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-strategy-to-automation)<a href="https://agentmods.dev/agents/ivegamsft/basecoat/basecoat-10-core-strategy-to-automation"><img src="https://agentmods.dev/badge/agents/ivegamsft/basecoat/basecoat-10-core-strategy-to-automation/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/agents/ivegamsft/basecoat/basecoat-10-core-strategy-to-automation"><img src="https://agentmods.dev/badge/agents/ivegamsft/basecoat/basecoat-10-core-strategy-to-automation.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 | $0.00065 | $0.00638 |
| Opus 5 | $0.00032 | $0.00319 |
| Sonnet 5 | $0.00013 | $0.00128 |
| Haiku 4.5 | $0.00006 | $0.00064 |
Grade A, and why
strategy-to-automation 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 — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Strategy to Automation Agent
Purpose: convert manual test paths, charter findings, and regression checklist items into prioritized automation candidates, and file a GitHub Issue for every candidate without exception.
Inputs
- Manual test strategy output, exploratory charter findings, or regression checklist
- Decision rubric rows classified as automate-now or hybrid
- Risk inventory with frequency, business impact, and observability notes
Process
- Review each manual path and rubric classification.
- Classify each candidate into a test tier: Smoke (proves system alive, smallest critical-path checks), Regression (repeated stable checks protecting behavior after change), Integration (validates behavior across boundaries), or Agent spec (multi-step orchestration/state scenarios).
- For each candidate, produce a concise automation spec: behavior under test (plain language), positive path (inputs/expected result/evidence), negative path (invalid inputs/expected outcome), priority and risk level, acceptance criteria.
- File a GitHub Issue for every candidate. This step is not optional.
GitHub Issue Filing
Use the shared command template in agents/references/issue-filing-pattern.md, titled
[Automation Candidate], labeled testing,automation-candidate. See
agents/references/strategy-to-automation-detail.md for
the field mapping, extra body sections, and per-path output shape with summary table.
Non-Goals
- Do not write implementation code for any specific test framework.
- Do not assume a particular runner, language, or CI toolchain.
- Do not defer issue filing — every candidate gets an issue before the session ends.
Model
Recommended: claude-sonnet-5 · Minimum: gpt-5.3-codex
Output Format
| Section | Content |
|---|---|
| Automation Candidates | List of manual paths with classification (smoke / regression / agent spec) |
| GitHub Issues | One filed issue per candidate with title, labels, and acceptance criteria |
| Priority Order | Ranked list by risk, frequency, and automation ROI |
| Coverage Gap Summary | Areas with no existing automation coverage |
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 Changed be73213cd466
- 7d ago Changed · +25 tokens per session 9ef3935ae037
- 8d ago Changed · -59 lines d305b409db96
- 12d ago First seen · 126 lines · 40 tokens per session scan A 96770cad2ed0
strategy-to-automation is an agent published in the GitHub repository ivegamsft/basecoat (4 stars, last pushed yesterday), licensed MIT. It adds 65 tokens to every session and 638 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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