OpenTag connects Slack with coding agents running on a computer controlled by the user, so channel mentions can queue work and return status, decisions, and evidence in the same thread. It is for engineering teams that want to run agents such as Claude Code, Codex, or Cursor against local checkouts through the Agent Client Protocol.
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 amplifthq/opentag --skill writing-testsgit clone --depth 1 https://github.com/amplifthq/opentagWrote 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/amplifthq/opentag/writing-tests)<a href="https://agentmods.dev/skills/amplifthq/opentag/writing-tests"><img src="https://agentmods.dev/badge/skills/amplifthq/opentag/writing-tests/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/amplifthq/opentag/writing-tests"><img src="https://agentmods.dev/badge/skills/amplifthq/opentag/writing-tests.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 | $0.00211 | $0.04572 |
| Opus 5.5 | $0.00084 | $0.01829 |
| Sonnet 5.5 | $0.00042 | $0.00914 |
| Haiku 4.5 | $0.00021 | $0.00457 |
Grade A, and why
writing-tests 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 2d 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.
This is a copy
91% identical to writing-tests — 33 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 204 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Writing tests worth keeping
The rationale and the same rules in human-facing form live in the handbook: Backend coding conventions › Testing (docs/published/handbook/engineering/conventions/backend-coding.md).
This skill is the operational gate — run it before writing tests. It carries the decision procedure plus a catalog of the bug shapes we actually ship.
The gate covers added coverage, not new test functions. One fixture and one assertion added to an existing block goes through the same two questions, because that is the shape this skill asks you to prefer.
The gate: two questions
Before writing any test, answer both in one sentence each.
1. Does it earn its place?
What realistic regression does this test catch that no existing test already catches?
If you can't answer it concretely — name the bug, the code path, the input that would break — do not write the test. "Increases coverage", "good practice", and "the function exists" are not answers.
A good answer sounds like: "if someone makes parse_filters drop the team_id clause, this fails" or "empty-cohort input used to 500; this locks in the 400."
That is a test worth keeping.
Aim each test at a failure mode we actually hit, not a hypothetical. The bugs PostHog ships and reverts cluster into a handful of shapes — cataloged with the test that catches each, and the failure modes no unit test should, in references/mistakes-we-make.md. If your test doesn't map to one of them, be skeptical it's worth keeping.
2. Where does it go?
Passing the first question buys the coverage, not a new test function. Find the test that already covers the nearest behavior and answer:
Why can't this be a case in that test?
When your case is a variation of that behavior, extend it: a @parameterized case in Python, a test.each row in Jest.
A new standalone test is what you write when extending doesn't work, and you should be able to say why in a sentence: the setup differs, the behavior belongs to a different unit, or no relevant test exists.
"It's cleaner as its own test" isn't a reason on its own; name which of those three applies.
What ships with it
4 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.
- 2d ago First seen · 204 lines · 211 tokens per session scan A e6005730691c
writing-tests is a skill published in the GitHub repository amplifthq/opentag (0 stars, last pushed 2d ago), licensed MIT. It adds 211 tokens to every session and 4,572 once invoked, about $0.0008 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 91% identical to writing-tests, differing in 33 lines, and is treated as a copy.
Other skills, from other repositories
qa-acceptance
Produce QA acceptance criteria and a manual validation plan for a feature change — golden path, edge cases, error states, performance limits, and explicit pass/fail evidence.
codew-release-qa-sweep
Use before claiming Codewhale release work is done: run the full gate sweep and list the manual QA targets.
cw-gates
Use before claiming any Codewhale change is done, green, or ready to land: the focused-to-broad verification ladder, the budget checks CI enforces, and the rules for what counts as a passing test.
verify
Exercise the real app/API/CLI and collect observable evidence; tests alone do not count as end-to-end verification.
webapp-testing
Start/reuse a local app, wait for readiness, inspect rendered state/console/network, act from observed selectors, and verify with evidence.
verification-before-completion
Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always.