io.github.dingdawg/dingdawg-planning-agent
NPM · DINGDAWG-PLANNING-AGENT · SCANNED AUG 3
Planning AI. Sprint estimation, task breakdown, risk analysis. Learns your velocity.
Available components
How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. How we score →
Supply Chain Security87
- No malware found by supply-chain analysis.Pass
- Only part of the dependency tree could be resolved (95 of 99), so this covers what we could see, not the whole tree.Partial
- No install/post-install scripts declared.Pass
- Only part of the dependency tree could be resolved (95 of 99), so this covers what we could see, not the whole tree. View diagnostics → Partial
Provenance & Transparency45
- Source repository is publicly reachable at the declared URL. View diagnostics → Pass
- Provenance check failed: no build-provenance attestation is published. See how to fix → View diagnostics → Fail
- Clear OSI-approved license (MIT).Pass
- Actively maintained (last published 27 days ago).Pass
- Disclosure check failed: no security disclosure policy was found in the source repository. See how to fix → Fail
Schema Quality & AI Usability66
- AI-judged instruction clarity (good).Pass
- Tool/resource definitions use about 425 tokens (~70/item across 6 items; 6 tools + 0 resources), lean.Pass
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management27
- Stability observed for 8 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage100
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 100% of tool parameters carry a description.Pass
Capabilities100
- Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.Pass
Add this component to your MCP client. Where a client-specific snippet is available, pick your client below and copy it straight into your config; otherwise use the connection detail shown.
npm · dingdawg-planning-agent
claude mcp add dingdawg-dingdawg-planning-agent -- npx -y dingdawg-planning-agent
codex mcp add dingdawg-dingdawg-planning-agent -- npx -y dingdawg-planning-agent
{
"$schema": "https://opencode.ai/config.json",
"mcp": {
"dingdawg-dingdawg-planning-agent": {
"type": "local",
"command": [
"npx",
"-y",
"dingdawg-planning-agent"
],
"enabled": true
}
}
} openclaw mcp add dingdawg-dingdawg-planning-agent --command npx --arg -y --arg dingdawg-planning-agent
mcp_servers:
dingdawg-dingdawg-planning-agent:
command: "npx"
args: ["-y", "dingdawg-planning-agent"] {
"mcpServers": {
"dingdawg-dingdawg-planning-agent": {
"command": "npx",
"args": [
"-y",
"dingdawg-planning-agent"
]
}
}
} Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.
- 3 Aug 26 +4
- Stability: unverified → 0.27 ▲ functional
- 2 Aug 26 +42
- Provenance: unverified → fail ▼ security
- Known CVEs: unverified → partial ▲ security
- Install scripts: unverified → pass ▲ security
- Malware scan: unverified → pass ▲ security
- Stability: Stability not yet verified: not enough scan history yet (needs a 30-day window). security
- License: unverified → pass ▲ functional
- Dependency health: unverified → partial ▲ functional
- Maintenance: unverified → pass ▲ functional
- MCP protocol: unverified → pass ▲ functional
- Schema quality: unverified → good ▲ functional
- Licence: MIT functional
- 31 Jul 26 +15
- We updated how we score, so this day's move reflects our rubric, not a change to the server See what changed → functional
- 30 Jul 26 −40
- Malware scan: pass → unverified ▼ security
- Tool coverage: 100 → unverified ▼ functional
- First check of Schema quality: unverified functional
- 27 Jul 26 46
First indexed and scored.
Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.
Captured 3 Aug 2026 · Analysed npm/[email protected]
Provenance none
Ecosystem: npm · Outcome: none
Dependencies 95 packages
95 packages in the resolved dependency tree · 95 deprecated · 29 stale.
The dependency tree was only partially resolved, so these counts may be incomplete.
The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability.
decision_matrix ~69
AI-powered structured decision making with weighted scoring, bias detection, and assumption tracking. Requires DINGDAWG_API_KEY.
| Name | Type | Req | Description |
|---|---|---|---|
| criteria | string | — | Evaluation criteria (comma-separated) |
| decision | string | yes | Decision to evaluate |
| options | string | yes | Options to compare (comma-separated or described) |
No output schema declared.
No examples provided.
estimate_effort ~73
Free AI effort estimation with complexity scoring and PERT ranges. Deep LLM-powered subtask breakdown and confidence analysis with API key.
| Name | Type | Req | Description |
|---|---|---|---|
| context | string | — | Project context, tech stack, constraints |
| task | string | yes | Task or feature to estimate |
| team_experience | string | — | Team experience level |
No output schema declared.
No examples provided.
plan_project ~82
Generate a structured project plan with phases, milestones, dependencies, and resource allocation. Requires DINGDAWG_API_KEY for LLM-powered planning.
| Name | Type | Req | Description |
|---|---|---|---|
| deadline | string | — | Target deadline (YYYY-MM-DD) |
| methodology | string | — | Project methodology |
| project_description | string | yes | Describe the project to plan |
| team_size | number | — | Team size |
No output schema declared.
No examples provided.
retrospective ~80
AI project retrospective — what went well, what didn't, action items, pattern identification. Requires DINGDAWG_API_KEY.
| Name | Type | Req | Description |
|---|---|---|---|
| project | string | yes | Project name or description |
| team_feedback | string | — | Team feedback or notes |
| went_poorly | string | — | What went poorly |
| went_well | string | — | What went well |
No output schema declared.
No examples provided.
risk_analyze ~61
Free AI project risk analysis with risk matrix scoring. Deep LLM-powered mitigation strategies and contingency plans with API key.
| Name | Type | Req | Description |
|---|---|---|---|
| known_risks | string | — | Known risks or concerns |
| project_description | string | yes | Project or initiative to analyze for risks |
No output schema declared.
No examples provided.
standup_summary ~60
AI standup summary from team updates — blocker detection, dependency alerts, velocity tracking. Requires DINGDAWG_API_KEY.
| Name | Type | Req | Description |
|---|---|---|---|
| sprint_goal | string | — | Current sprint goal |
| updates | string | yes | Team standup updates (paste all updates) |
No output schema declared.
No examples provided.