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 skills add jaktestowac/awesome-copilot-for-testers --skill recording-change-intentgit clone --depth 1 https://github.com/jaktestowac/awesome-copilot-for-testersWrote 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/skills/jaktestowac/awesome-copilot-for-testers/recording-change-intent)<a href="https://agentmods.dev/skills/jaktestowac/awesome-copilot-for-testers/recording-change-intent"><img src="https://agentmods.dev/badge/skills/jaktestowac/awesome-copilot-for-testers/recording-change-intent/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/skills/jaktestowac/awesome-copilot-for-testers/recording-change-intent"><img src="https://agentmods.dev/badge/skills/jaktestowac/awesome-copilot-for-testers/recording-change-intent.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00104 | $0.02470 |
| Opus 5 | $0.00052 | $0.01235 |
| Sonnet 5 | $0.00021 | $0.00494 |
| Haiku 4.5 | $0.00010 | $0.00247 |
Grade A, and why
recording-change-intent 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 12d 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 — 160 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Recording Change Intent
Use this skill when a change carries risk and the reason for it exists only in someone's head, a chat thread, or a ticket nobody will find again.
Tests prove a change works. Coverage proves it was executed. Neither records why it was made, which constraint forced the odd shape, or what was considered and rejected. That is intent debt, and it is the one debt an agent structurally cannot pay down: a coding agent can write the implementation, the tests, and the docs, but it cannot supply the human reason the work was commissioned. As AI-assisted output grows, intent debt grows with it while every other quality signal keeps looking healthy.
The fix is small and unfashionable: on high-risk changes, require the rationale to be written down somewhere durable and linked to the change.
When to Use
- an AI-assisted or agent-generated change touches auth, money, schema, or public surface
- reviewers routinely ask "why is this like this" and nobody knows
- a codebase's decisions live in closed tickets and departed colleagues
- a per-change gate needs an intent check next to its test and coverage checks
- an ADR practice exists but nothing links ADRs to the commits that implement them
- a post-incident review cannot reconstruct why a change was made
Operating Principles
- Intent is why, not what. The diff already says what changed.
refactor: extract computeRefundis a restated diff, not a rationale. - Only high-risk surface needs it. Demanding a rationale on every typo fix trains people to write filler, and filler is worse than silence because it looks like compliance.
- A human writes it. An agent can draft it from context and must hand it to a human to confirm or replace. A generated rationale nobody read is intent theatre.
- Durable and linked. In the commit, in an ADR the commit references, or in a register keyed by module. Not in a PR description that a squash-merge will discard, and not in chat.
- A malformed record is not a missing one. Report them differently: one needs writing, the other needs fixing, and conflating them makes the gate feel arbitrary.
- Two layers, different strengths. Fast advisory feedback at commit time; real enforcement at push or CI. Fast feedback that blocks gets disabled; enforcement that is bypassable gets bypassed.
- Constraints and rejected alternatives are the valuable part. "Why not the obvious way" is what a future reader actually needs.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 160 lines · 104 tokens per session scan A d1cb17a2e18f
recording-change-intent is a skill published in the GitHub repository jaktestowac/awesome-copilot-for-testers (113 stars, last pushed 16d ago), licensed MIT. It adds 104 tokens to every session and 2,470 once invoked, about $0.0005 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 2026-08-30.
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