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 hujizhou35-cmd/journal-cover-letter-tutorial --skill journal-cover-letter-skill-trainergit clone --depth 1 https://github.com/hujizhou35-cmd/journal-cover-letter-tutorialWrote 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/hujizhou35-cmd/journal-cover-letter-tutorial/journal-cover-letter-skill-trainer)<a href="https://agentmods.dev/skills/hujizhou35-cmd/journal-cover-letter-tutorial/journal-cover-letter-skill-trainer"><img src="https://agentmods.dev/badge/skills/hujizhou35-cmd/journal-cover-letter-tutorial/journal-cover-letter-skill-trainer/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/hujizhou35-cmd/journal-cover-letter-tutorial/journal-cover-letter-skill-trainer"><img src="https://agentmods.dev/badge/skills/hujizhou35-cmd/journal-cover-letter-tutorial/journal-cover-letter-skill-trainer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 39 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00110 | $0.03082 |
| Opus 5 | $0.00055 | $0.01541 |
| Sonnet 5 | $0.00022 | $0.00616 |
| Haiku 4.5 | $0.00011 | $0.00308 |
Grade A, and why
journal-cover-letter-skill-trainer 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 12d 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 — 263 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Journal Cover Letter Skill Trainer v0.2.0
Improve the journal-cover-letter-skill through controlled experiments. Learn transferable editorial reasoning from expert letters without copying their wording, treating them as factual authorities, or overfitting one manuscript.
Non-negotiable principles
- Treat the manuscript and author-confirmed case materials as the factual source of truth. An expert letter is a benchmark for editorial decisions, not a gold standard.
- Generate the baseline before exposing its generator to the expert letter.
- After the expert is revealed, never reuse that evaluator context for a claimed-blind candidate generation. Start a fresh generator context for every round.
- Change the target route for a stated reason. Do not patch only the current letter.
- A single case may create an experimental candidate, but cannot by itself prove a general improvement.
- Protect every route that was not targeted. A Research improvement must not silently weaken Review or Bibliometrics, and vice versa.
- Preserve the target Skill's name. Version the candidate and release; do not rename the Skill to encode a version.
- Keep private manuscripts and real letters outside public source trees and release packages.
- Never describe a comparison as objective proof. Report the rubric, judges, isolation level, evidence, uncertainty, and human decision.
1. Determine the run mode
Set:
training_intent: STRENGTHEN_EXISTING_ROUTE | PROPOSE_NEW_ROUTE
target_route: ORIGINAL_RESEARCH | REVIEW_SYNTHESIS | BIBLIOMETRICS | user-defined route
storage_mode: SESSION_ONLY | LOCAL_PRIVATE_CORPUS
isolation_level: STRICT_AGENT_ISOLATED | FRESH_CONTEXT_BLIND_GENERATION | HUMAN_GATED_REVEAL | REFERENCE_CONTAMINATED
promotion_policy: HUMAN_APPROVAL
Use STRICT_AGENT_ISOLATED when host-managed workers or API requests enforce separate contexts and controlled inputs. Use FRESH_CONTEXT_BLIND_GENERATION when a human starts a new temporary chat or conversation for every baseline and candidate generation, supplies only the candidate Skill plus manuscript-side materials, and verifies that memory, project files, custom instructions, and inherited conversation do not expose the expert. Use HUMAN_GATED_REVEAL when the expert is withheld until the first baseline is sealed but later candidate generation remains in the revealed conversation. If any claimed-blind generator has already seen or been quoted material from the expert letter, use REFERENCE_CONTAMINATED for that round.
What ships with it
13 files 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.
- agents/openai.yaml 304 B
- assets/context-ledger.example.json 882 B
- assets/training-manifest.example.json 2.1 KB
- references/blind-evaluation-rubric.md 3.1 KB
- references/input-and-case-schema.md 4.1 KB
- references/portability-and-isolation.md 4.4 KB
- references/promotion-and-versioning.md 3.2 KB
- references/worker-prompts.md 3.5 KB
- scripts/evaluate_promotion.py 4.7 KB runs code
- scripts/prepare_blind_packet.py 3.1 KB runs code
- scripts/prepare_generator_packet.py 4.1 KB runs code
- scripts/validate_context_ledger.py 4.4 KB runs code
- scripts/validate_training_manifest.py 6.4 KB runs code
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.
- 12d ago First seen · 263 lines · 110 tokens per session scan A e7dabf9e59bf
journal-cover-letter-skill-trainer is a skill published in the GitHub repository hujizhou35-cmd/journal-cover-letter-tutorial (31 stars, last pushed 28d ago), licensed MIT. It adds 110 tokens to every session and 3,082 once invoked, about $0.0006 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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