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 skills add edwin-hao-ai/Awareness-SDK --skill donegit clone --depth 1 https://github.com/edwin-hao-ai/Awareness-SDKWrote 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/edwin-hao-ai/awareness-sdk/done)<a href="https://agentmods.dev/skills/edwin-hao-ai/awareness-sdk/done"><img src="https://agentmods.dev/badge/skills/edwin-hao-ai/awareness-sdk/done.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 5.1 | $0.00013 | $0.02327 |
| Opus 5 | $0.00006 | $0.01163 |
| Sonnet 5 | $0.00003 | $0.00465 |
| Haiku 4.5 | $0.00001 | $0.00233 |
Grade B, and why
done scanned grade B with 2 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 8d 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.
Sends data to an external URLmediumData exfiltration
A POST to an outside endpoint may be telemetry or may be exfiltration; either way the mod talks to somewhere, and you should know where.
curl -s -X POST http://localhost:37800/mcp -H "Content-Type: application/json" -d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"awareness_record","arguments":{"action":"remember_batch","items":[{"conte Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s -X POST http://localhost:37800/mcp -H "Content-Type: application/json" -d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"awareness_record","arguments":{"action":"remember_batch","items":[{"conte How it starts
The opening of the file, as written. The whole thing — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.
End the current Awareness Memory session.
How to call Awareness tools
Try MCP tools first (awareness_record).
If MCP tools are NOT available, use Bash to call the local daemon HTTP API directly:
curl -s -X POST http://localhost:37800/mcp -H "Content-Type: application/json" -d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"awareness_record","arguments":{"action":"remember_batch","items":[{"content":"..."}],"insights":{"knowledge_cards":[...],"action_items":[...],"risks":[...],"completed_tasks":[...]}}}}'
The response is JSON-RPC: result.content[0].text contains the tool output as JSON string.
Steps
-
Gather context about this session, then extract structured insights — salience-aware, not greedy:
Philosophy (distilled essence, not raw logs): your job is NOT "generate a card for every turn" — it is "identify what's worth recalling in 6 months on a fresh project". Empty
knowledge_cards: []is a first-class answer when the session was just tool testing or framework metadata.- knowledge_cards: only genuine insights.
- When to extract:
- knowledge_cards: only genuine insights.
- The user made a decision — chose X over Y, with a stated reason
- A non-obvious bug was fixed — symptom + root cause + fix + how to avoid recurring
- A workflow / convention was established — ordered steps, preconditions, gotchas
- The user stated a preference or hard constraint — "I prefer X", "never do Y"
- A pitfall was encountered and a workaround found — trigger + impact + avoidance
- An important fact about the user or project surfaced for the first time
- **When NOT to extract:**
- Agent framework metadata: content beginning with
Sender (untrusted metadata),turn_brief,[Operational context metadata ...],[Subagent Context], or wrapped insideRequest:/Result:/Send:envelopes that only carry such metadata. Strip those wrappers mentally and judge what remains. - Greetings / command invocations: "hi", "run tests", "save this", "try again".
- "What can you do" / AI self-introduction turns.
- Code restatement: code itself lives in git; only extract the lesson if one exists.
- Test / debug sessions where the user is verifying the tool works (including tests of awareness_record / awareness_recall themselves). A bug fix in those tools IS worth extracting as problem_solution; a raw "let me test if recall works" turn is not.
- Transient status / progress updates — "building...", "retrying...", "✅ done".
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.
- 8d ago First seen · 169 lines · 13 tokens per session scan B e5c53bdd3b72
done is a skill published in the GitHub repository edwin-hao-ai/Awareness-SDK (9 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 13 tokens to every session and 2,327 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it B with 2 findings (sends data to an external url, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
weekly-digests
Generate a serial week-by-week narrative digest of a project's full claude-mem timeline. Splits the timeline into per-ISO-week files, then runs one consecutive subagent per week — each receiving the prior week's carry-forward block — to produce one chapter per ISO week of data. Use when asked for "weekly digests"…
cloud-sync
Set up or check claude-mem cloud sync with cmem.ai Pro. Use when the user says "set up cloud sync", "sync my memories", "cmem pro", "cloud backup", "sync status", or wants their memory database backed up or synced to their cmem.ai account.
how-it-works
Explain how claude-mem captures observations, when memory injection kicks in, and where data lives. Use when the user asks "how does claude-mem work?" or "what is this thing doing?".
openclaw
This guide walks through setting up the claude-mem plugin on an OpenClaw gateway. By the end, your agents will have persistent memory across sessions via system prompt context injection, and optionally a real-time observation feed streaming to a messaging channel.
learn-codebase
Prime a codebase by reading every source file in full. Use when starting work on a new or unfamiliar project, or when the user asks to "learn the codebase", "read the codebase", "prime", or "get up to speed".
host-observer
Use this when fulfilling claude-mem observer jobs on Grok Bot: reply only skipsummary or one full observation XML, never prose.