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 skills/ghostwright/phantom/overheardnpx skills add ghostwright/phantom --skill overheardgit clone --depth 1 https://github.com/ghostwright/phantomWhat 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 | $0.00024 | $0.01138 |
| Opus 5 | $0.00012 | $0.00569 |
| Sonnet 5 | $0.00005 | $0.00228 |
| Haiku 4.5 | $0.00002 | $0.00114 |
Grade A, and why
overheard scanned grade A with 1 finding 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.
Strips warnings and disclaimerslowAnti-refusal
Omitting safety caveats hides risk from the user and is a common jailbreak preamble.
- Never moralize about broken commitments. The tone is "here is what I saw", not "here is what you should do". Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.
How it starts
The opening of the file, as written. The whole thing — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overheard: the promises audit
Goal
Find promises the user made in conversations (in Slack, in email, in messages) that start with phrases like "I'll send", "I will", "let me get back to you", "by Friday", "next week", "I'll follow up", and check whether there is evidence in subsequent memory of follow-through. Surface the gaps as a promises audit, with context for each and a draft action.
The shadow backlog most people carry. The user does not need to be reminded of every forgotten ping; they need the two or three that actually matter.
Steps
1. Pull the last 14 days of memory
Call mcp__phantom-reflective__phantom_memory_search with query: "I will", days_back: 14, limit: 30. Then call it again with query: "let me" and merge. Then query: "follow up", and merge. Then query: "by Friday" and merge. The merge should deduplicate by episode id.
Success criteria: you have a pool of recent episodes that contain commitment language.
2. Extract candidate commitments
For each episode, read the detail and pull out phrases where the user was the one making the commitment, not receiving one. Common patterns:
- "I will send you..."
- "Let me get back to you on..."
- "I'll check with and let you know."
- "By Friday I'll have..."
- "Next week I'll..."
- "I'll follow up on..."
Skip commitments that are clearly to yourself, to the agent, or to no one in particular ("I will make more coffee"). Skip commitments that the user has already negated ("I thought I'd send X but actually I'm not going to").
Success criteria: you have a list of 5-15 candidate commitments with the original context, who the promise was to (if known), and when it was made.
3. Check for follow-through
For each candidate, search memory again for follow-up evidence. The query is a short paraphrase of what was promised. Example: if the commitment was "I'll send Anna the revised doc by Friday", search for "send Anna doc" or "revised doc Anna" with days_back: 14.
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 · 86 lines · 24 tokens per session scan A 648928cf3e5c
overheard is a skill published in the GitHub repository ghostwright/phantom (1,463 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 24 tokens to every session and 1,138 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (strips warnings and disclaimers). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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