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 hdu-ailab/EasyResearch --skill specialist-handoffgit clone --depth 1 https://github.com/hdu-ailab/EasyResearchWrote 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/hdu-ailab/easyresearch/specialist-handoff)<a href="https://agentmods.dev/skills/hdu-ailab/easyresearch/specialist-handoff"><img src="https://agentmods.dev/badge/skills/hdu-ailab/easyresearch/specialist-handoff/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/hdu-ailab/easyresearch/specialist-handoff"><img src="https://agentmods.dev/badge/skills/hdu-ailab/easyresearch/specialist-handoff.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00040 | $0.02083 |
| Opus 5.5 | $0.00016 | $0.00833 |
| Sonnet 5.5 | $0.00008 | $0.00417 |
| Haiku 4.5 | $0.00004 | $0.00208 |
Grade A, and why
specialist-handoff 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 15d 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 — 211 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Specialist Handoff
Purpose
Before every normal terminal response, write one immutable project-local record of what this specialist inspected, did, produced, could not resolve, and recommends next. The chat response wakes the caller; the Markdown file lets the caller verify the work without reconstructing it from transcript prose.
This is an artifact contract, not runtime state. Pi session JSONL and the hidden
<agent_status> plus <agent_handoff> notification remain authoritative for
runtime completion, Error, Stop, and continuation.
When To Apply
The immutable handoff and terminal template apply to actual specialist children, not the user-facing Research Assistant. In single-Agent mode the Research Assistant may use the long-document recovery recipe below, but gives a natural user-facing result without creating a specialist handoff for itself.
Apply this Skill to every normally terminating Search, Experiment, Writing, Figures, or Review run, including:
complete,partial, andblockedoutcomes;- a fresh child run;
- every turn that continues a completed agent id.
A hard runtime Error or external interruption may prevent file creation. Never invent a handoff for work the specialist did not complete or observe.
Long Documents And Interrupted Writes
Apply this recipe before generating a long report, manuscript, or handoff. A complete draft is required before publication; it need not be generated in one tool call. Context-window capacity is not an output-token or time budget.
- Establish the section order. Create a unique hidden
.parts-<role>-<UUID>/directory beside the intended draft, within the role's authorized artifact root. Review's report pieces belong underreviews/; manuscript pieces undermanuscript/. Record the directory and ordered filenames in working notes. - Use one
writecall per small section, for example01-scope.md, then02-findings.md. Split a long section further. Wait for each successful result and inspect the saved section before moving on; keep calls comfortably below the model's output limit. Do not put all sections into one tool batch or one giant shell command.writereplaces a file, never appends. - After interruption, inspect the exact attempted file and confirmed pieces. A streamed path or prose announcement is not execution evidence. Preserve completed pieces; regenerate only missing or incomplete pieces. A read error other than confirmed absence is a blocker to resolve, not permission to overwrite. Never blindly append a retry or retransmit the whole document.
- Read the pieces and check required sections, findings, evidence locators and ordering. Assemble an explicit ordered list, not a wildcard, locally into a fresh unique draft. This short Python recipe reads every piece before opening the destination, retains pieces on failure, and refuses to overwrite a draft:
What ships with it
2 files 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.
- 15d ago Changed · -26 lines 69a8b9153afe
- 1mo ago Changed · +64 lines · +6 tokens per session 505296ecb9a7
- 1mo ago Changed · +31 lines 1e39ea699821
- 1mo ago First seen · 142 lines · 34 tokens per session scan A bbabb07aaa95
specialist-handoff is a skill published in the GitHub repository hdu-ailab/EasyResearch (14 stars, last pushed today), licensed MIT. It adds 40 tokens to every session and 2,083 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 2026-08-30.
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