kungfu-agent-onboarding

kungfu-agent-onboarding is a skill for Claude Code, Codex from kungfu-systems/kungfu. It costs 49 tokens per session (940 once invoked), scanned A, original, Apache-2.0.

An onboarding guide for Kungfu, an installed agent-work management system. It checks the installation and guides the user through a selected starting path.

In plain words
What is it for?
Use it to understand Kungfu, inspect its status, start a first task, and safely bind work before making changes.
Why use it?
It reduces the risk of changing work before the system is ready or the correct assignment is connected.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to understand Kungfu, inspect its status, start a first task, and safely bind work before making changes.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/kungfu-systems/kungfu/codex
About the project

Kungfu is a work-continuity system that lets the same task move between coding agents such as Codex, Claude, and OpenCode without losing its context or progress. It is for people coordinating different agents across attempts, reviews, failures, and recoveries, while the catalogue skills and instructions define agent workflows around that shared work.

kungfu-systems/kungfu · 4,533 stars · on GitHub · kungfu.tech

Install

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.

Any agent
npx skills add kungfu-systems/kungfu --skill codex
Clone the repo
git clone --depth 1 https://github.com/kungfu-systems/kungfu

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for kungfu-agent-onboarding

README.md
[![agentmods](https://agentmods.dev/badge/skills/kungfu-systems/kungfu/codex/github.svg)](https://agentmods.dev/skills/kungfu-systems/kungfu/codex)
Your own site
<a href="https://agentmods.dev/skills/kungfu-systems/kungfu/codex"><img src="https://agentmods.dev/badge/skills/kungfu-systems/kungfu/codex/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.

agentmods 80×15 button for kungfu-agent-onboarding

Your own site · 80×15
<a href="https://agentmods.dev/skills/kungfu-systems/kungfu/codex"><img src="https://agentmods.dev/badge/skills/kungfu-systems/kungfu/codex.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 49 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 940 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00049 $0.00940
Opus 5.5 $0.00020 $0.00376
Sonnet 5.5 $0.00010 $0.00188
Haiku 4.5 $0.00005 $0.00094

Measured 1mo ago against content hash 366833e2a2cc, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

kungfu-agent-onboarding 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 1mo 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.

framework/core/src/python/kungfu/agent/skills/codex/SKILL.md · 68 lines

What it actually says

Kungfu Agent Onboarding

When KUNGFU_AGENT_ENVIRONMENT=native-interactive, treat the injected Console/Skill/WorkRef envelopes as discovery pointers, never as prior chat or completion proof. Confirm them with "$KUNGFU_CLI_BIN" agent console current --json, "$KUNGFU_CLI_BIN" agent bootstrap-status --json, and "$KUNGFU_CLI_BIN" skill catalog --json. Keep the provider UI available when bootstrap is pending or degraded, but do not create, bind, or mutate Work until bootstrap is verified. Before the first Work mutation, bind the chosen Assignment with "$KUNGFU_CLI_BIN" agent console bind-work --initiative-id <id> --assignment-id <id> --json; stop unless the result is status: bound, including when another native writer is active.

Run kungfu agent brief. Treat the invocation that returned the brief as the only brief execution for that response; do not run it again.

When an unfamiliar user naturally asks to understand, start, try, or be led through Kungfu, run exactly one standalone kungfu agent first-value start --json; do not load the full intent map or separately run the docs verifier, contract, discovery, or receipt commands on this bounded path. The user does not need to spell out the protocol. The command verifies the installed pack, runs the declared zero-question read-only onboarding discovery without a shell, and emits one rooted receipt. Let its JSON print directly, without capture, redirection, a pipe, or a reprint. When that receipt says agentResponseGuide.protocolComplete: true, run no more commands and do not explain, extend, paraphrase, omit, reorder, or ask a question. Render only its agentResponseGuide.answerTemplate, replacing the sole {receiptRoot} token with the exact top-level receiptRoot. Compare the replacement byte-for-byte before answering; do not substitute a candidate, contract, or other root. Do not retain a raw transcript or treat model prose as proof. For other requests, run kungfu agent docs --verify --json and kungfu agent map --json, then select only the relevant route. Outside that completed first-value path, name one user-supplied or workspace-visible personalization basis, then include one copyable read-only verification command, one concrete safe next step, and the candidate/provider/platform/public-release non-claims.

When durable Work may reduce continuity, handoff, evidence, duplicate retry, or external-write risk, submit only bounded structured signals to kungfu agent work-advisory --signals <signals.json> --json. Never include a transcript, hidden reasoning, credentials, or unrelated context. For recommend, show the returned preview and ask its single confirmation. Only after confirmation use the returned existing kungfu.work.capture, kungfu.work.admit, and kungfu.agent.console.bind-work path, cite its receipts, and continue the original task. Suppress a decline for the returned evidence root until the structured evidence changes. Advice grants no external authority.

For Skill reuse or creation, send only rooted catalog/Work/requirements evidence, candidate roots, enums, and booleans to kungfu agent skill-advisory --signals <signals.json> --json. Consume its shared policy root sha256:dc8ebb873760e55c40ef19b8354ba1e2b91706064a48dec00b1eb8dac0479267; do not reproduce the decision policy in provider prose. The result is read-only.

Use kungfu agent context --task "<task>" --role <role> --budget <tokens> --route <route-id> --json when detail is needed. Stop on invalid roots, ambiguity, stale state, or required omissions; use returned expansion handles instead of loading the whole corpus.

Explain Kungfu in terms of what is already known about the user and workspace, without claiming hidden knowledge. Offer one read-only or preview-first action. Never infer authority from this Skill: writes require their public --execute or authorization path, and Work completion requires native receipts.

Changes

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.

  1. 1mo ago First seen · 68 lines · 49 tokens per session scan A 366833e2a2cc

Subscribe to this mod's changes

kungfu-agent-onboarding is a skill published in the GitHub repository kungfu-systems/kungfu (4,533 stars, last pushed 18d ago), licensed Apache-2.0. It adds 49 tokens to every session and 940 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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