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 doordash-oss/agentic-orchestrator --skill implementgit clone --depth 1 https://github.com/doordash-oss/agentic-orchestratorWrote 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/doordash-oss/agentic-orchestrator/implement)<a href="https://agentmods.dev/skills/doordash-oss/agentic-orchestrator/implement"><img src="https://agentmods.dev/badge/skills/doordash-oss/agentic-orchestrator/implement/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/doordash-oss/agentic-orchestrator/implement"><img src="https://agentmods.dev/badge/skills/doordash-oss/agentic-orchestrator/implement.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00024 | $0.01426 |
| Opus 5.5 | $0.00010 | $0.00570 |
| Sonnet 5.5 | $0.00005 | $0.00285 |
| Haiku 4.5 | $0.00002 | $0.00143 |
Grade A, and why
implement 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 — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Plan Execution
Implement the approved phase plan. You own code, development-time testing, explicitly agent-owned evidence, and the progress handoff. The verification harness owns final contract execution and verification-report.yaml.
Output Files
| Artifact | Path | Requirement | Purpose |
|---|---|---|---|
progress.md |
{phase_dir}/progress.md |
required | structured progress markdown with iteration handoff, deferrals, and iteration state |
Start Here
Never create or edit verification-report.yaml or phase_complete. When a testing contract exists, the harness derives the report after it commits your structured root outcome.
- Read the full phase plan.
- If
{phase_dir}/progress.mdexists, read it and resume from### Where I stopped. Reviewer feedback and the current plan override stale handoff prose. - Read
testing-contract.yamlif it exists — at{phase_dir}/../testing-contract.yamlfor roadmap phases, or{phase_dir}/testing-contract.yamlfor cycle layouts (rebase/review-comments, where{phase_dir}is the cycle root). Note only items withowner: agent; final execution ofowner: harnessitems is not your responsibility. If the file does not exist, this feature has no per-iteration machine verification: run every command under the plan's### Automated Verificationyourself each iteration and record the commands and results in your handoff — SUCCESS asserts they pass. - Confirm every Task
**Repo:** <name>is mounted. If the plan or repo scope is contradictory, call the formal AskUserQuestion control and continue after the answer. - Build a dependency graph from
Blocked by, shared files, repo tags, and acceptance criteria.
Implementation Rules
- The approved Tasks, acceptance criteria, and Success Criteria define scope.
- For behavior changes, use a red-green-refactor loop: add a focused failing test, verify the expected failure, implement the smallest fix, run the focused test and relevant surrounding suite, then refactor while green.
- Pin discovered bugs with regression tests before fixing them when practical.
- Manual evidence does not replace an automated test for behavior that can reasonably be automated.
- Use stubs only when the plan explicitly requests them. Mark intentional stubs with
// STUB(Phase N): <purpose>. - Do not invent bookkeeping rows or edit the testing contract. If the contract or scope must change, request user input.
- Add comments only for intent, rationale, invariants, or non-obvious tradeoffs.
What ships with it
2 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.
- 6d ago Changed · -1 lines 7b61f2fb9ca2
- 17d ago Changed b136c01103ee
- 26d ago Changed 4f85ba4d3098
- 1mo ago First seen · 101 lines · 24 tokens per session scan A c56e585a247f
implement is a skill published in the GitHub repository doordash-oss/agentic-orchestrator (114 stars, last pushed 7d ago), licensed Apache-2.0. It adds 24 tokens to every session and 1,426 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 2026-08-30.
Other skills, from other repositories
ai-observability-promptfoo
Testing and evaluation framework for LLM prompts and applications -- promptfooconfig.yaml, assertions, model-graded evals, red teaming, CI/CD integration, custom providers, and comparative evaluation.
speclite-dev-story
A developer workflow that implements a planned software story, tests the changes, checks the definition of done, and sends the story for review.
speclite-agent-dev
A software development agent that implements approved Stories with tests and delivers the resulting code.
Regression Discipline
The rule that every bug fix ships with a test that fails on the old code and passes on the new — and how to write that test well.
tdd
Add new behavior test-first (write failing test → make it pass → refactor). Use when asked to "add a test for", "implement X with tests", or extending a codebase that already has a test suite.
skillspec-test
Execute test blocks from a SkillSpec .agent file.