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 manishiitg/coding-agent-loop --skill playbookgit clone --depth 1 https://github.com/manishiitg/coding-agent-loopWrote 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/manishiitg/coding-agent-loop/playbook)<a href="https://agentmods.dev/skills/manishiitg/coding-agent-loop/playbook"><img src="https://agentmods.dev/badge/skills/manishiitg/coding-agent-loop/playbook/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/manishiitg/coding-agent-loop/playbook"><img src="https://agentmods.dev/badge/skills/manishiitg/coding-agent-loop/playbook.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 | $0.00031 | $0.00621 |
| Opus 5.5 | $0.00012 | $0.00248 |
| Sonnet 5.5 | $0.00006 | $0.00124 |
| Haiku 4.5 | $0.00003 | $0.00062 |
Grade A, and why
example-playbook 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 1mo 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 — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Example Playbook
Outcome
State the durable workflow capability and the result the customer receives.
When to use
State positive triggers, scope, and any important exclusion.
Discovery and user direction
Inspect the current workflow, goals, metrics, configuration, capabilities, stores, reports, and triggers before proposing changes. Summarize reusable design and gaps, then ask focused questions for unresolved customer choices such as scope, success, approvals, thresholds, ownership, and budgets. Record the answers as customer direction. Installation alone does not approve workflow changes or execution. Default to one small-team workflow; split only for incompatible access or lifecycle boundaries.
Required inputs
List the minimum information, access, policies, and existing artifacts the builder must resolve. Treat unavailable required inputs as explicit blockers.
Plan and AgentWorks tools
Explain how to compose this into the plan: important scripted steps, message sequences, routes, branches, schedules, or approvals; existing steps to reuse; and AgentWorks capabilities to resolve. Preserve the user's process and link to detailed guidance.
Recommend whether this route should start manually, from a workflow handoff, webhook or product event, release event, or schedule. Define the proposed scope, filters or cadence and timezone, concurrency, retries, budget, notifications, and durable completion evidence. Do not enable an unapproved trigger or invent cadence.
Keep review asynchronous unless the customer explicitly requests an attended run: persist a concrete pending_review proposal and end preparation, then use a separate action route that validates the later approval and current target state before applying it.
Publish review requests with create_human_input_request so they appear in the Report dashboard. Treat Ask in chat as discussion and Approve/Reject/Defer as saved decisions. Choose the later executor by target: a writable runtime code/folder route, typed Builder/Fixer tools for plan or configuration, or an approved external integration.
What ships with it
3 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.
- 1mo ago First seen · 56 lines · 31 tokens per session scan A 8ac14f5343bf
example-playbook is a skill published in the GitHub repository manishiitg/coding-agent-loop (13 stars, last pushed 19d ago), licensed MIT. It adds 31 tokens to every session and 621 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-14.
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