Borrowing it
Nothing to install: this file belongs to rajbos/ai-engineering-fluency. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/rajbos/ai-engineering-fluency/main/.github/instructions/workflows.instructions.mdgit clone --depth 1 https://github.com/rajbos/ai-engineering-fluencyWrote 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/instructions/rajbos/ai-engineering-fluency/workflows)<a href="https://agentmods.dev/instructions/rajbos/ai-engineering-fluency/workflows"><img src="https://agentmods.dev/badge/instructions/rajbos/ai-engineering-fluency/workflows/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/instructions/rajbos/ai-engineering-fluency/workflows"><img src="https://agentmods.dev/badge/instructions/rajbos/ai-engineering-fluency/workflows.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 | $0.01385 | $0.01385 |
| Opus 5.5 | $0.00554 | $0.00554 |
| Sonnet 5.5 | $0.00277 | $0.00277 |
| Haiku 4.5 | $0.00138 | $0.00138 |
Grade A, and why
ai-engineering-fluency workflows.instructions.md 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 yesterday.
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 — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
pnpm Publishing via OIDC (Trusted Publishing)
This repository publishes the CLI package to npm using GitHub Actions OIDC trusted publishing — no NPM_TOKEN secret is required. The CLI uses pnpm as the package manager; publishing is done via pnpm publish.
Required setup
- Job permission:
id-token: writemust be set on the publish job. - setup-node: Use
registry-url: 'https://registry.npmjs.org'so pnpm is configured for the registry. - Publish command: Always include
--provenanceso the OIDC token is exchanged automatically:
- name: Publish to npm
run: pnpm publish --provenance --access public
Common mistake to avoid
Do not set NODE_AUTH_TOKEN to an empty string:
# ❌ WRONG — explicitly clears the token and causes ENEEDAUTH
run: NODE_AUTH_TOKEN="" pnpm publish
# ❌ ALSO WRONG — requires a secret that isn't needed
run: pnpm publish
env:
NODE_AUTH_TOKEN: ${{ secrets.NPM_TOKEN }}
# ✅ CORRECT — OIDC token is exchanged automatically via --provenance
run: pnpm publish --provenance --access public
Setting NODE_AUTH_TOKEN="" overrides the auth that setup-node configures and causes ENEEDAUTH failures even though id-token: write is set.
Workflow Security: Validating Untrusted User Input
Overview
Workflows in this repository that are triggered by untrusted user input (issue bodies, PR descriptions, comments, branch names, etc.) must validate that input for hidden characters and potential prompt injection before processing it.
This is especially important for workflows that pass user content to AI/LLM systems (e.g. GitHub Copilot agents), but also applies to any automated processing where a malicious actor could influence the workflow's behavior.
The Central Validation Script
.github/workflows/validate-input.sh is the single, authoritative script
for this check. It detects:
| Threat | Description |
|---|---|
| Bidirectional Unicode control characters | Trojan Source attack (CVE-2021-42574) — makes text look different to humans vs. AI |
| Zero-width / invisible characters | Hidden text injected between visible characters, invisible to human reviewers |
| Unicode tag characters (U+E0000–E007F) | Completely invisible; can encode arbitrary ASCII instructions |
| Unicode variation selectors | Can steganographically encode hidden data |
HTML comment blocks (<!-- ... -->) |
Stripped by GitHub's renderer but fully visible to LLMs processing raw Markdown |
| Non-printable control characters | Unexpected control bytes that may confuse parsers |
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.
- yesterday First seen · 150 lines · 1,385 tokens per session scan A 1bba93c6df63
ai-engineering-fluency workflows.instructions.md is an instructions file published in the GitHub repository rajbos/ai-engineering-fluency (119 stars, last pushed yesterday), licensed MIT. It adds 1,385 tokens to every session, about $0.0055 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 2026-10-26.
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