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.
git clone --depth 1 https://github.com/terrene-foundation/kailash-coc-claude-pyWrote 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/commands/terrene-foundation/kailash-coc-claude-py/implement)<a href="https://agentmods.dev/commands/terrene-foundation/kailash-coc-claude-py/implement"><img src="https://agentmods.dev/badge/commands/terrene-foundation/kailash-coc-claude-py/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/commands/terrene-foundation/kailash-coc-claude-py/implement"><img src="https://agentmods.dev/badge/commands/terrene-foundation/kailash-coc-claude-py/implement.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 | $0.00020 | $0.02225 |
| Opus 5 | $0.00010 | $0.01112 |
| Sonnet 5 | $0.00004 | $0.00445 |
| Haiku 4.5 | $0.00002 | $0.00222 |
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- implement — 89% identical, 6 lines differ
How it starts
The opening of the file, as written. The whole thing — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Workspace Resolution
- If
$ARGUMENTSspecifies a project name or todo, parse accordingly - Otherwise, use the most recently modified directory under
workspaces/(excludinginstructions/) - If no workspace exists, ask the user to create one first
- Read all files in
workspaces/<project>/briefs/for user context (this is the user's input surface)
Phase Check
- Read files in
workspaces/<project>/todos/active/to see what needs doing - Read files in
workspaces/<project>/todos/completed/to see what's done - If
$ARGUMENTSspecifies a specific todo, focus on that one - Otherwise, pick the next active todo
- Reference plans in
workspaces/<project>/02-plans/for context - If any file in
briefs/was modified afterspecs/_index.md, STOP — briefs changed since analysis. Flag for user decision: re-run/analyzeor acknowledge the brief change.
Execution Model
This phase executes under the autonomous execution model (see rules/autonomous-execution.md). Implementation is fully autonomous — agents execute in parallel, self-validate through TDD, and converge through quality gates. The human observes outcomes but does not sit in the execution loop. Pre-existing failures are fixed, not reported (zero-tolerance). Agent-to-agent delegation (reviewer, security-reviewer) is autonomous, not human-gated.
Workflow
NOTE: Run /implement repeatedly until all todos/active have been moved to todos/completed
Wave boundary (MUST): /implement runs the CURRENT wave (one value-ranked milestone-group per rules/wave-loop.md MUST-1). At wave completion, STOP and run the inter-wave gate (rules/wave-loop.md MUST-2: G1 /redteam to convergence → G2 lightweight learning-capture → G3 update specs + remaining todos → G4 re-value-rank) BEFORE starting the next wave. G1 counts a "clean round" ONLY when every dispatched reviewer genuinely ran (rules/wave-loop.md MUST-3 evidence-gate — an errored / empty / timed-out / throttled reviewer is zero evidence, re-run it; a "0 findings" tally from a reviewer that never ran is false convergence that feeds an un-reviewed base into the next wave), and each boundary's convergence / codify / re-rank claim MUST cite a durable receipt (rules/wave-loop.md MUST-5 — journal entry / commit SHA, never a self-attested "converged ✓"). Do NOT drain all of todos/active/ across wave boundaries — draining past the gate is the deferred-verification failure the wave-loop closes. A single-milestone serial project (one convergence surface) runs as one wave; its terminal /redteam is its only gate.
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 · 140 lines · 20 tokens per session scan A d5248677f5c4
implement is a command published in the GitHub repository terrene-foundation/kailash-coc-claude-py (12 stars, last pushed 22d ago), licensed Apache-2.0. It adds 20 tokens to every session and 2,225 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 2026-09-03.
Other commands, from other repositories
template
Manage issue templates for streamlined issue creation.
sync-linear
Sync current work with Linear ticket status.
add-note
Add an internal or external note to a ConnectWise PSA ticket.
fest-show
Show festival progression (in-progress tasks, roadmap, and dependency view).
dispatcher
Pick the next-best repo to work on across the portfolio — rank free repos, recommend one, claim its lease atomically, and route to the entry command.
workpm
A project-management workflow for coordinating multiple AI workers through five stages. It includes task assignment, shared activity logs, worker replacement, and final checks.