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 rhein1/fable5-codex --skill fable-deep-reviewgit clone --depth 1 https://github.com/rhein1/fable5-codexWrote 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/rhein1/fable5-codex/fable-deep-review)<a href="https://agentmods.dev/skills/rhein1/fable5-codex/fable-deep-review"><img src="https://agentmods.dev/badge/skills/rhein1/fable5-codex/fable-deep-review/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/rhein1/fable5-codex/fable-deep-review"><img src="https://agentmods.dev/badge/skills/rhein1/fable5-codex/fable-deep-review.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 | $0.00054 | $0.00761 |
| Opus 5.5 | $0.00022 | $0.00304 |
| Sonnet 5.5 | $0.00011 | $0.00152 |
| Haiku 4.5 | $0.00005 | $0.00076 |
Grade A, and why
fable-deep-review 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 — 54 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fable Deep Review
Review like a senior engineer validating a change, not like a summarizer. Default to no edits unless the user asks to address findings.
For ECF-style governed runs, use ../../references/ecf-run-contract.md. For large or high-risk reviews, use real Codex subagents when the runtime exposes a subagent tool and the user has not opted out; treat repo-wide, cross-package, security/privacy/money/data/API, migration, release, or deep-review requests as large by default. Otherwise run single-agent multi-lens and say why no subagents were used. For bot-parseable PR review output, use ../../templates/fable-review-contract.md.
Use the centralized capability-aware profiles in ../../references/model-support.md and ../../config/model-profiles.json. Keep model and reasoning selections configurable, preserve explicit overrides, and record the requested and actual coordinator model and effort and requested and actual worker model plus any fallback. Request parallel delegation only for useful disjoint lenses; report single-agent multi-lens when no workers ran. The coordinator owns synthesis and final verification. Never claim a model, effort or subagent ran without runtime evidence.
Workflow
- Identify the review target: PR, branch, diff, commit range, or files.
- Restate authority boundaries and declare the ECF run mode:
multi-agentonly with real spawned subagents, otherwisesingle-agent multi-lens. - Read repo instructions and changed files completely before judging them.
- Inspect callers, importers, tests, docs, migrations, and generated/runtime surfaces touched by the change.
- Build a change map: intent, modified behavior, affected contracts, and risk areas.
- Generate candidate regressions from multiple lenses:
- API/contract compatibility
- data/schema and migration safety
- concurrency/idempotency/retry behavior
- auth, privacy, and permission checks
- user-facing/runtime behavior
- test adequacy and missing cases
- Verify each candidate. Prefer a small command or source trace that proves the issue.
- Report only actionable findings. Put open questions after findings.
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 09b539cfd993
- 1mo ago Changed fd921ed9c061
- 2mo ago First seen · 54 lines · 54 tokens per session scan A 2d3082127c1e
fable-deep-review is a skill published in the GitHub repository rhein1/fable5-codex (4 stars, last pushed 4d ago), licensed MIT. It adds 54 tokens to every session and 761 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 2026-08-31.
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skeptic
Adversarial reviewer for Bug Hunter. Challenges each behavioral finding against actual code, checks defenses, and preserves unresolved cases for independent review.
commit-security-scan
Scan code changes for security vulnerabilities using Bug Hunter-native artifacts and STRIDE context. Use whenever the user asks for PR security review, commit-diff scanning, staged-change security checks, branch-comparison security review, or pre-merge security analysis of changed code.