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/emaballarin/ccplugins/analyzenpx skills add emaballarin/ccplugins --skill analyzegit clone --depth 1 https://github.com/emaballarin/ccpluginsWhat 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 | $0.00176 | $0.01439 |
| Opus 5 | $0.00088 | $0.00720 |
| Sonnet 5 | $0.00035 | $0.00288 |
| Haiku 4.5 | $0.00018 | $0.00144 |
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 yesterday.
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 — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/tml:analyze — what the study actually showed
Run the checklist before answering the round's question, because several of its entries can invalidate the round, and nobody wants to hear that after being told the answer.
First action, always
ls -la ./.tml/rounds/ 2>/dev/null | tail -5
Then establish which mode you are in:
- Designed — a study spec exists in
./.tml/rounds/NNN/. Read it. The role assignment and the fixed-hyperparameter caveats are what make the fairness question answerable. - Standalone — results only, no spec. This is a normal mode, not a degraded
one (
references/regime.md§4). Ask which hyperparameters the question is about; treat the rest as unknown-role; and answer the fairness question "cannot be determined" rather than "yes".
Hard rules
- Checklist before conclusion. In order, and reported even when it passes.
- Never invent a role assignment you were not given. "Cannot be determined" is a real finding — it means the comparison's fairness is unverified.
- Name the disabled checks. Missing curves disable overfitting and late-variance detection; a missing infeasibility flag disables §4. Silence about a check that could not run reads as a check that passed.
- Read-first. Writes only under
./.tml/. Never edits project code.
Procedure
1. Ingest
The expected shape is templates/results-example.jsonl. Coerce a CSV or tracker
export into it. Required per trial: an identifier, the hyperparameters, and
the objective. Optional, and each one gates a check: the metric-vs-step
series, the best-step, the infeasibility flag and reason, the seed, wall-clock.
Say what was ingested and what was absent before analysing anything.
2. The checklist — references/diagnostics.md §1
- Search-space boundaries (§2) — plot the objective against each varied
hyperparameter. Best points hugging a bound means the space decided the
answer; expand and re-run. If everything above some learning rate is
infeasible and the best trials sit at that edge, stop and go to
references/instability.md— that is a stability defect wearing an optimum's clothes. - Sampling density (§3) — no general answer exists; say so, and show how many points landed in the good region.
- Infeasible fraction (§4) — a large fraction means a bad space or a bug. Report it as a number with reasons, never as missing rows.
- Optimisation failures →
references/instability.md. - Training curves (§5) — problematic overfitting, late step-to-step variance, still-improving, saturated-early, or training loss rising (a bug). Check the best trial of every scientific setting, not just the overall best, and look at the whole population: selecting the winner suppresses overfitting and quietly rewards configurations that were merely hobbled.
- Was the nuisance tuning good enough to make the comparison fair
(
references/study-design.md§4)?
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.
- yesterday First seen · 121 lines · 176 tokens per session scan A 61edd958e7b1
analyze is a skill published in the GitHub repository emaballarin/ccplugins (3 stars, last pushed 26d ago), licensed MIT. It adds 176 tokens to every session and 1,439 once invoked, about $0.0009 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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