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 terrylica/cc-skills --skill b-investigation-methodologygit clone --depth 1 https://github.com/terrylica/cc-skillsWrote 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/terrylica/cc-skills/b-investigation-methodology)<a href="https://agentmods.dev/skills/terrylica/cc-skills/b-investigation-methodology"><img src="https://agentmods.dev/badge/skills/terrylica/cc-skills/b-investigation-methodology/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/terrylica/cc-skills/b-investigation-methodology"><img src="https://agentmods.dev/badge/skills/terrylica/cc-skills/b-investigation-methodology.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 | $0.00029 | $0.02018 |
| Opus 5 | $0.00015 | $0.01009 |
| Sonnet 5 | $0.00006 | $0.00404 |
| Haiku 4.5 | $0.00003 | $0.00202 |
Grade A, and why
crucible-investigation-methodology 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 — 190 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Investigation Methodology — 6 execution patterns
Self-Evolving Skill: If any pattern here fails in practice (wrong results, wasted compute), update the section AND append to
references/evolution-log.md. Don't defer.
These 6 patterns executed in service of a-research-foundations. They are the "how" to the foundations' "why". Apply in roughly this order for a new hypothesis.
1. LLM-native data representation — quintile tokens
Before asking an agent to "look at" numerical market data, encode as per-bar token sequences using rolling quintile ranks within a causal window.
Canonical schema:
idx dir body_q range_q dur_q uwick_q lwick_q loc sess fwd+H...
Each quintile is 1..5 in a causal 200-bar rolling window (see Skill A §1). Agents can spot motifs like +1:5:1|+1:5:1|+1:5:1 (three consecutive fast big-up bars) that are invisible in float-space.
Context-budget rule: 60 KB tokenized stats-table fits in agent context; 67 MB raw bars don't.
Full reference: findings/methodology/01-llm-native-data-representation.md.
2. Serial adversarial gates (A/B/C/D/E protocol)
Before trusting any in-sample positive, survive 4-5 independent gates in series.
| Gate | Question | Catches |
|---|---|---|
| A — Directional breakdown | Is edge from long/short/both? | Diffusive-looking edges that are actually directional |
| B — Mirror symmetry | Does the inverse trigger show mirror edge? | Sample-window drift inflating one side |
| C — OOS time-split (80/20 chronological) | Does finding survive on held-out later data? | In-sample overfit |
| D — Cross-asset replay | Does it replicate on other symbols? | Asset-specific overfit |
| E — Full-history per-year | Is it positive in ≥60% of years? | Single-year-luck |
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 Changed 6334de8a9239
- 11d ago First seen · 190 lines · 29 tokens per session scan A c8190bc05a00
crucible-investigation-methodology is a skill published in the GitHub repository terrylica/cc-skills (72 stars, last pushed yesterday), licensed MIT. It adds 29 tokens to every session and 2,018 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 2026-08-30.
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