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/markmhendrickson/ateles/analyzenpx skills add markmhendrickson/ateles --skill analyzegit clone --depth 1 https://github.com/markmhendrickson/atelesWhat 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 | $0.00091 | $0.02974 |
| Opus 5 | $0.00046 | $0.01487 |
| Sonnet 5 | $0.00018 | $0.00595 |
| Haiku 4.5 | $0.00009 | $0.00297 |
Grade A, and why
analyze 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.
How it starts
The opening of the file, as written. The whole thing — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Notes
- This command is generic and works for any repo using foundation as submodule.
- Comparative analysis: All analysis is relative to all repos. Load repos from Neotoma (
repositoryentities). If empty, runexecution/scripts/sync_repos_to_neotoma.py. - Output documents are confidential and stored in the private docs submodule. Persistence of findings to Neotoma (Step P below) is always performed; opening public issues (Step I) is opt-in.
- Resource Type Detection: Command automatically detects if resource is a product/project (competitive/partnership analysis) or content/thought leadership (relevance analysis).
- Templates ensure consistent, thorough analysis across all assessments.
- For content/thought leadership, analysis focuses on extracting insights applicable to current repo rather than competitive positioning.
- Web Scraper Integration: ChatGPT and Twitter/X URLs are automatically handled via web scraper MCP server when configured (see MCP configuration).
- Browser Tools Fallback: Non-scraper URLs and search terms use browser tools for research.
Step P: Persist findings to Neotoma (required, same turn)
After producing the markdown analysis document (the existing foundation-template output), persist a structured record to Neotoma in the same turn per the Neotoma MCP turn lifecycle and [PROVENANCE] rules. This applies regardless of resource type (competitive, partnership, relevance) and regardless of whether public issues are also opened.
Entity-type reuse check (before first store in a fresh session)
Call list_entity_types with keywords analysis, task, issue, finding, repository to check for existing types. Reuse exact strings when present. Default types:
analysis— the overall analysis (one per invocation). Subtype the analysis kind via akindfield rather than minting separate top-level types.task— Mark-internal follow-up actions extracted from the analysis.proposed_github_issue— drafted public issues for repo-touching tasks (same type as used by/analyze-meeting).analysis_finding— discrete findings inside the analysis (one entity per finding so they can be cross-referenced and aggregated independently). Reusefinding,insight, or similar if already present in the type registry.repository— never created here; only retrieved viaretrieve_entitiesfor the comparative scope.
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 · 157 lines · 91 tokens per session scan A c69d0aa8fc7f
analyze is a skill published in the GitHub repository markmhendrickson/ateles (5 stars, last pushed 5d ago), licensed MIT. It adds 91 tokens to every session and 2,974 once invoked, about $0.0005 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.
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