Archon is a workflow engine for AI coding agents that turns development processes into YAML-defined sequences with phases, validation gates, and artifacts. Developers use it to run repeatable processes such as planning, implementation, testing, code review, and pull-request creation across projects. The catalogue entries provide commands, agents, skills, hooks, instructions, and settings for working with Archon.
Borrowing it
Nothing to install: this file belongs to coleam00/Archon. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/coleam00/Archon/dev/.claude/skills/triage/SKILL.mdgit clone --depth 1 https://github.com/coleam00/ArchonWrote 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/coleam00/archon/triage)<a href="https://agentmods.dev/skills/coleam00/archon/triage"><img src="https://agentmods.dev/badge/skills/coleam00/archon/triage/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/coleam00/archon/triage"><img src="https://agentmods.dev/badge/skills/coleam00/archon/triage.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk warn
- 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 | $0.00050 | $0.00798 |
| Opus 5.5 | $0.00020 | $0.00319 |
| Sonnet 5.5 | $0.00010 | $0.00160 |
| Haiku 4.5 | $0.00005 | $0.00080 |
Grade A, and why
triage 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 1mo 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 — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Triage GitHub Issues
Triage issues for this repository by applying appropriate labels.
Repository Context
- Current repo: !
gh repo view --json nameWithOwner -q .nameWithOwner 2>/dev/null || echo "unknown" - Open issues: !
gh issue list --state open --json number --jq 'length' 2>/dev/null || echo "?" - Existing labels: !
gh label list --json name -q '.[].name' 2>/dev/null | head -20 || echo "none found"
Scope
Determine which issues to triage based on the arguments: $ARGUMENTS
| Argument | Behavior |
|---|---|
| (empty) | Only unlabeled issues (default) |
unlabeled |
Only issues without any labels |
all |
All open issues |
N |
Specific issue (e.g., 67) |
N-M |
Range of issues inclusive (e.g., 60-67) |
Process
-
Fetch available labels — run
gh label list --json name,descriptionto understand the label taxonomy. Labels are organized into type, effort, priority, and area categories. -
Fetch target issues — based on the scope above. For each issue, fetch the full body:
gh issue view {number} --json number,title,body,labels -
For each issue:
- Read the title and full body carefully
- If needed, explore the codebase (
Glob,Grep,Read) to understand the affected code - Classify: one type, one effort, one priority, one or more areas
- Track relationships with other issues (duplicates, related, blocking)
- Apply labels:
gh issue edit {number} --add-label "type,effort/level,P#,area.domain" - Skip issues that already have complete labeling (type + effort + priority + area)
- For partially labeled issues, only add missing label categories
-
Output a triage summary:
## Triage Summary | Issue | Title | Labels Applied | Reasoning | |-------|-------|----------------|-----------| | #67 | ... | bug, effort/low, P1, core.config | ... | **Totals:** - Issues triaged: X - Already labeled (skipped): Y - By priority: P0(n), P1(n), P2(n), P3(n) ## Relationships Discovered | Issues | Relationship | Notes | |--------|--------------|-------| | #61, #62 | Related | Both involve config/logging UX |
What ships with it
1 file 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.
- 1mo ago First seen · 88 lines · 50 tokens per session scan A 0ec84e61aa72
triage is a skill published in the GitHub repository coleam00/Archon (23,752 stars, last pushed today), licensed MIT. It adds 50 tokens to every session and 798 once invoked, about $0.0002 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 2026-09-03.
Other skills, from other repositories
system-prompts
Write system prompts, tool docs, and agent definitions. Project tag conventions + RFC 2119 keywords + dense compression. Use when authoring or editing any prompt the model reads.
tool-prompt-optimization
Optimize the description prompts an AI agent reads to learn its built-in tools (the .md files under prompts/tools/). Two halves: (1) measure how much of a prompt is already inferable from the tool's JSON parameter schema + name, to prune redundancy with evidence; (2) house authoring rules for what belongs in a tool…
semantic-compression
Re-encode verbose prose into a dense telegraphic register — punctuation as connectives, label frames, verbless assertions — without losing normativity or precision. Use when compressing system prompts, tool/function descriptions, skill bodies, or agent instructions; reducing token count or context bloat; making…
swarm-plan
Full execution protocol for MODE: PLAN -- plan creation, external plan ingestion, QA gate persistence, task granularity, and traceability checks.
loop
Full execution protocol for MODE: LOOP — the compound-engineering loop: brainstorm → plan → build → review → improve, iterating under defense-in-depth stop conditions with generator/critic separation, durable resumable state, and mandatory compounding learning capture. Loaded on demand by the architect when the loop…
parallel-work-check
Apply before starting work on an existing branch. Checks for parallel work by other agents or developers that may supersede or conflict with your planned changes. Prevents wasted effort on stale branches.