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 asfbay-bit/opchain-skills --skill oc-prompt-opsgit clone --depth 1 https://github.com/asfbay-bit/opchain-skillsWrote 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/asfbay-bit/opchain-skills/oc-prompt-ops)<a href="https://agentmods.dev/skills/asfbay-bit/opchain-skills/oc-prompt-ops"><img src="https://agentmods.dev/badge/skills/asfbay-bit/opchain-skills/oc-prompt-ops/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/asfbay-bit/opchain-skills/oc-prompt-ops"><img src="https://agentmods.dev/badge/skills/asfbay-bit/opchain-skills/oc-prompt-ops.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 | $0.00082 | $0.05794 |
| Opus 5 | $0.00041 | $0.02897 |
| Sonnet 5 | $0.00016 | $0.01159 |
| Haiku 4.5 | $0.00008 | $0.00579 |
Grade A, and why
oc-prompt-ops 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 — 486 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Ops
On first invocation, read references/orchestrator.md and follow its welcome protocol.
Treat prompts as code: source-controlled, diffable, semver'd, and gated on an eval suite the same way application code is gated on tests. A prompt is the single most behavior-defining string in an LLM app — and the one most teams edit live, in a console, with no version history and no way to tell whether "it got better" is real or vibes. This skill makes a prompt change a reviewable diff with a measured score delta attached.
This is not a tri-agent harness. It's an operations layer the model-facing
skills build on: oc-claude-api owns the request surface (model routing,
caching, tool wiring), oc-agent-forge owns agent topology, oc-rag-forge
owns retrieval. Prompt Ops owns the part underneath all three — the prompt
text, the eval datasets that score it, and the regression gate that stops a
"small wording tweak" from quietly tanking quality on a migration.
opchain dogfoods this skill on itself. The worked example referenced throughout
is prompts/opchain-eval/ — opchain's own eval set (inputs.jsonl,
expected.jsonl, eval.yaml), published in Sprint 3 as the canonical
/oc-prompt eval artifact. Wherever this doc says "the eval set", that directory
is the live instance.
Model facts come from
oc-claude-api/ theclaude-apiskill, not memory. Judge-model choice, model IDs, and Batch-API economics in this skill are sourced there. Current models: Fable 5 (claude-fable-5), Opus 4.8 (claude-opus-4-8), Sonnet 4.6 (claude-sonnet-4-6), Haiku 4.5 (claude-haiku-4-5). When a prompt is pinned to a model, pin it to one of these exact IDs.
/oc-prompt — Command Reference
PROMPT OPS COMMANDS
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
PROMPT-AS-CODE
/oc-prompt Version / organize a prompt set (prompt-as-code layout)
/oc-prompt diff Diff two prompt versions + their eval-score deltas
EVALUATION
/oc-prompt eval Run a prompt version against an eval dataset → scorecard
/oc-prompt goldset Build or extend the eval goldset (inputs/expected/rubric)
/oc-prompt judge Configure / calibrate the LLM-as-judge grader
REGRESSION / DRIFT
/oc-prompt regress Re-run the eval suite and gate on score regression
/oc-prompt baseline Freeze the current scores as the regression baseline
/oc-prompt drift Re-run the frozen baseline to detect prompt/model drift
UTILITIES
/checkpoint Show checkpoint status
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Type any command to begin. /oc-prompt to see this again.
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 Changed 9dbdd12eb834
- 11d ago First seen · 486 lines · 82 tokens per session scan A a6cfe5dfe9c2
oc-prompt-ops is a skill published in the GitHub repository asfbay-bit/opchain-skills (0 stars, last pushed 7d ago), licensed Apache-2.0. It adds 82 tokens to every session and 5,794 once invoked, about $0.0004 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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