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 commands/desplega-ai/agent-swarm/implement-issuegit clone --depth 1 https://github.com/desplega-ai/agent-swarmWrote 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/desplega-ai/agent-swarm/implement-issue)<a href="https://agentmods.dev/commands/desplega-ai/agent-swarm/implement-issue"><img src="https://agentmods.dev/badge/commands/desplega-ai/agent-swarm/implement-issue.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 | $0.00015 | $0.00634 |
| Opus 5 | $0.00008 | $0.00317 |
| Sonnet 5 | $0.00003 | $0.00127 |
| Haiku 4.5 | $0.00002 | $0.00063 |
Grade A, and why
implement-issue 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 4d 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 — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Implement Issue
Read a GitHub or GitLab issue, implement the requested changes, and create a PR/MR.
Provider detection: Check the URL or remote:
- If GitHub → use
gh issue view/gh pr create - If GitLab → use
glab issue view/glab mr create
Arguments
issue-number-or-url: Either an issue number (e.g.,123) or a full URL
Workflow
1. Parse and Fetch
If given a URL, extract owner, repo, and issue number. Fetch issue details (title, body, labels, comments). Understand what's being requested, acceptance criteria, and any technical constraints.
2. Setup
- Ensure repo is cloned to
/workspace/personal/<repo-name>(clone withgh repo cloneif needed) - Fetch origin, checkout main, pull latest
- Create a feature branch:
fix/issue-<number>-<short-description>
3. Implement
- Understand the codebase — explore relevant files and existing patterns
- Plan your approach — consider using
/planningfor complex changes - Write the code — implement the requested functionality
- Test your changes — run existing tests, add new tests if appropriate
- Verify it works — manual verification where possible
Keep changes focused on what the issue requests. Avoid scope creep.
4. Quality Checks, Commit, and Push
- Run PR checks (MANDATORY) — Run ALL checks from the "PR Checks" section of your Repository Guidelines. Fix any failures before proceeding. If no guidelines are defined, check the project's CLAUDE.md for a pre-PR checklist.
- Commit with a message referencing the issue (e.g.,
Fix #123: <description>). Use conventional commit style if the repo uses it. - Push with
git push -u origin HEAD.
5. Create the PR
Create the PR with a descriptive title and body including: summary of changes, key changes list, testing done, and Fixes #<issue-number> to auto-close the issue on merge.
After creating the PR, check CI status with gh pr checks (GitHub) or glab mr view --json pipelines (GitLab). If CI fails, fix the issues, push, and re-check until green.
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.
- 4d ago First seen · 63 lines · 15 tokens per session scan A a12d96338dcf
implement-issue is a command published in the GitHub repository desplega-ai/agent-swarm (740 stars, last pushed today), licensed MIT. It adds 15 tokens to every session and 634 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 commands, from other repositories
distill
Distill repository files into the RLM Summary Ledger using agentic intelligence (fast) or Swarm Workers (offline batch).
distill-agent
High-speed RLM distillation of project documentation using agentic intelligence.
os-loop
Run a full OS improvement cycle — execute, eval, emit friction events, close with post-run metrics, and trigger a Triple-Loop Retrospective if the friction threshold is crossed.
os-architect
Front-door intake for Agentic OS evolution — classifies intent, audits existing capabilities, proposes a path (A/B/C), and dispatches implementation work via Copilot CLI.
os-init
Bootstrap the project by triggering the agentic-os-setup conversational architect.
os-memory
Force garbage collection and conflict resolution on the tiered memory system.