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.
git clone --depth 1 https://github.com/skyfox675/agents-skillsWrote 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/commands/skyfox675/agents-skills/jira-issue-recon)<a href="https://agentmods.dev/commands/skyfox675/agents-skills/jira-issue-recon"><img src="https://agentmods.dev/badge/commands/skyfox675/agents-skills/jira-issue-recon/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/commands/skyfox675/agents-skills/jira-issue-recon"><img src="https://agentmods.dev/badge/commands/skyfox675/agents-skills/jira-issue-recon.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 | $0.00128 | $0.01432 |
| Opus 5 | $0.00064 | $0.00716 |
| Sonnet 5 | $0.00026 | $0.00286 |
| Haiku 4.5 | $0.00013 | $0.00143 |
Grade A, and why
jira-issue-recon 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.
This is a copy
86% identical to gh-issue-recon — 28 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 37 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/jira-issue-recon — technically scope, size, and de-risk groomed issues
Operate in caveman mode (load the
cavemanskill) — recon fans out and tokens compound. Keep Atlassian-MCP calls, JQL, labels/fields,file:linerefs, and theTECH-RECONcontract line byte-exact; the findings comment is the exception (humanizer — see below).
Arguments: $ARGUMENTS
Parse them as:
- One or more issue keys (e.g.
PROJ-412). Each is a separately groomed, stakeholder-accepted issue that the dev team has not yet technically reviewed or sized. A lead may drop several at once. - Optional
model:<tier>andeffort:<level>tokens, anywhere — these pin the recon agent's tier (defaultsonnet; escalate toopusfor deep or cross-cutting asks). They are NOT the implementation tier — recon recommends that separately in its findings. The cheap tier is never used here (technical judgement on real code needs a capable model; see the technical-recon skill).
Example: /jira-issue-recon PROJ-412 PROJ-415 PROJ-418 model:opus
The technical-recon skill is the protocol — the read-only sandbox, the findings anatomy (implementation approach, verified file:line, LoE + confidence, risks/unknowns, dependencies, split recommendation, dispatch recommendation), the verdict states, and the output contract all come from it. This is the deeper dev-side pass, distinct from the cheap intake-triage recon in the jira-issue-filing skill. Jira reads/writes go through the Atlassian MCP (getJiraIssue, editJiraIssue, addCommentToJiraIssue, transitionJiraIssue, searchJiraIssuesUsingJql) — allowlist your server's tools in .claude/settings.json (the mcp__atlassian__ prefix above may differ from your configured server name); adopters without the MCP can substitute the Jira REST v3 API or a Jira CLI. Consult the skill before dispatching.
Steps
- Resolve the batch. Collect the issue keys from the args (strip the
model:/effort:tokens first). For each, confirm it exists and is open (getJiraIssue). Skip and report any that are Done/closed or carry an operator hold (do-not-dispatch, per jira-issue-fields).
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 · 37 lines · 128 tokens per session scan A 8c753be90709
jira-issue-recon is a command published in the GitHub repository skyfox675/agents-skills (11 stars, last pushed 11d ago), licensed MIT. It adds 128 tokens to every session and 1,432 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to gh-issue-recon, differing in 28 lines, and is treated as a copy.
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