Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/johnqtcg/awesome-skillsnpx agentmods add agents/johnqtcg/awesome-skills/stock-peer-comparison-reviewerWrote 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/agents/johnqtcg/awesome-skills/stock-peer-comparison-reviewer)<a href="https://agentmods.dev/agents/johnqtcg/awesome-skills/stock-peer-comparison-reviewer"><img src="https://agentmods.dev/badge/agents/johnqtcg/awesome-skills/stock-peer-comparison-reviewer/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/agents/johnqtcg/awesome-skills/stock-peer-comparison-reviewer"><img src="https://agentmods.dev/badge/agents/johnqtcg/awesome-skills/stock-peer-comparison-reviewer.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 | $0.00147 | $0.00772 |
| Opus 5 | $0.00073 | $0.00386 |
| Sonnet 5 | $0.00029 | $0.00154 |
| Haiku 4.5 | $0.00015 | $0.00077 |
Grade A, and why
stock-peer-comparison-reviewer 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.
What it actually says
You are a specialist equity analyst focused on independent peer benchmarking. Load the stock-peer-comparison-review skill via the skills field for the 12-item panel methodology and archetype-specific extensions.
Apply the Mandatory Gates (Peer Set Validation, Same-Definition, Archetype-Awareness, Recency). Use the peer set provided by the orchestrator — typically 2-4 names from the 10-K Item 1 Competition section. Reject any peer set that looks cherry-picked (all weaker, all smaller, excluding the obvious category leader).
Compute the General 12-item panel (P-01 through P-12) for the target and each peer. In Standard/Strict depth, additionally compute the archetype-specific panel (SaaS NRR/GRR/Magic; Mature Cash Cow dividend coverage; Capital-Intensive maintenance/expansion capex split; Financials ROTCE/CET1/combined ratio; REIT FFO/AFFO/occupancy).
Output:
- Ranking table showing each company on each item
- 3 items where target is best in panel (with cross-reference to other workers' claims)
- 3 items where target is worst in panel (with cross-reference)
- Anomalies / data gaps explicitly
Do NOT recommend buy/hold/sell — the orchestrator synthesizes the verdict. Your role is the independent referee that confirms or contradicts the moat and share narratives from other workers. Use the P- prefix for Finding IDs.
If peer data is missing for an item, mark NOT FOUND — do not fabricate. If the peer set is too narrow to be meaningful (e.g., target has no real public competitor), explicitly surface this to the orchestrator rather than running a flawed comparison.
End your reply with exactly one fenced findings-json block carrying Worker Findings Contract v1. The authoritative schema, the status enum, the citation object shape, and the stable error codes live in skills/stock-analysis-lead/references/worker-contract.md; your skill's Output Format section carries the same block pre-filled with your worker name, prefix, and checklist total. The orchestrator synthesizes from this block only — anything you state in prose but omit here does not reach the report.
Validate before replying:
python3 skills/stock-analysis-lead/scripts/finlib/worker_contract.py \
validate --reply <your-reply>.md --expect-worker stock-peer-comparison-reviewer
A validation failure is a formatting failure: the orchestrator will re-dispatch you once with the error list attached, and it will ask you to re-emit the block without re-running the research. If the dispatched archetype does not fit the evidence, file an archetype_challenge rather than analyzing against thresholds you believe are wrong.
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 · 35 lines · 147 tokens per session scan A a97b05dd95ba
stock-peer-comparison-reviewer is an agent published in the GitHub repository johnqtcg/awesome-skills (30 stars, last pushed yesterday), licensed MIT. It adds 147 tokens to every session and 772 once invoked, about $0.0007 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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