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 commands/get-convex/convex-agent-plugins/convex-advisorgit clone --depth 1 https://github.com/get-convex/convex-agent-pluginsWrote 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/commands/get-convex/convex-agent-plugins/convex-advisor)<a href="https://agentmods.dev/commands/get-convex/convex-agent-plugins/convex-advisor"><img src="https://agentmods.dev/badge/commands/get-convex/convex-agent-plugins/convex-advisor.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 | $0.00035 | $0.00969 |
| Opus 5 | $0.00017 | $0.00485 |
| Sonnet 5 | $0.00007 | $0.00194 |
| Haiku 4.5 | $0.00003 | $0.00097 |
Grade A, and why
convex-advisor 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 — 28 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Live-deployment advisor
Static review guesses; the deployment KNOWS. The official Convex MCP ships an insights tool with typed 72h health events per function — documentsReadLimit / bytesReadLimit (hard limit hits), documentsReadThreshold / bytesReadThreshold (approaching), occFailedPermanently / occRetried (write contention) — each carrying evidence (table_name, bytes_read, documents_read, occ document id + retry count). The advisor turns each event into a root-caused finding by reading the flagged function's actual code, and emits findings on the findings bus (specs/finding.schema.json) so fixers can be dispatched and launch-readiness can score.
Steps
- GUARD: run deploy-guard step 0-1 — identify + announce the deployment being read. Reading insights/logs on prod is allowed read-only; never enable mutating prod access for an advisory pass.
- GATHER (deterministic, via the official Convex MCP):
status→ deployment selector;insights→ the typed 72h events;tables→ schema + row counts;functionSpec→ the public/internal surface. Theinsightstool is only available on cloud dev/prod deployments when logged in as a user (not on previews or deploy-key-scoped contexts) and needs ~72h of traffic; if it returns nothing or is unavailable, say so and fall back to offering convex-reviewer — do NOT invent findings. - ROOT-CAUSE each insight event by reading the flagged function's code:
- bytesReadThreshold/Limit or documentsReadThreshold/Limit → look for
.collect()/ unindexed.filter()/ missing pagination on the named table; the fix is an index +.withIndex,.take(n), or.paginate(convex-expert patterns), or an aggregate component for counting shapes. - occRetried / occFailedPermanently → look for read-modify-write hotspots on the named document (shared counters, status toggles); the fix is @convex-dev/sharded-counter, narrowing the read set, or moving contention to a workpool.
- repeated failures in
logs(status: failure) → classify: crash loop in a cron, validator rejections, unhandled error shapes.
- bytesReadThreshold/Limit or documentsReadThreshold/Limit → look for
- EMIT findings per specs/finding.schema.json: class perf/correctness/cost, severity from the insight kind (limit hits = high, thresholds = med, retried = med, permanent OCC failure = high), locus {kind: deployment, functionId, tableName}, evidence {kind: insight-event, detail: the raw event}, confidence: confirmed (the event happened — it is not a hypothesis), fixCapability + autofixable where the repair is mechanical.
- REPORT: findings ranked by severity, each with (a) the runtime evidence in one line ('messages:list read 4.2MB from messages 31× yesterday'), (b) the code-level root cause with file:line, (c) the concrete fix and which capability applies it. Offer to apply fixes; apply only on confirmation, then re-run
insightsafter traffic to verify the trend, or re-run the static check immediately. - Scope discipline: this is a health/perf/cost pass. Route authz findings to convex-authz, code-idiom findings to convex-reviewer, error triage to sentinel — emit a pointer finding rather than duplicating their work.
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 · 28 lines · 35 tokens per session scan A bf0f38a17e5b
convex-advisor is a command published in the GitHub repository get-convex/convex-agent-plugins (112 stars, last pushed 7d ago), licensed MIT. It adds 35 tokens to every session and 969 once invoked, about $0.0002 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.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.