Borrowing it
Nothing to install: this file belongs to opendatahub-io/strat-creator. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/opendatahub-io/strat-creator/main/.claude/skills/assess-strat/SKILL.mdgit clone --depth 1 https://github.com/opendatahub-io/strat-creatorWrote 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/opendatahub-io/strat-creator/assess-strat)<a href="https://agentmods.dev/skills/opendatahub-io/strat-creator/assess-strat"><img src="https://agentmods.dev/badge/skills/opendatahub-io/strat-creator/assess-strat/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/opendatahub-io/strat-creator/assess-strat"><img src="https://agentmods.dev/badge/skills/opendatahub-io/strat-creator/assess-strat.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00026 | $0.02161 |
| Opus 5.5 | $0.00010 | $0.00864 |
| Sonnet 5.5 | $0.00005 | $0.00432 |
| Haiku 4.5 | $0.00003 | $0.00216 |
Grade A, and why
assess-strat 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 2mo 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 — 171 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Usage
/assess-strat artifacts/strat-tasks/RHAISTRAT-1469.md
/assess-strat artifacts/strat-tasks/
/assess-strat RHAISTRAT-1469
Instructions
Script Location
The assess-strat scripts are maintained in this repository under
scripts/assess-strat/. Use that path for all script and rubric references.
Rules
- No shell pipes or compound commands. Run all scripts as simple commands (e.g.,
python3 scripts/assess-strat/setup_run.py /path/to/strategies). Do not use|,&&,;,2>/dev/null, or redirects. The Bash tool returns command output as a string -- parse it programmatically in your logic, not withsed/awk/wc/greppipelines.
Architecture
Single-input mode scores one strategy from a file path. Batch mode scores all strategies in a directory. Results are saved as individual files in a timestamped run directory. Strategies are local markdown files -- no Jira fetch needed.
Directory Structure
assessments/ # in the project directory (persistent)
20260415-143000/ # timestamped run
RHAISTRAT-1469.result.md
queue.txt # pending keys (managed by next_batch.py)
strat_dir.txt # path to source strategy directory
scores.csv # generated by parse_results.py when complete
current -> 20260415-143000 # symlink to active/latest run
/tmp/strat-assess/single/ # single-mode temp files
RHAISTRAT-1469.result.md
Single input (file path or strategy key)
Detect the input type:
- File path (starts with
/or./or~, or contains.md): Use the file directly. - Strategy key (matches
[A-Z]+-\d+orSTRAT-\d+): Search for the corresponding.mdfile in common locations:artifacts/strat-tasks/{KEY}.md.context/strat-tasks/{KEY}.md- Search with Glob:
**/{KEY}.md
Then assess:
- Run
python3 scripts/assess-strat/prep_single.py {KEY}to clean up stale result files and ensure the output directory exists. - Spawn one background agent (model: opus, run_in_background: true, subagent_type: strat-scorer) with the launch prompt below, setting
{DATA_FILE}to the resolved strategy file path and{RUN_DIR}to/tmp/strat-assess/single. - Read the result from
/tmp/strat-assess/single/{KEY}.result.md, wrap it with a header, and present it to the user.
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.
- 2mo ago First seen · 171 lines · 26 tokens per session scan A f6dcab446045
assess-strat is a skill published in the GitHub repository opendatahub-io/strat-creator (3 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 26 tokens to every session and 2,161 once invoked, about $0.0001 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 2026-09-04.
assess-strat compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| assess-stratopendatahub-io/strat-creator | Skill | 3 | 1mo ago | Apache-2.0 | A | 26 | Claude Code |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |