scan A 08649f880ecb
autonomous_execution is a cursor rule published in the GitHub repository markmhendrickson/foundation (2 stars, last pushed 2mo ago), licensed MIT. It adds 27 tokens to every session and 1,161 once invoked, about $0.0001 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.
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