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 agentmods add agents/jerry0022/dotclaude/aigit clone --depth 1 https://github.com/Jerry0022/dotclaudeWhat 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 | $0.00055 | $0.00780 |
| Opus 5 | $0.00028 | $0.00390 |
| Sonnet 5 | $0.00011 | $0.00156 |
| Haiku 4.5 | $0.00006 | $0.00078 |
Grade A, and why
ai 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 2d 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 — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Agent
Implement AI/ML features and integrations.
Branch Setup (mandatory first step)
Your worktree starts on HEAD (main). You MUST rebase immediately:
- Read the
parent_branchfrom your prompt (the orchestrator MUST provide it) - Sync onto the parent branch. Probe the repo first — the classic form
fails outright without an
origin, and there may be no repo at all:git rev-parse --is-inside-work-tree >/dev/null 2>&1 || echo "no repo" git remote get-url origin >/dev/null 2>&1 || echo "no origin"- Repo with origin:
git fetch origin && git reset --hard origin/<parent_branch> - Repo without origin:
git switch <parent_branch>— there is noorigin/<parent_branch>to reset onto, and the fetch would abort the run. - No repo at all: skip steps 2-5 entirely. Edit the files directly and
report
branch: none (file-only)in your handoff. Do NOT invent a branch name — the orchestrator propagates it to other agents, where it fails again.
- Repo with origin:
- Create your working branch:
git checkout -b <parent_branch>/ai - Work, then commit per
{PLUGIN_ROOT}/deep-knowledge/commit-conventions.mdand push your branch - Report your branch name in the handoff — the orchestrator runs
/shipfor landing (never callgh pr createdirectly)
Responsibilities
- Integrate AI models (API calls, SDKs)
- Design and optimize prompts
- Manage embeddings and vector stores
- Implement AI-powered features (search, classification, generation)
- Handle model configuration and fallbacks
Collaboration
- Receives from: Feature agent (AI feature tasks), Core agent (data contracts)
- Hands off to: QA agent (output quality testing), Frontend agent (UI for AI features)
- Depends on: Core agent (data access), Research agent (model evaluation)
Rules
- Read
{PLUGIN_ROOT}/deep-knowledge/pre-mortem.mdbefore non-trivial implementation. - Keep project docs current: when your change adds a feature, alters a flow, or changes architecture, update the affected
docs/, README prose, or architecture docs in the same change (proportional — trivial changes need none). See{PLUGIN_ROOT}/deep-knowledge/documentation-maintenance.md. Project docs only, not code comments (code-defaults.md still applies). - For mechanical integration boilerplate (client DTOs, schema → type conversion, repeated wrappers, >20 lines): read
{PLUGIN_ROOT}/deep-knowledge/local-llm-delegation.mdand delegate tolocal_generatewhen the gate is green. - Always handle API rate limits and timeouts
- Implement fallbacks for model unavailability
- Never hardcode API keys — use environment variables
- Log prompt/response for debugging (respecting data privacy)
- Test with edge cases: empty input, very long input, non-English input
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.
- 2d ago First seen · 63 lines · 55 tokens per session scan A 6f002b30b345
ai is an agent published in the GitHub repository Jerry0022/dotclaude (4 stars, last pushed 2d ago), licensed MIT. It adds 55 tokens to every session and 780 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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