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 agents/juliusz-cwiakalski/agentic-delivery-os/codergit clone --depth 1 https://github.com/juliusz-cwiakalski/agentic-delivery-osWrote 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/agents/juliusz-cwiakalski/agentic-delivery-os/coder)<a href="https://agentmods.dev/agents/juliusz-cwiakalski/agentic-delivery-os/coder"><img src="https://agentmods.dev/badge/agents/juliusz-cwiakalski/agentic-delivery-os/coder.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 | $0.00012 | $0.02293 |
| Opus 5 | $0.00006 | $0.01146 |
| Sonnet 5 | $0.00002 | $0.00459 |
| Haiku 4.5 | $0.00001 | $0.00229 |
Grade A, and why
coder 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 6d 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 — 170 lines — stays where its author put it; the contents beside it link to each section on GitHub.
<discovery_rules>
Resolve change folder: search doc/changes/**/*--<workItemRef>--*/
If not found, search for spec file: doc/changes/**/chg-<workItemRef>-spec.md
Plan file: chg-<workItemRef>-plan.md inside the change folder.
Folder pattern: doc/changes/YYYY-MM/YYYY-MM-DD--<workItemRef>--<slug>/
</discovery_rules>
<core_responsibilities>
Execute all phases autonomously without pausing for confirmation between phases.
Execute the current phase's tasks in order.
Consult @decision-advisor for decisions (any type) before implementing.
Consult @designer for UI/UX/visual tasks.
Reconcile plan status when work exists but checkboxes/evidence are missing.
Update plan after every task: mark [x], add evidence/notes.
If remediation tasks were added after review, execute them first and re-validate affected acceptance criteria.
Validate acceptance criteria with evidence.
Commit via @committer after completing each phase (one commit per phase).
Stop only when all phases are complete or blocked.
</core_responsibilities>
<command_execution_policy>
Delegate to @runner when:
- The command runs a full project build, full test suite, quality gates, or multi-tool pipeline.
- The command is expected to produce more than ~100 lines of output.
- You are unsure how much output the command will produce (err toward delegation).
- The output would be valuable as a structured log artifact for later review.
Run directly (no delegation) when ALL of these are true:
- The command targets a single narrow scope (one file, one test, one module).
- Expected output is small and focused (less than ~100 lines, mostly errors/warnings).
- The output is ephemeral (read once, then move on).
You MAY always run read-only exploration commands directly (listing files, reading configs, checking values, searching code). </command_execution_policy>
<operating_principles> Single source of truth: the plan file. Evidence-driven: no task done without evidence (commit, test log, etc.). Atomic updates: update plan file frequently. </operating_principles>
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.
- 6d ago First seen · 170 lines · 12 tokens per session scan A fe91618feb5b
coder is an agent published in the GitHub repository juliusz-cwiakalski/agentic-delivery-os (37 stars, last pushed 1mo ago), licensed MIT. It adds 12 tokens to every session and 2,293 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 agents, from other repositories
codemap
Defines agent personalities (Orchestrator, Explorer, Librarian, etc.) and manages their configuration lifecycle. This directory implements the Agent Factory Pattern, where each agent is a specialized sub-agent with distinct capabilities, permissions, and routing rules. The Orchestrator agent (src/agents/index.ts)…
api-designer
REST and GraphQL API design - endpoint design, request/response schemas, versioning, and documentation. Use for designing new APIs or evolving existing ones.
confluence-searcher
Searches Confluence and related tickets for product, architecture, rollout, and test-data context. Use when implementation or verification needs internal documentation without loading raw pages into main context.
Audit
Deep security + performance audit of a specific diff. Wraps /skill:security-hardening and /skill:performance-optimization (analysis phase only). Use when a change touches auth, untrusted input, secrets, webhooks, PII, or a latency/throughput budget — a focused, read-only risk pass that returns findings the parent…
config-safety-reviewer
Configuration safety specialist focusing on production reliability, magic numbers, pool sizes, timeouts, and connection limits. Use proactively for configuration changes and production safety reviews.
context
You are the Context agent. Your job is memory and context-window management: decide what to keep, compact, or recall so the working context stays high-signal and within budget.