Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/ucsandman/dashclaw-skillsnpx agentmods add skills/ucsandman/dashclaw-skills/instrument-agentWrote 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/ucsandman/dashclaw-skills/instrument-agent)<a href="https://agentmods.dev/skills/ucsandman/dashclaw-skills/instrument-agent"><img src="https://agentmods.dev/badge/skills/ucsandman/dashclaw-skills/instrument-agent/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ucsandman/dashclaw-skills/instrument-agent"><img src="https://agentmods.dev/badge/skills/ucsandman/dashclaw-skills/instrument-agent.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00019 | $0.02180 |
| Opus 5 | $0.00010 | $0.01090 |
| Sonnet 5 | $0.00004 | $0.00436 |
| Haiku 4.5 | $0.00002 | $0.00218 |
Grade A, and why
instrument-agent scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -sf "$DASHCLAW_BASE_URL/api/health" | jq '.status' This is a copy
100% identical to instrument-agent — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 313 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Instrument Your Agent with DashClaw
Help developers add DashClaw governance to any AI agent. Walk through the 4-step governance loop with working code.
The 4-Step Governance Loop
Every governed decision follows this deterministic flow:
1. Guard → "Can I do this?" (POST /api/guard)
2. Record → "I am doing this." (POST /api/actions)
3. Verify → "I believe this is true." (POST /api/assumptions)
4. Outcome → "This was the result." (PATCH /api/actions/:id)
Step 0: Install & Initialize
Node.js
npm install dashclaw
import { DashClaw } from 'dashclaw';
const claw = new DashClaw({
baseUrl: process.env.DASHCLAW_BASE_URL,
apiKey: process.env.DASHCLAW_API_KEY,
agentId: 'my-agent'
});
Python
pip install dashclaw
from dashclaw import DashClaw
claw = DashClaw(
base_url=os.environ["DASHCLAW_BASE_URL"],
api_key=os.environ["DASHCLAW_API_KEY"],
agent_id="my-agent"
)
Step 0.5: Session Lifecycle (Optional but Recommended)
Create a session to track the full lifecycle of your agent's work. Sessions enable monitoring, recovery, and continuity across restarts.
// Create a session at agent startup
const session = await fetch(`${baseUrl}/api/sessions`, {
method: 'POST',
headers: { 'Authorization': `Bearer ${apiKey}`, 'Content-Type': 'application/json' },
body: JSON.stringify({ agent_id: 'my-agent', metadata: { task: 'deploy-pipeline' } })
}).then(r => r.json());
// Report status during execution
await fetch(`${baseUrl}/api/sessions/${session.id}`, {
method: 'PATCH',
headers: { 'Authorization': `Bearer ${apiKey}`, 'Content-Type': 'application/json' },
body: JSON.stringify({ status: 'running', checkpoint: { step: 'guard-check' } })
});
Session lifecycle is optional — all governance loop steps work without it — but it provides visibility into long-running agent tasks and enables automatic recovery when sessions are interrupted.
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.
- 10d ago First seen · 313 lines · 19 tokens per session scan A b3000fbce893
instrument-agent is a skill published in the GitHub repository ucsandman/dashclaw-skills (2 stars, last pushed yesterday), licensed MIT. It adds 19 tokens to every session and 2,180 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 100% identical to instrument-agent, differing in 0 lines, and is treated as a copy.
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