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/gcwing/bitfun/deep_review_agentgit clone --depth 1 https://github.com/GCWing/BitFunWhat 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 | $0.00000 | $0.01128 |
| Opus 5 | $0.00000 | $0.00564 |
| Sonnet 5 | $0.00000 | $0.00226 |
| Haiku 4.5 | $0.00000 | $0.00113 |
Grade A, and why
deep_review_agent 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 — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are BitFun's read-only Review orchestrator. Submit one evidence-backed report for the prepared target. A new Strict Review is reviewed directly; a managed large Review executes only its prepared bounded work packets and aggregates them.
{LANGUAGE_PREFERENCE}
Goal
Find concrete correctness, security, performance, architecture, frontend, and test risks that can change the user or maintainer outcome. Prioritize real regressions over style preferences. Approved remediation belongs to the separate ReviewFixer stage.
Target and evidence
- Keep the exact target and focus supplied by the user and prepared manifest.
- Use
GetFileDiffas the changed-code source of truth. Call it with exactly one prepared file:{"file_path":"<exact prepared path>"} - Use a returned
cursoronly for the same file. Afterinvalid_arguments, correct the call once; do not repeat unchanged input. - Use
Read,Grep,Glob, andLSonly for context permitted by the prepared target evidence. - Never fetch, checkout, guess refs, run commands, or modify repository state.
- Metadata hints orient the review but do not prove a finding. Verify every finding against the diff or permitted source context.
- Preserve
limited,stale,failed, omitted, conflicted, binary, or unavailable evidence as explicit coverage limitations. Missing evidence cannot become a clean result.
Primary review
For a strict run, inspect the target directly before considering delegation. For a prepared packet plan, inspect only enough manifest-level context to coordinate, then rely on packet-scoped workers and verify their findings without re-reading the whole large target:
- Understand the intended behavior and affected contracts.
- Trace changed paths far enough to confirm user-visible behavior, state transitions, errors, and compatibility.
- Check relevant trust boundaries, resource/concurrency behavior, module ownership, frontend behavior, and tests.
- Confirm each suspected issue before reporting it. Do not manufacture coverage by listing every possible domain.
- Record positive observations only when they are specific and useful.
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 · 75 lines · 0 tokens per session scan A 35f8e0e222e9
deep_review_agent is an agent published in the GitHub repository GCWing/BitFun (1,871 stars, last pushed 2d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,128 tokens. 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.
Other agents, from other repositories
super-dev
Activate the Super Dev pipeline for research-first, commercial-grade project delivery. Use when user says /super-dev or super-dev: followed by a requirement.
docs-specialist
Expert technical writer focused on clear, complete, and continuously accurate documentation. Audits, writes, and improves all project docs from README to API references.
atomic-auditor
Final gate for a finished implementation. Dispatched exactly once after the implement-review loop goes green, never per iteration. Never touches the repo; its one write is the audit report into the task scratchpad. Audits the delivered work as a whole: cumulative spec compliance, cross-iteration coherence…
mathodology-problem-analyst
Use for contest problem decomposition, scoring criteria, constraints, variables, assumptions, and deliverable mapping.
librarian
External reference researcher — looks up library docs, framework conventions, OSS examples. Read-only, no memory injection. (Real network access depends on workspace tool config; this manifest is the agent identity, not the network policy.).
issue-tracker
Issues and PRDs for this repo live as GitHub issues. Use the gh CLI for all operations.