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/clay-good/openlore/openlore-implement-storynpx skills add clay-good/OpenLore --skill openlore-implement-storygit clone --depth 1 https://github.com/clay-good/OpenLoreWrote 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/clay-good/openlore/openlore-implement-story)<a href="https://agentmods.dev/skills/clay-good/openlore/openlore-implement-story"><img src="https://agentmods.dev/badge/skills/clay-good/openlore/openlore-implement-story.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 5.1 | $0.00044 | $0.02231 |
| Opus 5 | $0.00022 | $0.01115 |
| Sonnet 5 | $0.00009 | $0.00446 |
| Haiku 4.5 | $0.00004 | $0.00223 |
Grade A, and why
openlore-implement-story 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 6d 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 — 259 lines — stays where its author put it; the contents beside it link to each section on GitHub.
openlore: Implement Story
When to use this skill
Trigger this skill when the user asks to implement a story or task on a codebase that has openlore analysis available, with phrasings like:
- "implement story X"
- "work on task Y"
- "start implementing this feature"
- explicit command
/openlore-implement-story
Prerequisite: openlore analysis must exist (openlore analyze has been run).
If orient returns "error": "no cache" → run analyze_codebase first, then retry.
Step 1 — Read the story and risk context
Read the story file. Extract:
$STORY_TITLE,$AC(acceptance criteria),$PROJECT_ROOT$RISK_CONTEXT— therisk_contextsection if present (pre-filled by Architect Agent)
| Situation | Approach |
|---|---|
risk_context present, risk 🟢 < 40 |
Skip to Step 3 — use insertion point from context |
risk_context present, risk 🟡 40–69 |
Run Step 2 impact check, then proceed |
risk_context present, risk 🔴 ≥ 70 |
Stop — a blocking refactor story must be resolved first |
risk_context absent |
Run the full Step 2 orientation |
Step 2 — Orient and assess risk
Call the openlore MCP tool orient with:
{
"directory": "$PROJECT_ROOT",
"task": "$STORY_TITLE",
"limit": 7
}
For the top 2 functions returned, get minimal context first (callers, callees, body, test coverage in one call):
// get_minimal_context
{
"directory": "$PROJECT_ROOT",
"functionName": "$FUNCTION_NAME"
}
What to read from the result before proceeding:
function.riskLevel—"high"means fanIn ≥ 30 or fanOut ≥ 15; the tool expanded caller/callee lists to 24. All shown entries are in scope.callers[*].callType— all"awaited"= async interface frozen; changing signature or return type breaks every caller. Mixed = looser coupling.callees[*].isExternal: true— function touches HTTP/DB boundary; new code paths may fail silently in tests (mocked) but loudly in production.testedBy[*].confidence—"called"= direct test (strong)."imported"= test imports module only;vi.mock()can nullify it. Only"imported"entries = treat as effectively untested.
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.
- 6d ago First seen · 259 lines · 44 tokens per session scan A 11e17de7c289
openlore-implement-story is a skill published in the GitHub repository clay-good/OpenLore (297 stars, last pushed today), licensed MIT. It adds 44 tokens to every session and 2,231 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 2026-08-30.
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