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/pipekit/mcp-for-argo-workflows/review-progressgit clone --depth 1 https://github.com/pipekit/mcp-for-argo-workflowsWhat 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 | $0.00000 | $0.00362 |
| Opus 5 | $0.00000 | $0.00181 |
| Sonnet 5 | $0.00000 | $0.00072 |
| Haiku 4.5 | $0.00000 | $0.00036 |
Grade A, and why
review-progress 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 2d 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.
What it actually says
Review Project Progress
Review the current state of the mcp-for-argo-workflows project against the Linear plan.
Step 1: Gather Current State
- Fetch Linear issues using
mcp__linear-server__list_issueswithproject: "mcp-for-argo-workflows" - Check local codebase - What files exist? What's implemented?
- Run verification - Execute
make lintandmake testif Makefile exists
Step 2: Identify Discrepancies
Report on:
- Issues marked Done in Linear but not implemented locally
- Code implemented locally but issues still in Backlog
- Failing tests or lint errors
- Missing dependencies or blockers
Step 3: Generate Report
Produce a summary with:
## Progress Report: mcp-for-argo-workflows
### Overall Status
- Setup: X/10 complete
- MCP Tools: X/35 complete
- Testing: X/2 complete
- Documentation: X/3 complete
### Recently Completed
- [PIP-X] Issue title
### In Progress
- [PIP-X] Issue title - status notes
### Ready for Implementation
- [PIP-X] Issue title (dependencies met)
### Blocked
- [PIP-X] Issue title - blocked by [PIP-Y]
### Recommendations
1. Next priority task
2. Any issues needing attention
Step 4: Update Linear (Optional)
If discrepancies found, offer to:
- Update issue statuses to match reality
- Add progress comments to in-progress issues
- Flag any blockers or concerns
Begin by fetching the Linear project state.
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.
- 2d ago First seen · 59 lines · 0 tokens per session scan A f97d517c5582
review-progress is a command published in the GitHub repository pipekit/mcp-for-argo-workflows (5 stars, last pushed 2d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 362 tokens. 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 commands, from other repositories
git
Git operations with intelligent commit messages and workflow optimization.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
constitution
Create or update the project constitution from interactive or provided principle inputs.