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/conventionsgit 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/conventions)<a href="https://agentmods.dev/rules/techtalk/ai-readiness-assessment/conventions"><img src="https://agentmods.dev/badge/rules/techtalk/ai-readiness-assessment/conventions.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 | $0.00848 | $0.00848 |
| Opus 5 | $0.00424 | $0.00424 |
| Sonnet 5 | $0.00170 | $0.00170 |
| Haiku 4.5 | $0.00085 | $0.00085 |
Grade A, and why
conventions 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 3d 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 — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Project Conventions
These conventions are synced from HARNESS.md. Do not edit this file
directly — run /convention-sync to regenerate.
Stack
- Primary languages: Markdown (content), JSON (plugin manifest). No programming language is compiled or executed; the plugin's entire product is the prose inside the command and skill files.
- Build system: None for the plugin artifact — a plugin distribution
layout (
.claude-plugin/plugin.json+commands/+skills/) consumed directly by Claude Code, Copilot, Cursor, and Windsurf, with no compilation, bundling, or packaging. The one build step is the docs site (mkdocs build --strict, see CI/CD). - Test framework: TDAB (Test-Driven Agentic Behaviours). The A-tier
(structural) assertions are automated in
tests/run.py(stdlib only) and CI-enforced on every PR by.github/workflows/agentic-behaviours.yml. B-tier (behavioural) and C-tier (semantic) assertions in each fixture'sexpected.mdare run manually — seetests/README.md. - CI/CD: GitHub Actions. On every PR to a branch-protected
main, four required checks run —A-tier structural assertions(the TDAB suite,agentic-behaviours.yml),Changelog gate(changelog-gate.yml),Spec-first gate(spec-first-gate.yml) andOnboarding gate(onboarding-gate.yml). On a version bump,release.ymlpublishes a GitHub Release fromCHANGELOG.md; on docs changes,pages.ymlbuilds and deploys the MkDocs site. - Container strategy: N/A. No runtime, no container.
Conventions
- Dual-surface sync: The framework content embedded in
commands/ai-readiness-assess.mdmust be identical to the same content inskills/ai-readiness-assessment/SKILL.md. Editing one without updating the other is forbidden. - Self-contained: Neither
commands/ai-readiness-assess.mdnorskills/ai-readiness-assessment/SKILL.mdmay reference, invoke, or depend on any other plugin, skill, agent, MCP server, or external service. Thedependenciesfield in.claude-plugin/plugin.jsonmust remain absent or empty. - Single CTA: The assessment's recommendation output must propose exactly one specific TechTalk engagement. Multi-option menus, "consider one of…", or "you might want…" lists are forbidden.
- Frontmatter shape: Every file in
commands/and everySKILL.mdunderskills/must carry YAML frontmatter withnameanddescription. For commands,nameequals the filename without.md; for skills,nameequals the parent directory name. - Plain-text output: The optional rendered output (HTML, printable PDF, or other) must use print-friendly typography. No emojis appear in any rendering template the skill emits.
- Spec-first for substantive changes: Substantive, behaviour-changing
work (the assessment instrument, the model or scoring, a new workflow)
is captured as a spec under
specs/— at minimum a one-paragraph intent — written or updated with the change and referenced in the PR. Docs, chore, surface-sync, dependency, and pure-fix PRs are exempt. Each spec carries a "Risks / what could go wrong" section (a lightweight adversarial review). Enforced — see the Spec-first constraint.
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.
- 3d ago First seen · 65 lines · 848 tokens per session scan A 82d5afd5075d
conventions is a cursor rule published in the GitHub repository techtalk/ai-readiness-assessment (9 stars, last pushed 16d ago), licensed Apache-2.0. It adds 848 tokens to every session, about $0.0042 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.
03-skill-testcases
SKILL: Generate Test Scenarios Matrix from API spec (use for /api-isolated-tests).
cursorrules
ALWAYS start your session by reading AGENTS.md and .memory/wiki/hot.md to get project context before suggesting code or answering questions.
angular-20
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