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 skills/orin-dx/agent-plugins/implementnpx skills add orin-dx/agent-plugins --skill implementgit clone --depth 1 https://github.com/orin-dx/agent-pluginsWhat 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 | $0.00166 | $0.01951 |
| Opus 5 | $0.00083 | $0.00975 |
| Sonnet 5 | $0.00033 | $0.00390 |
| Haiku 4.5 | $0.00017 | $0.00195 |
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 yesterday.
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 — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Smith — Implementation Skill
smith is one skill, not four. Every real invocation runs the same TDD pipeline through implementer: read the task's acceptance criteria, write a failing test, confirm red, write the minimal implementation, confirm green, commit — then mutator and reviewer gate the batch before exit-gate runs the final adversarial check.
The frontmatter description also triggers on "generate tests for X," "explain what this code does," and "refactor this without changing behavior." None of those have dedicated agent behavior — no agent here writes tests without implementation, produces a plain-language explanation, or runs a refactor-only mode. A request framed that way still routes to implementer's standard TDD cycle; if that's not a fit (there is no failing test to write for "explain this"), say so rather than forcing the pipeline to produce something no agent actually defined.
Consumes: plan@1 (preferred) or spec@1 directly if no plan exists.
Produces: committed code, and per-task criteria_evidence (exact test and implementation file/line for each criterion proven). No smith agent assembles a changeset@2 itself — that schema is produced by changeset-analyzer when shipping. The caller aggregates each task's criteria_evidence across the run and hands the collection to changeset-analyzer alongside the diff, so courier uses smith's exact evidence instead of reconstructing approximate locations from the diff.
<tdd_cycle>
For every implementation task:
- Write the failing test exactly as specified.
- Run the test — confirm it fails with the expected error (red phase required; a test that passes before implementation is broken).
- Write the minimal implementation to make it pass — no more.
- Run the test — confirm it passes (green).
- Commit with the conventional commit message specified in the task.
</tdd_cycle>
<mutation_gate>
After each implementation task commits, mutator runs mutation testing scoped to the files changed in that task. It detects language from the workspace root (Cargo.toml → cargo-mutants; package.json → Stryker) and analyzes every mutant the test suite failed to catch.
For each surviving mutant, mutator designs a precision test that would kill it and returns those tests to implementer as additional failing tests. The implementer writes them and makes them green before any further tasks proceed.
When the mutation tool is unavailable, mutator reports tool_unavailable and exit-gate records the gap rather than blocking.
</mutation_gate>
<exit_gate>
After all tasks are complete and mutator has run, exit-gate runs the sentinel protocol against the spec independently. It reads the spec from spec_file_path on disk — not from spec content forwarded through conversation context. It reads the current code state from scratch, assumes the implementation is incomplete, and confirms that mutation testing ran (or was noted as unavailable). It returns a verdict@1. A spec without spec_file_path set is a hard block.
The caller passes the aggregated per-task criteria_evidence to exit-gate alongside the manifest. The gate uses each entry as a pointer to check — reading exactly the named file and line — rather than searching the codebase cold for every criterion. This makes verification faster without making it less adversarial: the gate still reads the actual location itself and confirms the criterion holds; it does not accept the pointer's existence as ranger.
</exit_gate>
<subagent_dispatch_matrix>
| Agent | Role | Tier | When to delegate |
|---|---|---|---|
recon |
Workspace manifest & baseline | haiku / low | Before any code is written — detect language, test runner, inventory plan files, confirm baseline passes. |
implementer |
TDD batch execution | sonnet / medium | Executes a Subsystem Batch (or single task) from plan@1 — full red/green/commit cycle adhering to YAGNI (Ponytail layer). |
mutator |
Mutation testing gate | sonnet / medium | After each Subsystem Batch commits — verify the test suite catches real faults; returns precision tests for survivors. |
reviewer |
Pre-gate review | sonnet / medium | After batch mutation gate passes — neutral review of batch scope, non-negotiables, sibling gaps, test quality. |
exit-gate |
Adversarial exit verification | opus / high | After all batches complete — independent sentinel check against spec; produces verdict@1. |
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.
- yesterday First seen · 104 lines · 166 tokens per session scan A af4f664ef616
implement is a skill published in the GitHub repository orin-dx/agent-plugins (1 stars, last pushed 5d ago), licensed MIT. It adds 166 tokens to every session and 1,951 once invoked, about $0.0008 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.
Other skills, from other repositories
campus-assignment-cleaner
Activates when a student or campus developer needs to make a working-but-messy codebase submission-ready before uploading it for grading. Trigger phrasings include "clean up my assignment before submitting", "remove debug prints and commented code", "tidy this up before I upload it to Moodle/Canvas/GitHub Classroom"…
peon-ping-log
Log exercise reps for the Peon Trainer. Use when user says they did pushups, squats, or wants to log reps. Examples - "/peon-ping-log 25 pushups", "/peon-ping-log 30 squats", "log 50 pushups".
agy-delegate
Delegate a coding task to the Google Antigravity CLI (agy) as a background implementer, then review its diff and land it yourself. Use this whenever the user wants to hand implementation work to Antigravity or agy - phrasings like "have Antigravity do X", "delegate this to agy", "run it through agy", or "use…
hyperpod-version-checker
Check and compare software component versions on SageMaker HyperPod cluster nodes - NVIDIA drivers, CUDA toolkit, cuDNN, NCCL, EFA, AWS OFI NCCL, GDRCopy, MPI, Neuron SDK (Trainium/Inferentia), Python, and PyTorch. Use when checking component versions, verifying CUDA/driver compatibility, detecting version mismatches…
conductor-implement
Executes the tasks defined in the specified track's plan. Use this to start or continue working on a feature, bug fix, or chore.
dsql
Build with Aurora DSQL — manage schemas, execute queries, handle migrations, diagnose query plans, diagnose cluster performance, load data, and develop applications with a serverless, distributed SQL database. Covers IAM auth, multi-tenant patterns, MySQL-to-DSQL and PostgreSQL-to-DSQL schema conversion, FK…