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 GongShichen/LuminaCode --skill acceptance-runbookgit clone --depth 1 https://github.com/GongShichen/LuminaCodeWrote 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/gongshichen/luminacode/acceptance-runbook)<a href="https://agentmods.dev/skills/gongshichen/luminacode/acceptance-runbook"><img src="https://agentmods.dev/badge/skills/gongshichen/luminacode/acceptance-runbook/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/gongshichen/luminacode/acceptance-runbook"><img src="https://agentmods.dev/badge/skills/gongshichen/luminacode/acceptance-runbook.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 | $0.00013 | $0.01095 |
| Opus 5.5 | $0.00005 | $0.00438 |
| Sonnet 5.5 | $0.00003 | $0.00219 |
| Haiku 4.5 | $0.00001 | $0.00110 |
Grade A, and why
Acceptance Runbook scanned grade A with 1 finding 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 2mo 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
import urllib.error How it starts
The opening of the file, as written. The whole thing — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Produce a runnable acceptance runbook and use it to populate <project_root>/QA_REPORT.md.
Inputs:
- Runtime Team Acceptance Contract from
GetTeamContext. - Required documents and implementation files.
- Project manifest/scripts and stack conventions.
- Integration report, if present.
Contract handling:
- The runtime Team Acceptance Contract is the source of truth for required artifacts, commands, smoke tests, and completion criteria.
- Do not require a standalone contract file unless the runtime contract explicitly lists one in required artifacts.
- If a required artifact is absent from
GetTeamContext.artifacts, verify whether the file exists on disk before marking it missing; then report the exact missing path.
Runbook structure:
-
Setup
- Working directory.
- Runtime version and dependencies.
- Environment variables, ports, fixtures, seed data.
- Background process start/stop plan.
-
Required command matrix
- Command.
- Purpose.
- Expected pass signal.
- Actual result and evidence summary.
- Blocking or non-blocking if unavailable.
- Confirm the command is executable as written. For Python projects,
python -m <name>must use an importable module/package name; a hyphenated product name is not valid unless packaging metadata creates a console entrypoint. - Preserve failure exit codes. Do not accept evidence from
command | head,command | tail, or similar pipelines unless the original command status is captured and reported.
-
Integration smoke
- Real user-facing path.
- Real backend/API/storage/tooling contract.
- Positive case.
- Negative/validation case.
- Persistence or restart case when relevant.
-
Manual checks
- User-visible workflow.
- Accessibility/keyboard basics.
- Error and empty states.
- For CLI/terminal products, include parameter-parse failures and validation failures: missing required argument, invalid type such as a nonnumeric ID, unknown command/option, empty input, nonexistent resource, and corrupted data/configuration when relevant.
- If the user request, PRD, UX design, or contract requires localized output, every user-visible stderr/stdout message in these failures must be checked for that locale.
- If an explicit requirement fails, classify it as blocking unless the Team Leader recorded a specific deferral accepted by the user.
-
Cleanup
- Stop background processes.
- Remove temporary files or confirm they are inside project root.
- Verify parent workspace cleanliness.
-
Verdict
- pass, fail, or not_applicable.
- Findings with owner and reproduction.
- Evidence paths and command summaries for
SubmitGateVerdict.
If a required command cannot be run, mark the reason precisely and classify whether it blocks completion. Missing evidence for a contract-required command is a QA failure.
Embedded HTTP smoke helper:
For small HTTP API checks, you may copy this helper into the project root or run it inline with python3 - <<'PY' ... PY. Adapt endpoint paths and payloads to the actual interface contract. Do not rely on a separate helper file in the skill directory.
#!/usr/bin/env python3
"""Small stdlib HTTP smoke helper for QA runbooks.
Usage:
http_smoke.py GET http://localhost:8000/api/feedback 200
http_smoke.py POST http://localhost:8000/api/feedback 201 '{"author":"QA","message":"ok"}'
"""
from __future__ import annotations
import json
import sys
import urllib.error
import urllib.request
def main(argv: list[str]) -> int:
if len(argv) not in (4, 5):
print(__doc__.strip(), file=sys.stderr)
return 2
method, url, expected_raw = argv[1], argv[2], argv[3]
body = argv[4].encode("utf-8") if len(argv) == 5 else None
try:
expected = int(expected_raw)
except ValueError:
print(f"expected status must be an integer: {expected_raw}", file=sys.stderr)
return 2
headers = {}
if body is not None:
headers["Content-Type"] = "application/json"
request = urllib.request.Request(url, data=body, headers=headers, method=method.upper())
try:
with urllib.request.urlopen(request, timeout=10) as response:
status = response.status
payload = response.read().decode("utf-8", errors="replace")
except urllib.error.HTTPError as exc:
status = exc.code
payload = exc.read().decode("utf-8", errors="replace")
print(f"status={status}")
if payload:
try:
print(json.dumps(json.loads(payload), indent=2, ensure_ascii=False))
except json.JSONDecodeError:
print(payload[:2000])
if status != expected:
print(f"expected status {expected}, got {status}", file=sys.stderr)
return 1
return 0
if __name__ == "__main__":
raise SystemExit(main(sys.argv))
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.
- 2mo ago First seen · 127 lines · 13 tokens per session scan A 2ff7d9653d19
Acceptance Runbook is a skill published in the GitHub repository GongShichen/LuminaCode (9 stars, last pushed 4d ago), licensed Apache-2.0. It adds 13 tokens to every session and 1,095 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
tika-eval-compare
Compare extracts from two Tika builds over a corpus to detect regressions in content, encoding, exceptions, and embedded-document handling. Use for "compare before/after extracts", "eval this change against the corpus".
cua-driver
Use Cua Driver for desktop or browser tasks that are awkward or unavailable through Bash/APIs, or when the user explicitly wants GUI interaction: app testing, visual bug reproduction, form filling, calendar entry, screenshots, and demo recording. Also covers Cua setup; not OpenAI Codex Computer Use or web research.
jetson-validate-image
Use after jetson-flash-image to run static BSP checks, on-target smoke/regression tests on a flashed DUT, or both. Not for build or flash steps. Triggers: validate bsp, on-target validation.
atmos-validation
Validate Atmos projects, components, arbitrary JSON Schema inputs, EditorConfig, and GitHub Actions; use affected-file selection and native CI annotations.
skill-benchmark
Benchmark AI skill effectiveness by measuring implementation quality against legacy constraints.