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 skills/codagent-ai/agent-skills/prepare-acceptancenpx skills add Codagent-AI/agent-skills --skill prepare-acceptancegit clone --depth 1 https://github.com/Codagent-AI/agent-skillsWrote 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/codagent-ai/agent-skills/prepare-acceptance)<a href="https://agentmods.dev/skills/codagent-ai/agent-skills/prepare-acceptance"><img src="https://agentmods.dev/badge/skills/codagent-ai/agent-skills/prepare-acceptance.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 | $0.00058 | $0.03820 |
| Opus 5 | $0.00029 | $0.01910 |
| Sonnet 5 | $0.00012 | $0.00764 |
| Haiku 4.5 | $0.00006 | $0.00382 |
Grade A, and why
prepare-acceptance 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 5d 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 — 281 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prepare Acceptance
Prepare an implemented change for a separate human acceptance session. Exercise it as a human user or real client would, report clear defects, wait for CI once no defects remain, and produce concise evidence for the code currently checked out. Fixes and automated validation belong to separate caller-managed steps when used. PR and commit identity establish final CI and handoff alignment; they are not prerequisites for exercising the product.
Required inputs
Resolve these from the caller before acting:
- approved requirements, scenarios, design, test plan when one exists, and task artifacts;
- caller-supplied implementation summary or completion evidence identifying delivered behavior;
- evidence output directory;
- unresolved-assumptions ledger path, when one exists;
- verification scope:
full;targeted, naming the affected flows, directly dependent flows, a concise impact rationale, and prior full-pass evidence to retain, plus the caller's attestation that this scope covers the tracked changes since that baseline; orevidence-only, identifying prior flow evidence whose coverage revision exactly matches the current tracked contents and supplying the caller's attestation of that content match.
If source-of-truth artifacts or the evidence directory are missing, report what is missing and stop
before flow execution. Do not infer product behavior from implementation alone. When an approved
test plan exists, treat its required and activated conditional AT-* flows, evidence requirements,
authorized effects, and permitted substitutes as authoritative. Use full when the caller does not
supply a scope or no trustworthy full-pass baseline exists. Targeted and evidence-only reuse depend on
the caller's impact or content attestation: reconcile it with the approved flow inventory, but do not
independently inspect a diff. Use evidence-only only when the caller attests that no tracked product
contents changed after the recorded coverage revision; PR alignment, pushing the already-tested commit,
waiting for CI, or a Validator run that made no tracked change do not invalidate flow evidence. When no
assumptions-ledger path is supplied, use
<evidence-directory>/acceptance-assumptions.md and create it if needed. Keep this preparation
autonomous: preserve product, scope, or design ambiguity for the later human acceptance session
instead of asking the user here.
What ships with it
1 file 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.
- 5d ago First seen · 281 lines · 58 tokens per session scan A 79e4eeaee497
prepare-acceptance is a skill published in the GitHub repository Codagent-AI/agent-skills (30 stars, last pushed 1mo ago), licensed MIT. It adds 58 tokens to every session and 3,820 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 2026-08-30.
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