Apache ShardingSphere is a database enhancement layer that makes heterogeneous databases easier to access and govern as a unified system, without replacing the underlying databases. It supports distributed database capabilities such as sharding, read-write splitting, SQL federation, encryption, masking, auditing, and traffic control for enterprise data architectures.
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 agents/apache/shardingsphere/policy-maintenancegit clone --depth 1 https://github.com/apache/shardingsphereWrote 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/apache/shardingsphere/policy-maintenance)<a href="https://agentmods.dev/agents/apache/shardingsphere/policy-maintenance"><img src="https://agentmods.dev/badge/agents/apache/shardingsphere/policy-maintenance.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 5.1 | $0.00000 | $0.01448 |
| Opus 5 | $0.00000 | $0.00724 |
| Sonnet 5 | $0.00000 | $0.00290 |
| Haiku 4.5 | $0.00000 | $0.00145 |
Grade A, and why
policy-maintenance 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.
How it starts
The opening of the file, as written. The whole thing — 44 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Repository Policy Maintenance
Apply this file only when the user authorizes changes to AGENTS.md, a canonical policy source listed in .codex/harness/agents/policy-sources.toml, or the harness that validates those sources. cases.toml is the source of the harness catalog: each case records its group, purpose, enforcement phase, and whether the ordinary completion loop rechecks it. Run python3 .codex/harness/agents/run.py --list-cases to render the table.
validate and trace are local deterministic modes. semantic and all start isolated networked Codex evaluations, so run them only when non-sensitive policy transmission is authorized and the execution environment has network access; use command-bound platform approval when the platform requires it.
- Treat every explicit requirement, prohibition, exception, authorization boundary, and verification step as an independent policy capability.
- Before editing, build an old-to-new capability ledger and classify every capability as exactly preserved, changed with the user's explicit authorization in the current task, or restored before handoff.
- Reconcile every deleted or weakened rule explicitly. A replacement is equivalent only when it preserves the same trigger, required or forbidden action, scope, exceptions, and verification obligation. A positive general rule does not replace a specific negative prohibition. Ordinary Codex competence, implication, nearby prose, a Skill, or a canary is not evidence that any explicit rule is preserved. Partial or implicit coverage is a regression.
- Never remove, weaken, merge away, or broaden an exception to a capability unless the user explicitly authorizes that exact policy change. When equivalence is uncertain, keep the existing rule.
- Before editing, capture a V0 policy baseline and a source snapshot for every manifest entry; then change one coherent instruction group. Keep the original V0 and source snapshot across every candidate in the active task.
- Count a canary as passing only when its decision matches, every required action and reason is present, and every action outside its complete allowed-action set is absent. A partial forbidden-action list is insufficient.
- Change an established canary contract only when the normative policy and observed output prove that it rejects policy-permitted behavior. Record the exact mismatch and preserve the required decision, action, reason, and prohibition coverage. Do not weaken a canary or edit the runner solely to accept one model's wording. When the user explicitly authorizes a case-contract change, name only that case with
--authorized-contract-change; every other changed or removed critical contract remains a regression. - After each coherent working-revision edit, run
--mode validateand only the added, changed, or directly affected semantic cases and source-read traces. A working revision cannot be handed off or used as the local bootstrap. - Promote a working revision only after those focused checks pass, then run
--mode allacross every case and trace and compare pass rate, duration, input tokens, and uncached input tokens with V0. Only a promoted candidate that passes this full comparison may enter bootstrap, completion review, or handoff. Each full failure returns the candidate to working-revision state; inspect its summary and bounded logs, and do not rerun an unchanged candidate. - Validate the manifest, exact-path reference graph, root byte limit, source hashes, staged policy bundle, and source-read traces. A source-read trace passes only when it proves that each required exact file was locally available and read through EOF from both root and applicable nested working directories; it does not prove semantic interpretation by a separate Codex evaluation. Semantic canaries evaluate the root
AGENTS.mddecision surface unless a separately authorized harness mode explicitly supplies other non-sensitive policy sources. Report these proof boundaries directly; automatic Skill catalog discovery is diagnostic and does not replace the exact-path route. - Bind every legacy critical case whose controlling rule moves out of
AGENTS.mdto its routing profile, manifest source, and complete normative sentences withpolicy_binding_profileandpolicy_assertionsincases.toml. The normalized binding set and digest prove that each routed source still contains the bound rule, but they do not prove that a model interpreted it. Reject a missing, shortened, moved, or changed established binding even when the source and binding are edited together. - After the first promoted policy-bundle candidate passes, run
--mode validatewithout a policy-bundle baseline and retain its summary as the explicit local inventory-and-binding bootstrap. Pass that summary with--baselineto every later deterministic candidate; the original semantic V0 remains the independent behavior baseline and is not replaced by this bootstrap. - Change an established binding only for an explicitly authorized equivalent rule migration, name only that case with
--authorized-policy-binding-change, and preserve the old-to-new sentence audit as task evidence. Never use binding authorization to weaken a rule, bypass a semantic canary, or change an unrelated case. - After the bootstrap, treat every source addition, removal, relocation, metadata change, or content hash change as a policy-source change. Accept it only when the user explicitly authorized that exact source and the run names its source ID with
--authorized-policy-source-change; this source authorization does not replace the capability ledger, binding migration audit, or semantic canaries. - If the same policy failure appears twice and is not an environment failure or a brittle canary contract, add one focused case instead of generic prose. Stop after five promoted candidates or when no measurable improvement remains.
- After the canaries pass, complete the applicable simplification, internal candidate review, finding-fix, and re-review steps in
.codex/context/change-completion.mdbefore handoff.
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 · 44 lines · 0 tokens per session scan A 68d1e3095e75
policy-maintenance is an agent published in the GitHub repository apache/shardingsphere (20,793 stars, last pushed today), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 1,448 tokens. 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 2026-09-03.
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