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/andr-ca/agentharness/github-issue-triagenpx skills add andr-ca/agentharness --skill github-issue-triagegit clone --depth 1 https://github.com/andr-ca/agentharnessWhat 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 | $0.00047 | $0.01689 |
| Opus 5 | $0.00023 | $0.00844 |
| Sonnet 5 | $0.00009 | $0.00338 |
| Haiku 4.5 | $0.00005 | $0.00169 |
Grade A, and why
github-issue-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 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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GitHub Issue Triage
Assess a GitHub issue by verifying its claims against actual code, checking fit against product vision, and leaving a structured comment with findings, corrections, and a test plan. This skill assesses and comments — it does not implement fixes. Fixes follow a separate deliberate step per this repo's Recommendation Assessment mandate in CLAUDE.md.
The Prompt (canonical form)
Triage issue #N. Start by fetching the full issue details (title, body, comments, labels, linked PRs). Then verify every factual claim in the issue against the actual running code — grep the relevant source, read the actual files/lines, and where feasible run the relevant code path (in a scratch/sandboxed way) to confirm it's real, not stale, not a misunderstanding. Check the ask against roadmap/vision docs (ROADMAP.md, ARCHITECTURE.md, DECISIONS.md, MANIFEST.md) and note whether it's a scoped fix vs. a bigger direction-setting ask needing human scoping. Draft and post a
gh issue commentwith: confirmed findings with evidence, corrections to stale/wrong claims, a recommendation per CLAUDE.md's Recommendation Assessment mandate, and concrete test/verification steps for the fix. Report what you found and what you posted.
Procedure
1. Fetch the issue
Run gh issue view <n> --json title,body,comments,labels,linkedPullRequests.
Extract:
- Title and body (claims to verify)
- All comments (context, earlier findings, clarifications)
- Labels (priority, type, status signals)
- Linked PRs (related work, partial fixes, predecessor context)
2. Verify every factual claim against running code
This is the hardest step and the highest leverage. Do not trust the issue's prose — verify against the source.
For each claim:
- File changes: Does the file exist?
test -e <path>. Read it and check the actual line/section. - Behavior claims: Grep for the relevant code path. If feasible, run it in a scratch/sandboxed context (e.g., a test, a manual script in a temp directory, a clone of this repo or the repo's dependency) to observe actual behavior.
- "Already fixed" archaeology: Run
git log --all -p --grep=<keyword>orgit log --all -S<string>to check whether a claimed-broken thing was already fixed by a later commit on a different branch or after the issue was filed. - Misunderstandings: If the claim rests on a false premise (wrong file path, misread config, older API surface), note that explicitly.
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 · 123 lines · 47 tokens per session scan A cd49078bd631
github-issue-triage is a skill published in the GitHub repository andr-ca/agentharness (1 stars, last pushed 2d ago), licensed MIT. It adds 47 tokens to every session and 1,689 once invoked, about $0.0002 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
html-ppt-zhangzara-coral
OpenDesign's community-growth campaign across GitHub, Discord, and X: the loops, the content calendar, and the pipeline math. Built as a decision-grade marketing & GTM deck for growth team, community lead.
hr-onboarding
A new-hire onboarding plan as a single page — first week schedule, buddy + manager intro, learning track, equipment checklist, and "you're set when…" outcomes. Use when the brief mentions "onboarding", "new hire", "first week plan", or "入职".
html-ppt-hermes-cyber-terminal
OpenDesign + BYOK: choosing and wiring your own model, hands-on — cost, quality, and the routing decision. Built as a decision-grade AI literacy deck for engineers, IT, applied-AI teams.
html-ppt-taste-brutalist
16:9 HTML deck in tactical-telemetry / CRT-terminal taste. Deactivated-CRT charcoal slides, white-phosphor monospace, hazard-red accent, scanline overlay, ASCII syntax, density over decoration. Distilled from Leonxlnx/taste-skill brutalist-skill (Tactical Telemetry mode).
cupynumeric-migration-readiness
Pre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment…
deepstream-sop
Use this skill when building, deploying, evaluating, debugging, or measuring latency for the DeepStream SOP Inference Microservice — a GPU-accelerated FastAPI service that detects whether operators perform assembly-line steps in order via event boundary detection (GEBD) plus VLM classification. Trigger even if the…