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/getpercept/percept/percept-summarizenpx skills add GetPercept/percept --skill percept-summarizegit clone --depth 1 https://github.com/GetPercept/perceptWrote 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/getpercept/percept/percept-summarize)<a href="https://agentmods.dev/skills/getpercept/percept/percept-summarize"><img src="https://agentmods.dev/badge/skills/getpercept/percept/percept-summarize.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.00448 |
| Opus 5 | $0.00000 | $0.00224 |
| Sonnet 5 | $0.00000 | $0.00090 |
| Haiku 4.5 | $0.00000 | $0.00045 |
Grade A, and why
percept-summarize 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 5d 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.
What it actually says
percept-summarize
Automatic conversation summaries with entity extraction and relationship mapping.
What it does
When a conversation ends (60 seconds of silence), Percept generates an AI-powered summary with extracted entities (people, companies, topics), action items, and relationship connections. Summaries are stored locally and searchable.
When to use
- User asks "what did we talk about?" or "summarize that meeting"
- User wants meeting notes or action items from a conversation
- Agent needs context from a recent conversation
Requirements
- percept-listen skill installed and running
- OpenClaw agent accessible via CLI (used for LLM summarization)
How it works
- Conversation ends (60s silence timeout)
- Percept builds a speaker-tagged transcript
- Sends transcript to OpenClaw for AI summarization
- Extracts entities (people, orgs, topics) and relationships
- Stores summary + entities in SQLite
- Entities linked via relationship graph (works_on, client_of, mentioned_with)
Entity resolution
5-tier cascade for identifying entities:
- Exact match (confidence 1.0)
- Fuzzy match (0.8) — handles typos, nicknames
- Contextual/graph (0.7) — uses relationship connections
- Recency (0.6) — recently mentioned entities ranked higher
- Semantic search (0.5) — vector similarity via LanceDB
Querying summaries
Summaries are searchable via the Percept dashboard (port 8960) or SQLite directly:
SELECT * FROM conversations WHERE summary LIKE '%action items%' ORDER BY end_time DESC;
Full-text search via FTS5:
SELECT * FROM utterances_fts WHERE utterances_fts MATCH 'project deadline';
Data retention
- Utterances: 30 days
- Summaries: 90 days
- Relationships: 180 days
- Speaker profiles: never expire
Links
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.
- 5d ago First seen · 61 lines · 0 tokens per session scan A c1c0706f9468
percept-summarize is a skill published in the GitHub repository GetPercept/percept (8 stars, last pushed 5mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 448 tokens. 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 2026-08-31.
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