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 flonat/flonat-research --skill code-archaeologygit clone --depth 1 https://github.com/flonat/flonat-researchWrote 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/flonat/flonat-research/code-archaeology)<a href="https://agentmods.dev/skills/flonat/flonat-research/code-archaeology"><img src="https://agentmods.dev/badge/skills/flonat/flonat-research/code-archaeology/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/flonat/flonat-research/code-archaeology"><img src="https://agentmods.dev/badge/skills/flonat/flonat-research/code-archaeology.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.01073 |
| Opus 5.5 | $0.00020 | $0.00429 |
| Sonnet 5.5 | $0.00010 | $0.00215 |
| Haiku 4.5 | $0.00005 | $0.00107 |
Grade A, and why
code-archaeology 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 — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Audit Skill
CRITICAL RULE: Never delete data or code files. Copy to legacy/, never move or delete originals.
Systematically review and understand old code, data, and analysis files.
Purpose
Based on Scott Cunningham's workflow of reviving old projects - understanding what exists, documenting it, and making it safe to work with.
For formal audits with cross-language replication and referee reports, use the Referee 2 agent (.claude/agents/referee2-reviewer.md). This skill is for understanding and documenting existing code, not formal verification.
When to Use
- Returning to an old project after months/years
- Taking over code from a coauthor
- Before extending existing analysis
- R&R requiring you to revisit old work
When NOT to Use
- Brand new projects — use project-safety skill instead to set up structure
- Formal code verification — use the Referee 2 agent for cross-language replication
- Quick code questions — just ask directly, no need for full audit
Workflow
-
Explore the directory:
- What files exist?
- What's the structure?
- When were things last modified?
-
Understand the pipeline:
- What are the main scripts?
- What order do they run in?
- What data do they use?
- What outputs do they produce?
-
Document findings:
- Create/update README.md
- Map data flows
- Note dependencies
-
Establish safety:
- Create legacy/ folder
- Copy (don't move) originals
- Set up version control if not present
-
Create audit report:
- What the code does
- Potential issues found
- Recommendations for cleanup
Safety Rules (from Scott Cunningham)
1. Never delete data. Under no circumstances.
2. Never delete programs. No do-files, no R scripts, nothing.
3. Stay in this folder. Can go down, not up.
4. Use a legacy folder. Move originals there for safekeeping.
5. Copy, don't move. When reorganising, always copy from legacy.
Prompt Template
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 · 142 lines · 50 tokens per session scan A c17a3eadad49
code-archaeology is a skill published in the GitHub repository flonat/flonat-research (147 stars, last pushed 28d ago), licensed MIT. It adds 50 tokens to every session and 1,073 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
check-pipeline
Check that a code and its Pipeline/ .md are still compatible — that every symbol, cell number, data file, and data-flow claim in the pipeline doc still matches the current code (drift detection). Use after editing a documented notebook/engine/script, before trusting a pipeline doc, or on request ("does the pipeline…
reality-check
Re-derive a contested or pushed-back claim in isolation from the definitions, to catch sycophantic capitulation. Use when a result is disputed or you suspect you changed an answer only because the user pushed back.
apply-pipeline
The write-side inverse of /check-pipeline and the pipeline-auditor — read a Pipeline/ doc (and/or an audit report) and EDIT the code accordingly: apply an optimization the pipeline describes, correct code the pipeline (backed by your authoritative notes) shows is wrong, or bring the code in line with the documented…
omh-live-incident-response
This is an OMH live-incident-response workflow skill, projected for Agent Skills hosts (Claude Code, Codex, Cursor, opencode, OpenClaw, pi).
debugging
Use this skill when something is broken and the cause is unknown — a failing test, a crash, an error message, wrong output, a regression, a hang, or behaviour that differs between environments — and it has to be found and fixed at the root. Also use it to build the failing proof for a suspected defect before fixing…
drill-root-causes
Move from symptoms to causal structure using complementary root-cause representations and causal-validity checks.