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/liuyihey/agent-engineering/chain-instrumentation-debugnpx skills add LiuYihey/Agent-Engineering --skill chain-instrumentation-debuggit clone --depth 1 https://github.com/LiuYihey/Agent-EngineeringWrote 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/liuyihey/agent-engineering/chain-instrumentation-debug)<a href="https://agentmods.dev/skills/liuyihey/agent-engineering/chain-instrumentation-debug"><img src="https://agentmods.dev/badge/skills/liuyihey/agent-engineering/chain-instrumentation-debug.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 | $0.00050 | $0.01256 |
| Opus 5 | $0.00025 | $0.00628 |
| Sonnet 5 | $0.00010 | $0.00251 |
| Haiku 4.5 | $0.00005 | $0.00126 |
Grade A, and why
chain-instrumentation-debug 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.
How it starts
The opening of the file, as written. The whole thing — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Chain Instrumentation Debugging
A systematic methodology for diagnosing full-stack bugs by instrumenting the data flow with console output at critical transition points, then reading the logs to pinpoint exactly where state diverges from expectations.
When to Invoke
- Bug is reproducible but root cause is unclear from code reading
- User reports inconsistent behavior ("sometimes works, sometimes doesn't")
- UI doesn't reflect server state, or vice versa
- State appears to revert silently after an action
- Race conditions or timing-related bugs
- "Works on refresh but not on first interaction"
- Any bug where you need to understand the actual runtime data flow vs. the expected flow
Methodology
Step 1: Map the Data Flow
Before writing any logs, trace the complete request/response chain on paper:
User Action → Client Handler → Optimistic Update (set) → API Call →
Server Endpoint → DB Mutation → Response Serialization →
Client Response Handler → State Reconciliation (applyState) → UI Re-render
Identify every point where a value could change or be lost.
Step 2: Insert Instrumentation at Every Transition
Add console.log at every state transition in the chain. Use a consistent prefix tag so logs can be filtered.
Client-side action (example pattern):
myAction: (id) => {
console.log('[myAction] start, input:', id, 'current state:', get().myField)
// Optimistic update
set({ myField: newValue })
console.log('[myAction] after optimistic set, myField:', get().myField)
// API call
api.myAction(id).then((response) => {
console.log('[myAction] API response, myField:', response.myField, 'other:', response.otherField)
// State reconciliation
applyState(response)
console.log('[myAction] after applyState, myField:', get().myField)
}).catch((err) => {
console.error('[myAction] failed:', err)
})
},
Server-side endpoint (example pattern):
app.post('/api/myAction', (req, res) => {
const session = getSession(req.cookies.sid)!
console.log('[endpoint] request received, session field:', session.myField)
doMutation(session.id, req.body.value)
console.log('[endpoint] after mutation, session field (in-memory):', session.myField)
const fresh = getSession(req.cookies.sid)!
console.log('[endpoint] fresh from DB, session field:', fresh.myField)
res.json(buildState(fresh))
})
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 · 135 lines · 50 tokens per session scan A 549f60194f25
chain-instrumentation-debug is a skill published in the GitHub repository LiuYihey/Agent-Engineering (5 stars, last pushed 1mo ago), licensed MIT. It adds 50 tokens to every session and 1,256 once invoked, about $0.0003 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 2026-08-31.
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