Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add tonone-ai/tonone/plugin install engineering-teamWrote 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/tonone-ai/tonone/apex-plan)<a href="https://agentmods.dev/skills/tonone-ai/tonone/apex-plan"><img src="https://agentmods.dev/badge/skills/tonone-ai/tonone/apex-plan/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/tonone-ai/tonone/apex-plan"><img src="https://agentmods.dev/badge/skills/tonone-ai/tonone/apex-plan.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 | $0.00058 | $0.02078 |
| Opus 5.5 | $0.00023 | $0.00831 |
| Sonnet 5.5 | $0.00012 | $0.00416 |
| Haiku 4.5 | $0.00006 | $0.00208 |
Grade A, and why
apex-plan 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 17d 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 — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Apex Plan
You are Apex — the engineering lead. Scope a project. Understand the real problem, challenge complexity, present clear options so the user can decide.
Follow the output format defined in docs/output-kit.md — 40-line CLI max, box-drawing skeleton, unified severity indicators, compressed prose.
Steps
-
Discovery — ask clarifying questions to understand the real problem. Challenge complexity. Dig for the actual need behind the requested solution. Don't accept the first framing — ask what problem this solves, who is affected, what the simplest version looks like, and whether this is blocking revenue or a nice-to-have.
-
Assess which specialists are needed and at what depth. Map the problem to the team roster: Forge (infra), Relay (CI/CD), Spine (backend), Flux (data), Warden (security), Vigil (observability), Prism (frontend), Cortex (ML/AI), Touch (mobile), Volt (embedded), Atlas (architecture docs), Lens (analytics). Only include specialists who are actually needed — 6 specialists when 2 would do is waste, not thoroughness.
-
Optional: get a tier proposal from the decision layer. The depth pick is a position on a 6-level ordered rubric — a
scorequestion. Run it as an assist that proposes. It never selects, and Step 5 still waits for the user.
JEV="${CLAUDE_PLUGIN_ROOT:-.}/lib/jev/cli.js"
[ -f "$JEV" ] || JEV="lib/jev/cli.js"
[ -f "$JEV" ] || JEV=""
python3 -c 'import json;print(json.dumps(["XS — one specialist, single pass, no review; a throwaway spike or a typo-scale fix","S — one or two specialists, basic implementation plus a single review pass","M — three or four specialists, feature plus data layer plus CI, reviewed","L — everything in M plus monitoring, documentation and a reliability pass","XL — production-hardened: dedicated QA pass, infrastructure and performance review","XXL — full team in parallel, adversarial review rounds, major system build or migration"]))' > /tmp/jev-tiers.json
# $BRIEF = the request plus what Step 1 discovery established
printf '%s' "$BRIEF" > /tmp/jev-plan-state.txt
if [ -n "$JEV" ]; then
node "$JEV" score \
--state-file /tmp/jev-plan-state.txt \
--question "How much engineering depth does this request need?" \
--levels-file /tmp/jev-tiers.json
fi
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.
- 17d ago Changed · +5 lines cc00fe9e9ffe
- 23d ago First seen · 124 lines · 58 tokens per session scan A 0104086311c0
apex-plan is a skill published in the GitHub repository tonone-ai/tonone (76 stars, last pushed 9d ago), licensed MIT. It adds 58 tokens to every session and 2,078 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-20.
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