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/pigsly/axiomflow/render_pdr_statusnpx skills add pigsly/AxiomFlow --skill render_pdr_statusgit clone --depth 1 https://github.com/pigsly/AxiomFlowWrote 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/pigsly/axiomflow/render_pdr_status)<a href="https://agentmods.dev/skills/pigsly/axiomflow/render_pdr_status"><img src="https://agentmods.dev/badge/skills/pigsly/axiomflow/render_pdr_status.svg" alt="Measured on agentmods" 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 | $0.00022 | $0.00503 |
| Opus 5 | $0.00011 | $0.00251 |
| Sonnet 5 | $0.00004 | $0.00101 |
| Haiku 4.5 | $0.00002 | $0.00050 |
Grade A, and why
render_pdr_status 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 4d 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.
What it actually says
SKILL: render_pdr_status
Purpose
Render PDR result with a required Rich bordered panel output.
This skill is observation-only. No summarization. No interpretation.
Input
pdr_result(JSON)
{
"overall_signal": "Review Suggested",
"alignment_score": 86,
"breakdown": {
"req_spec_alignment": 45,
"adr_alignment": 27,
"execution_clarity": 14
},
"safety_gates": {
"required_docs_present": true,
"references_resolved": true,
"contract_safety": "Needs Clarification"
}
}
Execution (YAML)
executor: python_script
entry: runner.py
command:
- python3
- runner.py
input:
type: json
source: stdin
fallback: lock.json
modes:
- rich_panel
env:
RENDER_MODE: rich_panel
rich_panel is the required default mode and renders a bordered status box with the rich package.
Behavior
- Parse PDR result (stdin or lock.json)
- Normalize fields
- Render output
Layout (rich_panel)
Rich bordered status box:
- title:
AxiomFlow PDR Status - boxed key/value layout
- intended for non-interactive terminals and transcript-friendly output
- must be used as the standard output layout for this skill
Observations and Summary List are intentionally out of scope for this skill.
If a workflow requires them, they must be produced by the upstream PDR layer or another renderer.
Constraints
- No mutation to PDR source
- Pure rendering only
- Deterministic output for same input
- No summarization layer
- No interpretation
- No
Observationsrendering - No
Summary Listgeneration rich_panelis mandatory
Notes
This skill belongs to the Observation Layer:
PDR Engine → JSON → Render Skill → UI
It must NOT:
- infer meaning
- generate summary
- render
Observations - generate
Summary List - suggest actions
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.
- 4d ago First seen · 117 lines · 22 tokens per session scan A 3db1099c0571
render_pdr_status is a skill published in the GitHub repository pigsly/AxiomFlow (2 stars, last pushed 4mo ago), licensed MIT. It adds 22 tokens to every session and 503 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 2026-08-31.
Other skills, from other repositories
fork-tab
Open an exact fork of the active Codex CLI chat in a new terminal tab while leaving the source chat open. Use when the user asks to fork, branch, duplicate, or continue the current Codex conversation in another terminal, including phrases such as “fork this into a new tab” or “open a parallel copy of this chat.”.
git-workflow
This skill should be used when the user asks to "create git commit", "manage branches", "follow git workflow", "use Conventional Commits", "handle merge conflicts", or asks about git branching strategies, version control best practices, pull request workflows. Provides comprehensive Git workflow guidance for team…
daily-paper-generator
Use when the user asks to generate daily paper digests on a general topic. This skill supports both arXiv and bioRxiv (or either one), then produces structured Chinese/English summaries for selected papers.
workthreads
SpecStory Workthreads - a weekly work-thread rollup across a team's repos from SpecStory coding histories (any agent - Claude Code, Codex, Cursor, Gemini, and more). It groups the window's sessions into threads of work per project and labels each new / open / recently closed, so a lead sees what shipped, what is still…
codex-autoresearch
Run autonomous, measurable experiments in a Git repository: change one hypothesis, verify a numeric metric, keep improvements, and revert failures. Use when the user wants Codex to keep iterating toward a numeric target in the foreground or as a detached background run. Do not use for ordinary one-shot coding…
refine-plan
Iteratively refine development plans using TDD methodology. Ensures plans are clear, actionable, and include red-green-refactor cycles with proper test coverage.