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 commands/xsaven/vector-memory-mcp/statusgit clone --depth 1 https://github.com/Xsaven/vector-memory-mcpWhat 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 | $0.00016 | $0.01322 |
| Opus 5 | $0.00008 | $0.00661 |
| Sonnet 5 | $0.00003 | $0.00264 |
| Haiku 4.5 | $0.00002 | $0.00132 |
Grade A, and why
task:status scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Iron Rules
Always-summary (MEDIUM)
Always show summary at the end of output
- why: Provides quick overview regardless of query type
- on_violation: Append: "Summary: {total} total, {
completed}completed({pct}%)"
Input
STORE-AS($RAW_INPUT = $ARGUMENTS) STORE-AS($STATUS_QUERY = {query extracted from $RAW_INPUT})
Parse arguments
Parse captured input to detect query type
detect: STORE-AS($QUERY_TYPE = {detect: time|status|group|specific|default from $RAW_INPUT})- Time filters: yesterday, today, this week, this month, last 7 days, last N days
- Status filters:
completed,pending,in_progress,stopped - Grouping: by priority, by tags, by parent
- Specific: parent_id=N, task_id=N
- Empty: default overview statistics
Route query
Route to handler based on detected query type
route: IF(STORE-GET($RAW_INPUT) is empty) → Execute: default-stats workflow → ELSE → Execute: custom-query workflow based on STORE-GET($QUERY_TYPE) → END-IF
Default stats
Default overview when STORE-GET($RAW_INPUT) is empty
fetch: mcp__vector-task__task_stats('{}')store: STORE-AS($STATS = statistics response)format: Display: Total tasks: {total} → Display: Pending: {pending} | In Progress: {in_progress} | Completed: {completed} → Calculate: completion_pct = (completed/ total) * 100 → Render: [####------] {completion_pct}%
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.
- yesterday First seen · 95 lines · 16 tokens per session scan A 68538c112ccf
task:status is a command published in the GitHub repository Xsaven/vector-memory-mcp (0 stars, last pushed 6mo ago), licensed MIT. It adds 16 tokens to every session and 1,322 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 2026-09-01.
Other commands, from other repositories
git
Git operations with intelligent commit messages and workflow optimization.
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.