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 Alex8791-cyber/cognithor --skill customgit clone --depth 1 https://github.com/Alex8791-cyber/cognithorWrote 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/alex8791-cyber/cognithor/custom)<a href="https://agentmods.dev/skills/alex8791-cyber/cognithor/custom"><img src="https://agentmods.dev/badge/skills/alex8791-cyber/cognithor/custom/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/alex8791-cyber/cognithor/custom"><img src="https://agentmods.dev/badge/skills/alex8791-cyber/cognithor/custom.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 | $0.00013 | $0.00499 |
| Opus 5.5 | $0.00005 | $0.00200 |
| Sonnet 5.5 | $0.00003 | $0.00100 |
| Haiku 4.5 | $0.00001 | $0.00050 |
Grade A, and why
code-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 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.
What it actually says
Code Review
When to Apply
When the user asks for a code review, quality check, or wants feedback on their code. Typical triggers: "Review this file", "Check the code quality", "What can I improve in src/...".
Prerequisites
- A file path or directory to review
- Optional: specific focus areas (security, performance, readability)
Steps
- Identify target — Ask which file or directory to review if not specified.
- Read the code — Use
read_fileto load the target. For directories, uselist_directoryfirst, then read key files. - Analyze — Use
analyze_codefor automated metrics (complexity, duplication, style). Note any issues. - Review manually — Check for:
- Security: injection, hardcoded credentials, path traversal
- Performance: unnecessary loops, missing caching, N+1 queries
- Readability: naming, function length, comments
- Architecture: coupling, single responsibility, error handling
- Summarize — Present findings grouped by severity (critical, warning, suggestion) with line references.
Output Format
## Code Review: {filename}
### Critical
- Line 42: SQL injection via string concatenation
### Warnings
- Line 15-30: Function too long (87 lines), consider splitting
- Line 55: Bare except clause catches SystemExit
### Suggestions
- Line 8: Consider using `pathlib.Path` instead of `os.path`
- Missing type hints on public API functions
### Metrics
- Cyclomatic complexity: 12 (moderate)
- Lines of code: 234
Known Pitfalls
- Do not modify files during review — this is a read-only operation.
- For large directories, focus on the most important files first.
- Be constructive — frame suggestions positively.
Quality Criteria
- All critical issues identified
- Specific line references provided
- Actionable suggestions (not vague "improve this")
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.
- 1mo ago First seen · 64 lines · 13 tokens per session scan A 36e42adfa878
code-review is a skill published in the GitHub repository Alex8791-cyber/cognithor (157 stars, last pushed 5d ago), licensed Apache-2.0. It adds 13 tokens to every session and 499 once invoked, about $0.0001 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 2026-09-03.
Other skills, from other repositories
coding-assistant
Help users write, review, and debug code.
forkmind
Use when debugging, comparing, or regression-testing LLM / agent calls — when the user wants to capture LLM traffic, see a conversation as a branchable DAG, fork an alternative prompt or model from a past turn, or pin good outputs as baselines to catch drift. ForkMind is local-first (no cloud, no account) and proxies…
interrogate
Use for "interrogate", "adversarial review", "multi-model review", "challenge this", "stress test this code", "find blind spots", or "tear this apart". Multiple LLM reviewers challenge changes from independent angles.
blast-radius
Find what a change could break somewhere else before it ships, beyond the diff, and prove the one fact it's safe because of by running real code instead of writing it up. Use for 'blast radius of X', 'what could this break', or reviewing a small diff you don't trust.
code-review
Reviews code for bugs, security issues, and best practices.
resolve-review-pr
Review an existing fix PR using the audited repro and full diff, preserving branch and approval rules.