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/qgolem/orc/orc-scoutnpx skills add qGolem/orc --skill orc-scoutgit clone --depth 1 https://github.com/qGolem/orcWrote 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/qgolem/orc/orc-scout)<a href="https://agentmods.dev/skills/qgolem/orc/orc-scout"><img src="https://agentmods.dev/badge/skills/qgolem/orc/orc-scout.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 5.1 | $0.00012 | $0.00898 |
| Opus 5 | $0.00006 | $0.00449 |
| Sonnet 5 | $0.00002 | $0.00180 |
| Haiku 4.5 | $0.00001 | $0.00090 |
Grade A, and why
orc-scout 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 6d 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 — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Orc Scout
Phase-scoped codebase discovery that runs forked (non-interactive). Explores files referenced in PLAN.md and their surrounding code. For phases > 1, reads prior phase artifacts for cross-phase awareness.
Input
- $ARGUMENTS —
{slug} {phase-num}(e.g.,my-feature 2)
Template
See templates/SCOUT.md for output format.
Process
Step 1: Parse Arguments and Locate Phase
slug = first word of $ARGUMENTS
phase_num = second word of $ARGUMENTS
Find phase directory:
Glob .claude/plans/{slug}/phases/0{phase_num}-*/PLAN.md
Read the matched PLAN.md.
Step 2: Extract Target Files
From PLAN.md, collect all target files:
files_modifiedfrom frontmattermust_haves.artifactspaths<files>sections from each task
Deduplicate into a single list.
Step 3: Analyze Each Target File
For each target file:
Existence & size:
Glob for the file path
Bash: wc -l {file}
Imports (what it depends on):
Grep: import.*from in the file
Imported by (what depends on it):
Grep: import.*{filename} across src/
Related tests:
Glob: **/{basename}.test.* and **/{basename}.spec.*
Dependency graph:
Bash: madge --depends {file}
Step 4: Analyze Target Directories
For each unique directory containing target files:
Bash: tree -L 2 -P '*.ts' {target-dir}
Step 5: Discover Conventions
From the target files and their neighbors, document:
- Naming conventions (files, exports, variables)
- Error handling patterns
- Testing patterns (framework, mocking, location)
- Import organization
Include file:line references for each convention.
Step 6: Cross-Phase Context (phase > 1 only)
If phase_num > 1, read prior phase artifacts:
Glob .claude/plans/{slug}/phases/0{prior}-*/PROGRESS-*.md → Read each match
Glob .claude/plans/{slug}/phases/0{prior}-*/SUMMARY-*.md → Read each match
Glob .claude/plans/{slug}/phases/0{prior}-*/IMPL-VERIFICATION.md → Read if exists
Fallback: If PROGRESS-*.md not found, try reading PROGRESS.md (single-executor phases)
Fallback: If SUMMARY-*.md not found, try reading SUMMARY.md (single-executor phases)
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.
- 6d ago First seen · 138 lines · 0 tokens per session scan A 03ef78985596
orc-scout is a skill published in the GitHub repository qGolem/orc (5 stars, last pushed 5mo ago), licensed MIT. It adds 12 tokens to every session and 898 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 2026-08-31.
Other skills, from other repositories
recipe-front-review
Reviews completed frontend implementation for governing-source compliance, scope economy, repository quality, and security, then applies user-approved React corrections.
new-skill
Scaffold a new brooks-lint analysis skill so it passes npm run validate and npm run evals on the first try — generates skills/{name}/SKILL.md (with the mandatory "Do NOT trigger for:" clause and a Process section citing guide step ranges) plus skills/{name}/{name}-guide.md (sequentially numbered steps), then appends…
recipe-add-integration-tests
Add integration/E2E tests to existing codebase using Design Docs.
llm-friendly-context
Clarifies inputs, outputs, success criteria, decisions, and unresolved conditions so downstream consumers can execute without guessing. Use when writing or revising LLM-facing prompts, handoffs, planning artifacts, reviews, reports, or generated instructions.
prompt-tuning
Tune a prompt, or anything whose quality is measured by non-deterministic model output, without chasing noise - a noise baseline before the first edit, medians over repeated runs, enforcement AFTER generation rather than in the wording. Use when iterating on prompts or model-judged output.
write-article
Write high-quality articles from scratch using a 6-phase pipeline: deep STORM-inspired research with parallel agents, multi-perspective outline generation, section-by-section drafting with research grounding, adaptive anti-slop enforcement (hard/soft banned vocabulary per tone), cross-model adversarial review, and SEO…