SkillHub is a self-hosted registry where organizations publish, discover, version, and manage reusable skills for AI agents. Teams use it to share governed skill packages privately, with namespaces, role-based access control, audit logs, and CLI installation; the catalogue entries are skills and related agent integrations that can be managed through it.
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 iflytek/skillhub --skill orca-replaygit clone --depth 1 https://github.com/iflytek/skillhubWrote 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/iflytek/skillhub/orca-replay)<a href="https://agentmods.dev/skills/iflytek/skillhub/orca-replay"><img src="https://agentmods.dev/badge/skills/iflytek/skillhub/orca-replay/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/iflytek/skillhub/orca-replay"><img src="https://agentmods.dev/badge/skills/iflytek/skillhub/orca-replay.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 | $0.00043 | $0.02858 |
| Opus 5.5 | $0.00017 | $0.01143 |
| Sonnet 5.5 | $0.00009 | $0.00572 |
| Haiku 4.5 | $0.00004 | $0.00286 |
Grade A, and why
orca-replay scanned grade A with 1 finding 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 23d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
— a `curl`, `npm install`, `git push` or database call inside a recorded shell command goes How it starts
The opening of the file, as written. The whole thing — 204 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reading a recorded agent run
A recording is evidence. Your memory of a session is not, and neither is a transcript you were handed — both are missing the tool results, the exit codes, and the files that changed without anyone mentioning it.
The rule: when a question is about something that already happened, read the trace before you answer. Do not reconstruct it. If a recording exists, guessing is the wrong move even when the guess would have been right.
When to Use This Skill
- "Why did you delete/overwrite/move X?"
- "What changed this file?" / "Which step broke the build?"
- "Can you reproduce yesterday's failure?"
- "Does this still reproduce?" (see the limit on that in step 4 — replay cannot tell you whether a fresh run would fail again)
- "Would a different model have got this right?"
Workflow
1. Find the run
orca_list_runs — newest first, and it names the run each fork came from. Skip this only when the
user clearly means the most recent one; every other tool defaults to run: "last".
2. Narrow to the chain that produced the thing being asked about
orca_show_run gives the whole timeline: model turns with token counts and stop reasons, tool
calls with arguments and results, shell commands with exit codes, and every file the run changed.
Good for orientation, long for a specific question.
orca_graph is usually the better tool. It returns causal edges — which event produced which. Pass
to: <event seq> to get only the chain that produced one event. That is the shape of an answer
to "why did this happen", where the full timeline is the shape of an answer to "what happened".
3. Report recorded and inferred differently
Every edge from orca_graph is labelled:
recorded— the recorder watched it happen and wrote it into the trace.inferred— derived just now from a rule the edge names. The trace does not vouch for it.
Carry that distinction into your answer. "The trace shows the rm at step 14 removed it" and "this
looks like the rm at step 14, going by timing" are different claims, and flattening them into one
confident sentence is the specific failure this tool exists to prevent. Name the rule when you lean
on an inferred edge.
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.
- 23d ago First seen · 204 lines · 43 tokens per session scan A e849b1e4674a
orca-replay is a skill published in the GitHub repository iflytek/skillhub (5,223 stars, last pushed today), licensed Apache-2.0. It adds 43 tokens to every session and 2,858 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-23.
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