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/blisspixel/primr/qa-iterationnpx skills add blisspixel/primr --skill qa-iterationgit clone --depth 1 https://github.com/blisspixel/primrWrote 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/blisspixel/primr/qa-iteration)<a href="https://agentmods.dev/skills/blisspixel/primr/qa-iteration"><img src="https://agentmods.dev/badge/skills/blisspixel/primr/qa-iteration.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 | $0.00032 | $0.00287 |
| Opus 5 | $0.00016 | $0.00143 |
| Sonnet 5 | $0.00006 | $0.00057 |
| Haiku 4.5 | $0.00003 | $0.00029 |
Grade A, and why
qa-iteration 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 3d 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
QA Iteration
Purpose
Use this skill after research or strategy generation to evaluate output quality and identify the next corrective action.
Workflow
-
Read
primr://output/latestwhen the user does not specify a path. -
Run
run_qaon the concrete report path. -
Summarize the weakest sections first.
-
If the user wants a different deliverable rather than a rewrite, read
primr://strategies/available, callestimate_strategy, and get approval beforegenerate_strategy.
Operating Rules
-
Treat QA as a decision aid, not a final verdict.
-
Prioritize factual gaps, missing citations, and thin strategic interpretation.
-
Do not recommend regeneration until you can explain what is weak.
-
Keep the follow-up action specific: rerun research, generate a strategy, or revise the report.
Example
run_qa(report_path="output/exampleco/report.md")
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.
- 3d ago First seen · 77 lines · 32 tokens per session scan A 3193dd2d6eec
qa-iteration is a skill published in the GitHub repository blisspixel/primr (3 stars, last pushed 4d ago), licensed Apache-2.0. It adds 32 tokens to every session and 287 once invoked, about $0.0002 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
vaaya
Vaaya is the payment system for agents: one MCP server that lets your agent call paid APIs pay-per-call with no API keys. Web search, scraping, image and video generation, code sandboxes, browser automation, email, phone calls, lead enrichment, live data. Priced in cents per call, billed only on success, and every…
autosearch:channel-selection
Group-first channel selection algorithm for v2 tool-supplier architecture. Given a research query + clarify rubrics + channelpriority hints, picks 1-3 relevant groups from the router index, then 3-8 leaf channels from within those groups. Replaces flat-rank selection across 41 channels with a two-stage pick so runtime…
autosearch:experience-compact
Promote recurring patterns from experience/patterns.jsonl into the compact experience.md digest (≤120 lines, read by runtime AI before calling the skill). Triggers on N-events / file-size / user-feedback / session-end. Guards against single-success noise and pollution via promotion thresholds.
autosearch:citation-index
Deduplicate URLs across all sources, assign stable citation numbers, and merge citations from multiple subagents / sections into one consistent reference list. Prevents "same URL cited as [3] in one paragraph and [17] in another" and "different URLs merged under [5]" bugs that come from per-section synthesis.
autosearch:context-retention-policy
Session-level policy for keeping the runtime AI's context window healthy across long research — keep-last-k tool results, offload older evidence to disk, trigger compaction at thresholds. Borrows MiroThinker's keeptoolresult, deepagents' summarization middleware, and deer-flow's SummarizationEvent pattern. Orthogonal…
autosearch:delegate-subtask
Define the execution contract for isolating a research sub-task — input schema, budget, return summary, evidence list, failure status. Complements decompose-task (which only splits the problem) by giving each split a bounded, auditable execution unit the runtime AI can farm out to a sub-agent or parallel session.