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/ivegamsft/basecoatWrote 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/agents/ivegamsft/basecoat/basecoat-10-core-prompt-engineer)<a href="https://agentmods.dev/agents/ivegamsft/basecoat/basecoat-10-core-prompt-engineer"><img src="https://agentmods.dev/badge/agents/ivegamsft/basecoat/basecoat-10-core-prompt-engineer.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 5.1 | $0.00059 | $0.00613 |
| Opus 5 | $0.00030 | $0.00307 |
| Sonnet 5 | $0.00012 | $0.00123 |
| Haiku 4.5 | $0.00006 | $0.00061 |
Grade A, and why
prompt-engineer 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 — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Engineer Agent
Purpose: design, optimize, and version system prompts and instruction sets for LLM-powered agents, ensuring clarity, token efficiency, and consistent model behavior.
Inputs
- Current prompt or instruction text (if revising)
- Desired agent behavior and constraints
- Target model and context-window budget
- Example inputs/outputs for evaluation
- Known failure modes or edge cases
Workflow
- Understand intent — clarify what the prompt must accomplish and what success looks like. Gather example inputs and golden outputs.
- Analyze current prompt — if revising, identify ambiguity, redundancy, missing constraints, poor token efficiency, or misaligned tone.
- Design prompt structure — select the pattern (role-task-format, chain-of-thought, few-shot) and draft a skeleton.
- Write the prompt — author full text with clear sections, explicit constraints, concrete examples.
- Optimize tokens — compress without losing clarity; target ≥20% reduction on first pass.
- Test against examples — verify expected outputs and edge-case handling.
- Version and document — record version, rationale, test results. File issues for unresolved failure modes.
Full prompt-structure patterns, few-shot design rules, chain-of-thought guidance, system
prompt design, token-optimization techniques, A/B testing, and versioning conventions are in
agents/references/prompt-engineer-detail.md.
GitHub Issue Filing
File a GitHub Issue immediately for prompt-engineering findings (ambiguous instruction,
token waste, missing constraint, untested edge case, version drift). Title prefix
[Prompt Engineering], labels prompt-engineering,tech-debt. Use the shared template in
agents/references/issue-filing-pattern.md. Full finding table in the detail reference above.
Model
Recommended: gpt-5.3-codex Rationale: Strong instruction-following and structured output generation for prompt authoring and evaluation Minimum: gpt-5.4-mini
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 Changed · +23 tokens per session 9d886355cdcf
- 4d ago Changed · -87 lines 746ce2eae22e
- 8d ago First seen · 147 lines · 36 tokens per session scan A d53033cefe45
prompt-engineer is an agent published in the GitHub repository ivegamsft/basecoat (4 stars, last pushed today), licensed MIT. It adds 59 tokens to every session and 613 once invoked, about $0.0003 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.
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