Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add danielraffel/generous-corp-marketplace/plugin install chainerWrote 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/commands/danielraffel/generous-corp-marketplace/suggest)<a href="https://agentmods.dev/commands/danielraffel/generous-corp-marketplace/suggest"><img src="https://agentmods.dev/badge/commands/danielraffel/generous-corp-marketplace/suggest/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/commands/danielraffel/generous-corp-marketplace/suggest"><img src="https://agentmods.dev/badge/commands/danielraffel/generous-corp-marketplace/suggest.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 | $0.00009 | $0.01670 |
| Opus 5 | $0.00005 | $0.00835 |
| Sonnet 5 | $0.00002 | $0.00334 |
| Haiku 4.5 | $0.00001 | $0.00167 |
Grade A, and why
suggest scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 272 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Suggest Command
Intelligently suggest plugins and chains based on what you want to do.
User Communication: Be direct. Show suggestions immediately without explaining the matching process.
Usage
/chainer:suggest "<what you want to do>"
Examples
/chainer:suggest "plan and implement a login feature"
/chainer:suggest "review my code for security issues"
/chainer:suggest "create an isolated workspace for testing"
Instructions for Claude
When user invokes this command:
Step 1: Parse Input
Extract the description from the user's message (everything after /chainer:suggest).
If no description provided, show error:
❌ Please provide a description of what you want to do
Examples:
/chainer:suggest "plan and build a feature"
/chainer:suggest "review my code"
/chainer:suggest "create isolated workspace"
Step 2: Load Registry
Read the plugin registry to get plugin metadata and keywords:
const registryPath = '${CLAUDE_PLUGIN_ROOT}/registry/plugins.yaml';
const registryContent = Read(registryPath);
const registry = parseYaml(registryContent);
If registry doesn't exist or can't be parsed:
❌ Could not load plugin registry
Expected location: ${CLAUDE_PLUGIN_ROOT}/registry/plugins.yaml
Step 3: Keyword Matching
Tokenize description:
- Convert to lowercase
- Split on spaces and punctuation
- Remove common stop words: "the", "a", "an", "and", "or", "to", "for", "in", "on", "with", "my", "i", "want", "need"
- Extract meaningful words (2+ characters)
Score each plugin:
const scores = {};
const matchDetails = {};
for (const [pluginName, plugin] of Object.entries(registry.plugins)) {
if (!plugin.keywords) continue;
let score = 0;
const matches = [];
for (const keyword of plugin.keywords) {
const keywordLower = keyword.toLowerCase();
// Direct match (highest score)
if (descriptionTokens.includes(keywordLower)) {
score += 10;
matches.push(keyword);
}
// Partial match (medium score)
else if (descriptionTokens.some(t => t.includes(keywordLower) || keywordLower.includes(t))) {
score += 5;
matches.push(keyword);
}
}
if (score > 0) {
scores[pluginName] = score;
matchDetails[pluginName] = matches;
}
}
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.
- 10d ago First seen · 272 lines · 9 tokens per session scan A 8cf75275f405
suggest is a command published in the GitHub repository danielraffel/generous-corp-marketplace (11 stars, last pushed 11d ago), licensed MIT. It adds 9 tokens to every session and 1,670 once invoked, about $0.0000 per session on Opus 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.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.