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 rajitsaha/100xprism --skill orchestrategit clone --depth 1 https://github.com/rajitsaha/100xprismWrote 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/rajitsaha/100xprism/orchestrate)<a href="https://agentmods.dev/skills/rajitsaha/100xprism/orchestrate"><img src="https://agentmods.dev/badge/skills/rajitsaha/100xprism/orchestrate.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 | $0.00059 | $0.00734 |
| Opus 5 | $0.00030 | $0.00367 |
| Sonnet 5 | $0.00012 | $0.00147 |
| Haiku 4.5 | $0.00006 | $0.00073 |
Grade A, and why
orchestrate 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 7d 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 — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Orchestrate — Complex Task Orchestration
Apply workflow orchestration methodology for multi-step tasks with 3+ steps or architectural decisions.
How to use
/orchestrate <task>— plan-first approach for any non-trivial task
Methodology
1. Plan first (invoke writing-plans skill)
Always start by invoking the writing-plans skill (or native plan mode if your platform has it) to produce a structured plan with TDD-style task steps. Write it to wherever writing-plans puts plans for this repo — do not assume a fixed path. Do not proceed to coding until the plan is written and reviewed.
- If something goes sideways mid-task: STOP, re-plan using
writing-plansagain - Track progress via
tasks/todo.mdcheckboxes
2. Use subagents (fan out independent work)
- Offload research, exploration, and parallel analysis to subagents
- Keep main context window clean and focused
- One task per subagent for precision
- Invoke the
subagentsskill for the fan-out ladder and standard return contract
Once the plan splits into independent workstreams, do not implement them serially.
Fan them out using the ladder from the subagents skill (Workflow tool → parallel
subagents → serial fallback):
plan (writing-plans)
→ parallel implementation subagents # one per independent workstream, isolated
each returns { summary, files[], diff, risks[], confidence }
→ gather the structured diffs
→ independent reviewer subagent # fresh context, runs /code-review — no self-review bias
returns { findings[]{severity,file,detail}, verdict }
→ merge / verify gate # apply, run /gate, only then mark done
Reference dispatching-parallel-agents and subagent-driven-development for the
mechanics. Use worktree isolation when subagents edit files in parallel so they don't
collide. Workstreams that share state (same module, ordering dependency) stay serial.
3. Self-improvement loop
- After any correction: note the pattern in
tasks/lessons.md - Review lessons at session start for relevant context
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 7d ago First seen · 78 lines · 59 tokens per session scan A ab79b2002601
orchestrate is a skill published in the GitHub repository rajitsaha/100xprism (10 stars, last pushed 6d ago), licensed MIT. It adds 59 tokens to every session and 734 once invoked, about $0.0003 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 2026-08-31.
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