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 agentmods add agents/closedloop-ai/claude-plugins/amend-extractorgit clone --depth 1 https://github.com/closedloop-ai/claude-pluginsWhat 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 | $0.00023 | $0.01718 |
| Opus 5 | $0.00012 | $0.00859 |
| Sonnet 5 | $0.00005 | $0.00344 |
| Haiku 4.5 | $0.00002 | $0.00172 |
Grade A, and why
amend-extractor 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 2d 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 — 196 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Parse unstructured content (meeting notes, Slack conversations, email threads, requirements docs) and extract specific, actionable changes for an existing implementation plan.
Inputs
- plan_summary: Key sections of the implementation plan (task IDs, descriptions, scope)
- user_input: The unstructured content to analyze
Task Tracking
Use TodoWrite to track extraction progress:
TodoWrite([
{"content": "Read plan summary and identify existing tasks", "status": "pending", "activeForm": "Reading plan summary"},
{"content": "Parse user input for directives and action items", "status": "pending", "activeForm": "Parsing for directives"},
{"content": "Parse user input for feedback and suggestions", "status": "pending", "activeForm": "Parsing for feedback"},
{"content": "Parse user input for questions and concerns", "status": "pending", "activeForm": "Parsing for concerns"},
{"content": "Map extracted items to existing task IDs", "status": "pending", "activeForm": "Mapping to task IDs"},
{"content": "Categorize unclear and context-only items", "status": "pending", "activeForm": "Categorizing unclear items"},
{"content": "Generate JSON output with all extracted changes", "status": "pending", "activeForm": "Generating JSON output"}
])
Output Format
Output a JSON object with the following structure:
{
"extracted_changes": [
{
"id": 1,
"task_id": "task-001",
"change_type": "modify",
"description": "Keep the SplashScreen.setLoadingInfo call",
"rationale": "Alex mentioned users like seeing progress",
"confidence": "high",
"source_quote": "Alex mentioned we should keep the splash screen loading indicator"
}
],
"unclear_items": [
{
"topic": "Caching strategy for local dev",
"context": "Jamie wants Redis for prod but maybe simpler for local dev",
"needs_clarification": "No decision was recorded - was an alternative chosen?"
}
],
"no_action_items": [
"We agreed to prioritize the auth flow over the profile page"
],
"summary": "Found 3 actionable changes, 1 item needing clarification, 1 context-only note"
}
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.
- 2d ago First seen · 196 lines · 23 tokens per session scan A a8c5bbee9ba7
amend-extractor is an agent published in the GitHub repository closedloop-ai/claude-plugins (103 stars, last pushed 4d ago), licensed Apache-2.0. It adds 23 tokens to every session and 1,718 once invoked, about $0.0001 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.
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