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 hybridlabor-api/bdb-dev-optimized-agent-skills --skill startcycle-graph-usergit clone --depth 1 https://github.com/hybridlabor-api/bdb-dev-optimized-agent-skillsWrote 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/hybridlabor-api/bdb-dev-optimized-agent-skills/startcycle-graph-user)<a href="https://agentmods.dev/skills/hybridlabor-api/bdb-dev-optimized-agent-skills/startcycle-graph-user"><img src="https://agentmods.dev/badge/skills/hybridlabor-api/bdb-dev-optimized-agent-skills/startcycle-graph-user.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 111 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00102 | $0.01669 |
| Opus 5 | $0.00051 | $0.00834 |
| Sonnet 5 | $0.00020 | $0.00334 |
| Haiku 4.5 | $0.00010 | $0.00167 |
Grade A, and why
startcycle-graph-user 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 4d 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 — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
⚡ Light Graph — disposable multi-agent fan-out
/startcycle-graph's dispatcher graph is a permanent, project-scoped contract: seven
fixed BDB roles, a state.json schema, a repair loop, a Stop-hook gate. That's
the right tool for the BDB build pipeline — and the wrong tool for "spawn me 3
workers for this one thing right now" in a project that has never heard of
.agents/graph.md and never should. This skill is that lighter tool: a graph
sized to the task in front of you, run once, nothing left behind.
1. Design the graph — you do this, before touching any tool
Read the task. Decide how many nodes it actually needs — most tasks need 2 to 4, not seven:
- Plan (always, even if it's one line): what are the parallel workstreams, and what does "done" look like for each.
- Workers (1 to N, run in parallel): the actual fan-out. If the task doesn't parallelize, this is one node, not several for appearance's sake.
- Review (usually, skip only for genuinely trivial work): one adversarial pass over the workers' output before you call it finished.
Do not invent more structure than the task has. A two-file edit doesn't need a Plan node — just do it. This skill is for the cases actually shaped like a small graph, not an excuse to always draw one.
2. Detect what's available — before deciding how workers run
command -v agy >/dev/null 2>&1 && echo agy
command -v opencode >/dev/null 2>&1 && echo opencode
command -v codex >/dev/null 2>&1 && echo codex
This machine may have none of these — the skill (and whoever installed this package) cannot assume Antigravity, OpenCode, or a Codex plugin connector is present. Pick the worker path in this priority order, first one found wins:
- agy present → workers run via
agy-job start --tier flash [--yolo] "<task>"(orprofor harder reasoning) — see theantigravityskill for the exact invocation pattern and cost discipline. Separate compute pool, zero Anthropic tokens for the work itself. - opencode present → route worker tasks through it the same way (its own subagent/session primitive), if the project already uses it.
- codex present → same idea, via the Codex CLI's own task-delegation surface if this project has that plugin wired up.
- none present → fall back to Claude Code's own
Agenttool for each worker, with an explicitmodel: "haiku"override. This is the only path that costs Anthropic tokens for the worker step, and the only one guaranteed to exist everywhere — it is the floor, not the default.
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.
- 4d ago First seen · 120 lines · 102 tokens per session scan A 2843138082a0
startcycle-graph-user is a skill published in the GitHub repository hybridlabor-api/bdb-dev-optimized-agent-skills (6 stars, last pushed 3d ago), licensed Apache-2.0. It adds 102 tokens to every session and 1,669 once invoked, about $0.0005 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 2026-09-03.
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cog-knowledge-consolidation
Build structured knowledge frameworks from scattered vault notes with source attribution.
cog-onboarding
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cog-weekly-reflection
Cross-domain pattern analysis with personal, professional, and project domain synthesis.
context-engineering
Dynamic context injection, mode switching (dev/review/research), selective loading, and strategic compaction for token optimization.