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 agentmods add rules/techtalk/ai-readiness-assessment/constraintsgit clone --depth 1 https://github.com/techtalk/ai-readiness-assessmentWrote 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/techtalk/ai-readiness-assessment/constraints)<a href="https://agentmods.dev/rules/techtalk/ai-readiness-assessment/constraints"><img src="https://agentmods.dev/badge/rules/techtalk/ai-readiness-assessment/constraints.svg" alt="Measured on agentmods" 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 | $0.00397 | $0.00397 |
| Opus 5 | $0.00198 | $0.00198 |
| Sonnet 5 | $0.00079 | $0.00079 |
| Haiku 4.5 | $0.00040 | $0.00040 |
Grade A, and why
constraints 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 4d 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.
What it actually says
Project Constraints
These constraints are synced from HARNESS.md. Do not edit this file
directly — run /convention-sync to regenerate.
Consistent formatting
- Rule: All source files must pass the project's configured formatter without changes
- Enforcement: unverified
- Scope: commit
Tests must pass
- Rule: The project's test suite must pass with zero failures before any code is merged
- Enforcement: deterministic
- Scope: pr
Dual-surface sync (command ≡ skill)
- Rule: The embedded framework content (the model, the cognitive read,
the scoring heuristic, and the assessment process) in
commands/ai-readiness-assess.mdmust match the same content inskills/ai-readiness-assessment/SKILL.md. Editing one without updating the other is forbidden. - Enforcement: agent
- Scope: pr
Spec-first
- Rule: A PR that changes the instrument (commands/ or skills/) or the
model/scoring must reference a spec under
specs/(a changedspecs/NNNN-*.md), unless labelled chore/fix/docs. Each substantive spec carries an adjudicated Adversarial review. - Enforcement: deterministic (ordering) + agent (review adjudication)
- Scope: pr
No direct pushes to main
- Rule: All changes must be committed on a feature branch and merged via
a pull request — direct pushes to
mainare not permitted, including for administrators - Enforcement: deterministic
- Scope: pr
No secrets in source
- Rule: No API keys, tokens, passwords, or private keys may appear in committed source files
- Enforcement: deterministic
- Scope: commit
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.
- 4d ago First seen · 59 lines · 397 tokens per session scan A 1f0cf5304283
constraints is a cursor rule published in the GitHub repository techtalk/ai-readiness-assessment (9 stars, last pushed 17d ago), licensed Apache-2.0. It adds 397 tokens to every session, about $0.0020 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 cursor rules, from other repositories
constraints
Architectural constraints synced from HARNESS.md.
conventions
Project conventions synced from HARNESS.md.
verify-ai-readiness
Holistic assessment of the AI knowledge layer on a 5-level maturity scale; flags agent-blocking gaps.
01-qa-agent
QA AGENT PERSONA: Principles, Anti-patterns, workflows.
03-skill-testcases
SKILL: Generate Test Scenarios Matrix from API spec (use for /api-isolated-tests).
ai-dev-os-review
Performs a comprehensive self-review before creating a PR. Combines guideline compliance checking (L3) with design-level review (L2) and philosophical alignment (L1). Unlike @ai-dev-os-check which only checks rules, this also evaluates architecture decisions and code design quality.