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 cyborg-garden/hermes-agent-mt --skill sdlc-reviewgit clone --depth 1 https://github.com/cyborg-garden/hermes-agent-mtWrote 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/cyborg-garden/hermes-agent-mt/sdlc-review)<a href="https://agentmods.dev/skills/cyborg-garden/hermes-agent-mt/sdlc-review"><img src="https://agentmods.dev/badge/skills/cyborg-garden/hermes-agent-mt/sdlc-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/cyborg-garden/hermes-agent-mt/sdlc-review"><img src="https://agentmods.dev/badge/skills/cyborg-garden/hermes-agent-mt/sdlc-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 | $0.00015 | $0.01861 |
| Opus 5.5 | $0.00006 | $0.00744 |
| Sonnet 5.5 | $0.00003 | $0.00372 |
| Haiku 4.5 | $0.00002 | $0.00186 |
Grade A, and why
sdlc-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 today.
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.
This is a copy
100% identical to sdlc-review — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SDLC Review Skill
Independently verify work handed from a Kanban implementation run to the review lane, then approve it, request changes, or escalate. This skill reviews the deliverable and its evidence; it does not take over the implementer's work.
When to Use
Use this skill when all of the following are true:
- the dispatcher spawned you for a task claimed from the
reviewlane; - an implementer submitted a
review_requestedhandoff; - the task needs an independent verdict before it can be completed.
Do not use it for a separate downstream review card. A downstream card is ordinary implementation work with a review-oriented specification and completes through its own lifecycle.
Prerequisites
- A Kanban worker context with the current task and run identifiers.
- Native Kanban tools:
kanban_show,kanban_comment,kanban_complete,kanban_request_changes, andkanban_block. - Workspace access through
read_file,search_files, andterminalwhen the deliverable is code. - The task's original specification, acceptance criteria, handoff summary, and prior run history must be available through
kanban_show.
How to Run
This skill is loaded automatically by the review dispatcher. Start with kanban_show before inspecting files or choosing a verdict.
- Read the task specification and the latest
review_requestedhandoff. - Inspect the actual deliverable and run relevant verification.
- Choose exactly one verdict: approve, request changes, or escalate.
- Record concrete evidence in the terminal Kanban transition.
Quick Reference
| Verdict | When | Final action |
|---|---|---|
| Approve | Acceptance criteria and verification pass | kanban_complete |
| Request changes | Correctable implementation defects remain | kanban_comment, then kanban_request_changes |
| Escalate | A human decision or external prerequisite is required | kanban_block |
A requested-changes transition returns the task to its original implementer. When that implementer requests review again without naming a reviewer, the persisted reviewer provenance routes the re-review back to the same reviewer profile.
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.
- today First seen · 182 lines · 15 tokens per session scan A ce90e8a3e145
sdlc-review is a skill published in the GitHub repository cyborg-garden/hermes-agent-mt (14 stars, last pushed yesterday), licensed MIT. It adds 15 tokens to every session and 1,861 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to sdlc-review, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
github-issue-pr-attribs
Standardize GitHub ISSUE and PR metadata (Assignee, Reviewer, Labels, Project, Milestone, Development, Relationship) and ship a strong, well-structured code-review template. Includes ready-to-use issue forms, PR template, and a severity/rubric review model.
mnemosyne
Persistent cross-session memory via Mnemosyne — store, recall, and consolidate facts, preferences, and context.
mnemosyne-memory-override
Hard rule override that forces Mnemosyne for all durable memory storage. The legacy memory tool is DEPRECATED for user preferences, credentials, and project conventions. Use memory ONLY for ephemeral session state.
review-workflow
A review workflow for GitHub pull requests and issues, including judging whether changes can be merged or an issue can be closed. It requires read-only review, REST API access, and testing changes in practice.
bt6-queue-audit
Audit the full pull-request and issue queue of a BT6 research or support repository, classifying readiness, evidence risk, and next action without mutating tracker state.
closed-loop-delivery
Use when a coding task must be completed against explicit acceptance criteria with minimal user re-intervention across implementation, review feedback, deployment, and runtime verification.