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 skills add ArtemioPadilla/agent-triforce --skill self-reviewgit clone --depth 1 https://github.com/ArtemioPadilla/agent-triforceWrote 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/artemiopadilla/agent-triforce/self-review)<a href="https://agentmods.dev/skills/artemiopadilla/agent-triforce/self-review"><img src="https://agentmods.dev/badge/skills/artemiopadilla/agent-triforce/self-review/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/artemiopadilla/agent-triforce/self-review"><img src="https://agentmods.dev/badge/skills/artemiopadilla/agent-triforce/self-review.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 | $0.00049 | $0.00369 |
| Opus 5 | $0.00024 | $0.00185 |
| Sonnet 5 | $0.00010 | $0.00074 |
| Haiku 4.5 | $0.00005 | $0.00037 |
Grade A, and why
self-review 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 12d 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
Self-Review
Run 4 checks on the artifact at: $ARGUMENTS
The Protocol (under 60 seconds)
- Placeholder scan — Search for "TBD", "TODO", incomplete sections, vague requirements,
{placeholder}tokens. Fix each one. - Internal consistency — Do sections contradict each other? Do names/types match across references? Does architecture match feature descriptions? Fix contradictions.
- Scope check — Is this focused enough for its purpose? Does it try to do too much? If it needs decomposition, flag it.
- Ambiguity check — Could any requirement be interpreted two ways? Pick one interpretation and make it explicit.
Rules
- Fix inline, don't re-review. When you find issues, fix them immediately. Don't run self-review again after fixing. The purpose is "catch the obvious," not "iterate to perfection."
- This is NOT a subagent dispatch. Read your own output with fresh eyes. Multi-agent review is a separate concern.
- Report what you fixed. After running all 4 checks, briefly state what was found and fixed (or "clean — no issues found").
Output Format
Self-review of {artifact path}:
- Placeholder scan: {clean | fixed N items: list}
- Consistency: {clean | fixed: list}
- Scope: {focused | flagged: reason}
- Ambiguity: {clean | resolved N items: list}
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.
- 12d ago First seen · 35 lines · 49 tokens per session scan A 5046b85b9119
self-review is a skill published in the GitHub repository ArtemioPadilla/agent-triforce (3 stars, last pushed 5mo ago), licensed MIT. It adds 49 tokens to every session and 369 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
fellowship
Multi-task orchestrator. Coordinates agent teammates (led by Gandalf) running /quest (code) or /scout (research) workflows. Use when you have multiple independent tasks to run in parallel.
quest
Use for multi-file or multi-step changes that need research and a plan — not single-file fixes under 50 lines that follow an existing pattern (see Escape Hatch below). Runs the Research → Plan → Implement → Review lifecycle with a hard gate leaving each of the first three phases and context compaction between them.
retro
Invoke after a fellowship disbands, or when the user explicitly asks for a retrospective. Collects gate history, palantir alerts, and quest metrics to surface patterns and interactively recommend configuration improvements.
council
Invoke when the user runs /council or from /scout. Quest inlines this orientation as its Research step 2 and does not call it. Loads focused, task-relevant context by reading CLAUDE.md, scanning for related files, and producing a structured Session Context block.
lembas
Use between workflow phases or when context feels bloated. Writes a structured checkpoint capturing task, findings, files, state, and next steps, then continues from that summary instead of the full history. Invoke standalone or automatically between quest phases.
missive
Invoke only when spawning a quest from a GitHub issue reference. Fetches GitHub issue context for quest spawning. Parses issue references, retrieves structured data via gh, and produces branch suggestions and PR keywords. Used standalone or as input to quest orchestration.