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 closedloop-ai/claude-plugins --skill present-localgit clone --depth 1 https://github.com/closedloop-ai/claude-pluginsWrote 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/closedloop-ai/claude-plugins/present-local)<a href="https://agentmods.dev/skills/closedloop-ai/claude-plugins/present-local"><img src="https://agentmods.dev/badge/skills/closedloop-ai/claude-plugins/present-local/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/closedloop-ai/claude-plugins/present-local"><img src="https://agentmods.dev/badge/skills/closedloop-ai/claude-plugins/present-local.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 | $0.00136 | $0.03721 |
| Opus 5.5 | $0.00054 | $0.01488 |
| Sonnet 5.5 | $0.00027 | $0.00744 |
| Haiku 4.5 | $0.00014 | $0.00372 |
Grade A, and why
present-local 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.
How it starts
The opening of the file, as written. The whole thing — 304 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Local-Mode Code Review Presenter
This skill is the canonical presenter for /code-review local mode at stage_29_present. It is split out of commands/start.md so the orchestration spine stays lean; the orchestrator invokes it when MODE=local and stage_29_present is reached.
Gate A hygiene-only early-exit is not in this skill. That path is mode-agnostic (fires for both MODE=local and MODE=github) and the hygiene presentation lives in start.md adjacent to the Gate A definition — the orchestrator handles it inline, not via this skill.
Local Mode: Present Results (stage_29_present, MODE=local)
Mark "Present findings by severity" in_progress.
Render the report below. Three text conventions run through this template, so it stays clear which lines you emit verbatim and which are directions to you:
##section headers (Repo Hygiene, BLOCKING, …) are the report's own structure — emit them as written, with the live[count]substituted.- Fenced code blocks are card/footer templates — instantiate one per finding (or per stat block), substituting the
{PLACEHOLDER}and[bracketed]fields. [bracketed]and{BRACED}tokens are instructions or substitution points, never literal output.
Output in this format:
# Code Review Results
**Scope:** [staged/branch/files]
**Files Reviewed:** [count]
Reviewer Fleet block (PLN-725 Phase 9 / v2.23.0). Do NOT write the Reviewers / Model Routing / Fleet lines from scratch. Run the canonical renderer and embed its output verbatim:
python <HELPERS> render-fleet-summary --cr-dir <CR_DIR>
The renderer consumes <CR_DIR>/spawn.json (sections: spec — intended fleet from stage_19b; verification — runtime tally from stage_20b; route — model assignments from Gate B). The output is a deterministic markdown block of 2–9 lines — 2–4 for the core Reviewers / Model Routing / Fleet section, plus up to 5 conditional note bullets — covering:
- Reviewers — the actual fleet that spawned (with the rule-resolved vs LLM-proposed split surfaced on domain critics so the operator can tell operator-configured coverage apart from one-off LLM proposals).
- Model Routing — the per-agent model assignments derived from
spawn.json.route. - Fleet —
N intended | N ran | N required missingso the operator sees the runtime tally without scrolling to Verifier Stats. - Notes (conditional) — only emitted when the run was non-default: BLOCKING-sanitized arbitration, missing required reviewers (runtime crash), BHA partitions capped by post-arbitrate budget, PLN-723 deferral, malformed-plan required skips. Each note is a single bullet so the section stays scannable; multiple notes can fire together.
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 Changed 50577d286074
- 1mo ago First seen · 304 lines · 136 tokens per session scan A d60942b3692f
present-local is a skill published in the GitHub repository closedloop-ai/claude-plugins (122 stars, last pushed today), licensed Apache-2.0. It adds 136 tokens to every session and 3,721 once invoked, about $0.0005 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 2026-08-30.
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code-review-excellence
Master effective code review practices to provide constructive feedback, catch bugs early, and foster knowledge sharing while maintaining team morale. Use when reviewing pull requests, establishing review standards, or mentoring developers.
github
GitHub via gh CLI: PRs, issues, reviews, repos, auth.
make-pr-easy-to-review
Prepare PRs for review by cleaning noisy history, improving PR descriptions, and adding reviewer guidance without changing code behavior. Use for "make this easy to review", "tidy this PR", "clean up commits", or "annotate the diff".
gitnexus-explorer
Serve an interactive codebase knowledge graph web UI.