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/studioKjm/ai-harness-templatenpx agentmods add commands/studiokjm/ai-harness-template/observeWrote 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/commands/studiokjm/ai-harness-template/observe)<a href="https://agentmods.dev/commands/studiokjm/ai-harness-template/observe"><img src="https://agentmods.dev/badge/commands/studiokjm/ai-harness-template/observe.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 | $0.00025 | $0.01702 |
| Opus 5 | $0.00013 | $0.00851 |
| Sonnet 5 | $0.00005 | $0.00340 |
| Haiku 4.5 | $0.00003 | $0.00170 |
Grade A, and why
observe 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 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.
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 — 190 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/observe — Define Observability Before Implementation
Telemetry is a design output, not a retrofit.
When to use
- New story with performance or availability AC
- New endpoint added
- Logic-layer module touches business-critical operation
- Before writing implementation code (so signals are designed in, not bolted on)
Usage
/observe define <slug> --target-kind story|feature|endpoint|module --target-ref REF
/observe list-specs [--status draft|defined|instrumented|measuring|review-due]
/observe show-spec <spec-id>
/observe instrument <spec-id> # defined → instrumented (after coverage verified)
/observe measure <spec-id> # instrumented → measuring (production data flowing)
/observe coverage <spec-id> --files F1 [F2 ...] --symbols S1 [...]
/observe add-metric <spec-id> --name N --type counter|gauge|histogram|summary --question "..."
/observe add-log <spec-id> --event E --level info|warn|error --field F1 [--field F2:pii]
For SLOs, see /observe-slo.
State machine
[draft] → [defined] → [instrumented] → [measuring] → [review-due]
↑ ↓
└──────────────┘ (90d cycle)
| State | Meaning | Move when |
|---|---|---|
| draft | Spec being written | First metric/log/trace added (auto) |
| defined | Spec complete | Code emits matching telemetry |
| instrumented | Code verified | Production data flowing |
| measuring | SLOs computable | 90 days passed (review-due triggered) |
| review-due | Time for refresh | After review, back to measuring |
Instructions
Step 1 — Locate the script
.harness/methodologies/observability-first/scripts/obs.py
Step 2 — Run the requested subcommand
# Define spec for a story
python3 .harness/methodologies/observability-first/scripts/obs.py \
define refund-api \
--target-kind story \
--target-ref st-2026-04-30-refund \
--description "Refund API observability"
# Add metrics one at a time
python3 .harness/methodologies/observability-first/scripts/obs.py \
add-metric obs-2026-04-30-refund-api \
--name "refund_request_total" \
--type counter \
--labels "method" "status_code" "merchant_id" \
--question "How many refund requests, segmented by outcome?"
python3 .harness/methodologies/observability-first/scripts/obs.py \
add-metric obs-2026-04-30-refund-api \
--name "refund_processing_duration_ms" \
--type histogram \
--labels "merchant_id" \
--question "Distribution of refund processing time" \
--unit "milliseconds"
# Add log events
python3 .harness/methodologies/observability-first/scripts/obs.py \
add-log obs-2026-04-30-refund-api \
--event "refund.requested" \
--level info \
--field "merchant_id" \
--field "amount" \
--field "user_id:pii"
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 · 190 lines · 25 tokens per session scan A 63393ae0bd24
observe is a command published in the GitHub repository studioKjm/ai-harness-template (43 stars, last pushed 3mo ago), licensed MIT. It adds 25 tokens to every session and 1,702 once invoked, about $0.0001 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 2026-08-30.
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