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 commands/bar181/bar-observatory/bar-interpretgit clone --depth 1 https://github.com/bar181/bar-observatoryWrote 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/bar181/bar-observatory/bar-interpret)<a href="https://agentmods.dev/commands/bar181/bar-observatory/bar-interpret"><img src="https://agentmods.dev/badge/commands/bar181/bar-observatory/bar-interpret.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 | $0.00019 | $0.00282 |
| Opus 5 | $0.00010 | $0.00141 |
| Sonnet 5 | $0.00004 | $0.00056 |
| Haiku 4.5 | $0.00002 | $0.00028 |
Grade A, and why
bar-interpret 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 4d 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
$ARGUMENTS
Generate the optional, LLM-written self-improvement report — a narrative writeup layered on top of the deterministic facts, never mutating them.
- Determine the audience from
$ARGUMENTS:engineering(technical depth) orexecutive(cost/risk/value framing).--audienceis required by the CLI — there is no default; ask the user which one they want if$ARGUMENTSdoesn't say. - Determine the database path the same way as
/bar-report(default.bar/ambient.sqlite, confirm it exists first). - Run:
bar interpret <db> --audience <engineering|executive> - This does NOT call an LLM itself — it writes a deterministic brief (the exact facts plus audience-specific guidance) to a file. Read that brief file yourself, then write the actual self-improvement report from it: what should this agent/session do differently next time, grounded only in facts the brief cites — never invent a number that isn't in the brief.
- Present the report to the user, clearly labeled as AI-generated interpretation, distinct from
the deterministic report
/bar-reportproduces.
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.
- 4d ago First seen · 22 lines · 19 tokens per session scan A e8afab5c4157
bar-interpret is a command published in the GitHub repository bar181/bar-observatory (22 stars, last pushed 7d ago), licensed MIT. It adds 19 tokens to every session and 282 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.
Other commands, from other repositories
spec
Generate a specification for AI-assisted implementation.
ticket
Take a GitHub issue through an isolated implementation and pull request in its own Herdr tab.
krait-poc
Write and run a valid Foundry proof-of-concept that proves (or disproves) a Solidity exploit by asserting the actual harm on a forked chain or against local source.
usage
Audit pipeline: token spend attributed by phase, task, model, author and time — with cache economics, cost-per-task and a usage trend. Read-only (never mutates the manifest).
krait-review
Re-examine findings killed by the Critic's automatic gates. Catches over-filtering without compromising the main report's zero-FP standard.
report
Produce executive and technical compliance reports.